smolcic

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The Faint Radio Population in the
VLA-COSMOS Survey:
Star Forming Galaxies and
Active Galctic Nuclei
Vernesa Smolčić
Max–Planck–Institut für Astronomie
Heidelberg 2007
Dissertation in Astronomy
submitted to the
Combined Faculties for the Natural Sciences and for Mathematics
of the Ruperto–Carola University of Heidelberg, Germany
for the degree of
Doctor of Natural Sciences
presented by
Dipl. Ing. Phys. Vernesa Smolčić
born in Zagreb, Croatia
Oral examination: 19.12.2007, 2:00 pm
The Faint Radio Population in the
VLA-COSMOS Survey:
Star Forming Galaxies and
Active Galctic Nuclei
Referees:
Prof. Dr. Hans–Walter Rix
Prof. Dr. Stefan Wagner
Zusammenfassung (German)
Die Zusammensetzung der lichtschwachen – submillijansky – Radiopopulation, die in der
Vergangenheit ein Gegenstand heißer Debatten war und immer noch ist, wird erforscht:
durch Beobachtungen des 1.4 GHz (20 cm) Radiokontinuums des 2◦ COSMOS-Feldes,
die eine grosse statistisch signifikante Stichprobe ergeben, und der Entwicklung einer Methode, die eine minimale Parameteranzahl zur effizienten Unterscheidung zwischen den beiden Hauptpopulationen in extragalaktischen Radio-Durchmusterungen – aktive galaktische
Kerne (AGN) und sternbildende Galaxien – nutzt. Diese Methode hat das Potential auch
erfolgreich auf Stichproben, die bei anderen Wellenlängen selektiert wurden, angewendt zu
werden. Eines der Hauptergebnisse dieser Arbeit ist, dass sternbildende Galaxien nicht
die submillijansky Radiopopulation dominieren, wie oft angenommen wurde, sondern nur
ungefähr 30 − 40% beitragen, während der Rest aus AGN und Quasaren besteht. Diese
gut definierte Stichprobe von sternbildenden Galaxien im Radiobereich bei 1.4 GHz wurde
benutzt, um die kosmische Sternentstehungsgeschichte aus Radiodaten zu bestimmen. Insbesondere wurde zum ersten Mal die staub-unabhängige kosmische Entwicklung der Sternentstehungsrate der intensivsten sternbildenen Galaxien (& 100 M⊙ yr−1 ) seit ∼ 5 Gyr
nach dem ’Big Bang’ mit hoher Präzision bestimmt. Zusätzlich bestägtigt die, aus den
Radiodaten abgeleitete, kosmische Sternentstehungsgeschichte die Gültigkeit der großen
Korrekturen für Staub, die bei anderen Wellenlängen angebracht werden.
Abstract (English)
The composition of the faint – submillijansky – radio population, that has been a matter
of strong debate in the past, is explored by performing observations at 1.4 GHz (20 cm) radio continuum of the 2◦ COSMOS field providing a large statistically significant sample,
and by developing a method that uses a minimal number of parameters to efficiently discriminate between the two main populations in extragalactic radio surveys: active galactic
nuclei (AGN) and star forming galaxies. This method bears the potential to be successfully applied to similar samples selected at other wavelengths. One of the main findings is
that star forming galaxies do not dominate the submillijansky radio population, as often
assumed, but form only about 30−40% of it, while the remainder is composed of AGN and
quasars. Using this well defined sample of radio-selected star forming galaxies at 1.4 GHz,
the cosmic star formation history is derived using radio data, for the first time constraining the dust-unbiased cosmic evolution of star formation rate in the most intensively star
forming galaxies (& 100 M⊙ yr−1 ) since ∼ 5 Gyr after the Big Bang with high precision.
In addition, the radio derived cosmic star formation history confirms the validity of the
large dust corrections applied at other wavelengths.
Contents
1 Introduction
1.1 Evolution of galaxies and their panchromatic properties . . . . .
1.1.1 Galaxy properties in the NUV to NIR range . . . . . . .
1.1.2 About the evolution of galaxies . . . . . . . . . . . . . .
1.1.3 Major mergers as the main drivers for galaxy evolution .
1.1.4 The advantage of using panchromatic properties . . . . .
1.2 The radio sky . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.1 A brief history of radio astronomy . . . . . . . . . . . . .
1.2.2 The origin of radio emission in extragalactic sources . .
1.2.3 Properties of radio sources in the local universe . . . . .
1.2.4 Challenges in studying evolution of the radio population
1.3 Studying galaxy evolution via (panchromatic) look-back surveys
1.3.1 Selection of sky area and wavelength range surveyed . . .
1.3.2 The COSMOS and VLA-COSMOS surveys . . . . . . . .
2 VLA-COSMOS Large Project
2.1 Introduction . . . . . . . . . . . . . . . .
2.2 Survey objective . . . . . . . . . . . . . .
2.2.1 Survey area . . . . . . . . . . . .
2.2.2 Star forming galaxies . . . . . . .
2.2.3 Active galactic nuclei . . . . . . .
2.3 Observations . . . . . . . . . . . . . . . .
2.3.1 Lay-out of the pointing centers .
2.3.2 Correlator set-up and calibrators
2.3.3 Observing strategy . . . . . . . .
2.4 Data reduction and imaging . . . . . . .
2.4.1 Data reduction . . . . . . . . . .
2.4.2 Imaging . . . . . . . . . . . . . .
2.5 Tests . . . . . . . . . . . . . . . . . . . .
2.5.1 Flux calibration . . . . . . . . . .
2.5.2 Absolute and relative astrometry
2.6 The VLA-COSMOS catalog . . . . . . .
2.6.1 Source extraction . . . . . . . . .
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CONTENTS
2.7
2.6.2 Description of the catalog . . . . . . . . . . . . . . . . . . . . . . .
2.6.3 Comparison to other surveys . . . . . . . . . . . . . . . . . . . . . .
The VLA-COSMOS survey in the COSMOS context . . . . . . . . . . . .
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3 VLA-COSMOS faint radio population
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 The multi-wavelength data set . . . . . . . . . . . . . . . . . . . . . .
3.2.1 Radio data . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.2 Near-ultraviolet, optical and infrared imaging data . . . . . .
3.2.3 X-ray data . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.4 Photometric redshifts . . . . . . . . . . . . . . . . . . . . . .
3.2.5 Optical spectroscopic data . . . . . . . . . . . . . . . . . . .
3.3 VLA-COSMOS 1.4 GHz radio sources at other wavelengths . . . . .
3.3.1 Matching VLA-COSMOS and NUV/optical/NIR . . . . . . .
3.3.2 Radio – optical sources with IRAC and MIPS detections . . .
3.3.3 Radio – optical sources with point-like X-ray emission . . . .
3.3.4 Remaining radio sources . . . . . . . . . . . . . . . . . . . . .
3.4 Classification Methodology . . . . . . . . . . . . . . . . . . . . . . .
3.4.1 Calibration in the local universe . . . . . . . . . . . . . . . . .
3.4.2 Application to VLA-COSMOS . . . . . . . . . . . . . . . . . .
3.4.3 Classification outline and nomenclature . . . . . . . . . . . . .
3.5 Classification of VLA-COSMOS sources in the matched radio sample
3.5.1 Star candidates . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.2 Quasi stellar objects . . . . . . . . . . . . . . . . . . . . . . .
3.5.3 Star forming and AGN galaxies . . . . . . . . . . . . . . . . .
3.6 Comparison with other selection methods . . . . . . . . . . . . . . .
3.6.1 3.6-8 µm color – color diagnostics . . . . . . . . . . . . . . . .
3.6.2 The 24 µm – radio correlation . . . . . . . . . . . . . . . . . .
3.6.3 Selection based on spectroscopic diagnostics . . . . . . . . . .
3.7 Discussion: The composition of the faint radio population . . . . . .
3.7.1 Redshifts and luminosity distribution . . . . . . . . . . . . . .
3.7.2 The ’population mix’ in the VLA-COSMOS survey . . . . . .
3.7.3 Concluding remarks on the ’population mix’ . . . . . . . . . .
3.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4 The dust un-biased cosmic star formation history (CSFH)
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 The 1.4 GHz luminosity function for star forming galaxies . .
4.2.1 Star forming galaxy sample . . . . . . . . . . . . . . .
4.2.2 Derivation of the luminosity function . . . . . . . . . .
4.2.3 The luminosity function . . . . . . . . . . . . . . . . .
4.2.4 The evolution of star forming galaxies . . . . . . . . .
4.3 The cosmic star formation history . . . . . . . . . . . . . . . .
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CONTENTS
4.4
xi
4.3.1 The total cosmic star formation history . . . . . . . . . . . . . . . . 118
4.3.2 The CSFH of massively star forming galaxies . . . . . . . . . . . . 119
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5 Wide angle tail galaxy in the COSMOS field
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Observations and data reduction . . . . . . . . . . . . . .
5.2.1 Radio data . . . . . . . . . . . . . . . . . . . . . .
5.2.2 X-ray data . . . . . . . . . . . . . . . . . . . . . .
5.2.3 Optical data . . . . . . . . . . . . . . . . . . . . .
5.2.4 Cluster redshift . . . . . . . . . . . . . . . . . . . .
5.3 The wide angle tail galaxy: CWAT-01 . . . . . . . . . . . .
5.3.1 Radio properties of CWAT-01 . . . . . . . . . . . .
5.3.2 The host galaxy . . . . . . . . . . . . . . . . . . .
5.4 The clusters . . . . . . . . . . . . . . . . . . . . . . . . .
5.4.1 X-ray properties . . . . . . . . . . . . . . . . . . .
5.4.2 Optical properties . . . . . . . . . . . . . . . . . .
5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.1 Pressure balance . . . . . . . . . . . . . . . . . . .
5.5.2 Constraints on the CWAT-01’s galaxy velocity . . .
5.5.3 Subcluster merging in the CWAT-01 parent cluster?
5.5.4 Galaxy cluster assembly . . . . . . . . . . . . . . .
5.6 Summary and conclusions . . . . . . . . . . . . . . . . . .
6 Summary and outlook
6.1 The faint radio population and its cosmic evolution
6.1.1 The submillijansky radio population . . . .
6.1.2 The evolution of radio sources . . . . . . . .
6.2 Radio galaxies in galaxy clusters . . . . . . . . . . .
6.2.1 CWAT-01 galaxy cluster assembly . . . . . .
6.2.2 Future prospects . . . . . . . . . . . . . . .
6.3 Further radio observations of the COSMOS field . .
A All-sky surveys
A.1 SDSS . . . .
A.2 NVSS . . .
A.3 FIRST . . .
A.4 IRAS . . . .
A.5 2MASS . . .
A.6 2dF . . . . .
A.7 GALEX . .
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xii
Table of Contents
B Relevant equations
169
B.1 Radio luminosity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
B.2 Conversions of 1.4 GHz radio luminosity to star formation rate . . . . . . . 169
Acknowledgments
171
Zahvale (Croatian)
172
Author information
173
Curriculum Vitae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
List of publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
Bibliography
179
List of Figures
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
Color bimodality . . . . . . . . . . . . . . . . . . . . . . . .
Stellar mass density evolution for blue and red galaxies . . .
Blue-to-red galaxy evolutionary scenario (Faber et al. 2007)
AGN in ’green valley’ . . . . . . . . . . . . . . . . . . . . .
Radio synchrotron spectrum . . . . . . . . . . . . . . . . .
Radio to FIR SED of M82 . . . . . . . . . . . . . . . . . . .
Differential 1.4 GHz radio source counts . . . . . . . . . . .
Comparison of COSMOS, GEMS, GOODS, HUDF . . . . .
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3
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2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
2.11
2.12
2.13
2.14
2.15
2.16
2.17
2.18
VLA-COSMOS sensitivity as function of redshift . . . . . . . . . . .
Pointing pattern of VLA-COSMOS Large Project . . . . . . . . . . .
Synthesized (i.e. DIRTY) beam for different visibility weighting . . .
Representative synthesized beam . . . . . . . . . . . . . . . . . . . .
VLA-COSMOS 2◦ mosaic . . . . . . . . . . . . . . . . . . . . . . .
Distribution of noise in VLA-COSMOS mosaic . . . . . . . . . . . . .
Calibrator flux as function of observing date . . . . . . . . . . . . . .
Calibrator peak flux variations per day . . . . . . . . . . . . . . . . .
VLA-COSMOS astrometric accuracy in full mosaic . . . . . . . . . .
VLA-COSMOS astrometric accuracy for different partsof the mosaic .
Comparison of noise measurements . . . . . . . . . . . . . . . . . . .
Sensitivity map of VLA-COSMOS mosaic . . . . . . . . . . . . . . .
VLA-COSMOS visibility (areal coverage vs. noise) . . . . . . . . . . .
S/N map of VLA-COSMOS mosaic . . . . . . . . . . . . . . . . . . .
Total to peak flux ratio a function of S/N for VLA-COSMOS sources
Multi-component radio sources . . . . . . . . . . . . . . . . . . . . . .
Flux distribution of the VLA-COSMOS sources . . . . . . . . . . . .
Comparison of VLA-COSMOS and FIRST/NVSS total flux . . . . .
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57
3.1
3.2
3.3
3.4
3.5
Photometric redshift accuracy . . . . . . . . . . . . . . . . . . . . . . . . .
Radio – optical positional matching (distance vs. i band magnitude) . . . .
Distribution of total 1.4 GHz flux density for VLA-COSMOS sources . . .
Rest-frame color & BPT diagram for local SDSS/NVSS galaxies . . . . . .
Completeness/contamination of rest-frame (RF) color method (SDSS/NVSS)
66
67
69
75
77
xiv
LIST OF FIGURES
3.6
3.7
3.8
3.9
3.10
3.11
3.12
3.13
3.14
3.15
3.16
3.17
3.18
3.19
3.20
3.21
3.22
3.23
3.24
3.25
3.26
3.27
Rest-frame color & BPT diagram for local SDSS/NVSS/IRAS galaxies . . 78
Completeness/contamination of RF color method (SDSS/NVSS/IRAS) . . 80
Rest-frame color photometric accuracy (VLA-COSMOS) . . . . . . . . . . 82
Rest-frame color accuracy (SDSS/NVSS) . . . . . . . . . . . . . . . . . . . 83
Optical color-color diagrams (VLA-COSMOS stars/QSOs) . . . . . . . . . 86
i band FWHM vs. i band magnitude . . . . . . . . . . . . . . . . . . . . . 87
HST/ACS QSO stamps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Distribution of rest-frame color P 1 for VLA-COSMOS galaxies . . . . . . . 90
Mid-infrared color-color diagrams for classified VLA-COSMOS galaxies . . 92
24 µm – radio correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Distribution of 24 µm to radio flux ratio . . . . . . . . . . . . . . . . . . . 94
24 µm to radio flux ratio as a function of redshift . . . . . . . . . . . . . . 94
Best et al. diagnostic diagram for classified VLA-COSMOS galaxies . . . . 96
Redshift distribution of VLA-COSMOS star forming (SF) and AGN galaxies 98
1.4 GHz luminosity vs. redshift (VLA-COSMOS SF and AGN galaxies) . . 99
Distribution of 1.4 GHz luminosity for VLA-COSMOS SF/AGN galaxies . 99
Contribution of SF/AGN/QSO/high-z galaxies to sub-mJy population . . 102
MIR color-color digram for full radio – optical sample . . . . . . . . . . . . 103
BzK diagram for full radio – optical sample . . . . . . . . . . . . . . . . . 104
Distribution of the i band magnitude for SF/AGN/QSO/high-z galaxies . . 105
Contribution of remaining radio sample to sub-mJy population . . . . . . . 106
MIR color-color diagram for remaining radio sample . . . . . . . . . . . . . 107
4.1
4.2
4.3
4.4
1.4 GHz luminosity functions (LFs) for VLA-COSMOS SF galaxies
Luminosity density for VLA-COSMOS SF galaxies . . . . . . . . .
Cosmic star formation history . . . . . . . . . . . . . . . . . . . . .
Cosmic star formation history for VLA-COSMOS ULIRGs . . . . .
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5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
5.12
5.13
1.4 GHz radio map of the wide angle tail galaxy CWAT-01 . . . . . . . .
4.8 GHz radio map of CWAT-01 . . . . . . . . . . . . . . . . . . . . . . .
Spectral index map of CWAT-01 . . . . . . . . . . . . . . . . . . . . . . .
Optical host galaxy of CWAT-01 . . . . . . . . . . . . . . . . . . . . . .
Surface brightness profile of CWAT-01’s host galaxy . . . . . . . . . . . .
X-ray properties/analysis of the cluster assembly . . . . . . . . . . . . . .
Pressure/entropy in the cluster assembly . . . . . . . . . . . . . . . . . .
Color-magnitude diagram of cluster assembly galaxies . . . . . . . . . . .
Voronoi tessellation analysis of cluster assembly . . . . . . . . . . . . . .
Optical/X-ray/radio representation of cluster assembly . . . . . . . . . .
Optical/X-ray/radio representation of CWAT-01 parent cluster . . . . . .
Models of the mean velocity of CWAT-01 . . . . . . . . . . . . . . . . .
Color composite images of cluster assembly and CWAT-01 parent cluster
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6.1
LFs for VLA-COSMOS star forming and AGN galaxies . . . . . . . . . . . 160
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List of Figures
6.2
xv
Spectroscopic verification of CWAT-01 galaxy cluster assembly . . . . . . . 161
B.1 Comparison of 1.4 GHz luminosity to star formation rate calibrations . . . 170
xvi
List of Figures
List of Tables
2.1
2.2
2.3
2.4
Radio Surveys at 1.4 GHz . . . . . . . . . . . . . . . . . . . . . . . . . .
VLA-COSMOS Large Project Pointing Centers . . . . . . . . . . . . . .
VLA-COSMOS Large Project catalog layout . . . . . . . . . . . . . . . .
VLA-COSMOS Large Project catalog layout for multi-component sources
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3.1
VLA-COSMOS sources at other wavelengths . . . . . . . . . . . . . . . . .
71
5.1
5.2
5.3
5.4
5.5
IMACS spectra in cluster assembly . . . . . . . . . . .
SDSS spectra in cluster assembly . . . . . . . . . . . .
X-ray cluster properties from Finoguenov et al. (2007)
X-ray spectral analysis in cluster assembly . . . . . . .
X-ray properties of the cluster assembly . . . . . . . .
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138
139
139
xviii
List of Tables
Chapter 1
Introduction
One of the fundamental aims of astrophysics is understanding the formation and evolution
of galaxies, trying to reveal when the first galaxies formed and how they changed through
cosmic times. Answering these questions can, and needs to, be approached in numerous
different ways, each providing a little piece to the overall big puzzle. This thesis, in
particular, focuses on a radio-based view of the sky, and aims to shed light on the above
challenges using panchromatic (X-ray to radio) data for a large number of galaxies. This
chapter provides an introduction to the broad scientific context and topics relevant for this
thesis.
1.1
The evolution of galaxies and their panchromatic
properties
The most successful paradigm for understanding the distribution of matter in the universe is ’hierarchical galaxy formation’ (White & Rees 1978) within a Λ cold dark matter
(ΛCDM) cosmology. The success of the ΛCDM model in describing the universe, when
compared to observations, has been tremendous, ranging from the anisotropies of the cosmic microwave background at high redshifts (e.g. Spergel et al. 2007) to the clustering
properties of galaxies in the local universe (e.g. Peacock et al. 2001; Weinberg et al. 2004;
Springel et al. 2005). In this model, structure in the universe is formed via gravitational
instabilities arising from small perturbations seeded during the inflationary epoch in the
early universe (e.g. Pen 1999; Springel et al. 2005). The dominant mass component in the
universe is collisionless cold dark matter, which is the main driver of the dynamics and
structure of baryons on large scales. Both the visible and invisible matter on these large
scales, once they have formed, continue to grow ’violently’ in a hierarchical fashion: large
features grow through mergers of smaller precursors. In such a universe strong evolution
of galaxies through cosmic times is expected (and observed). In the following, average
properties of galaxies in a ΛCDM universe, and their evolution, relevant to the content of
this thesis, are discussed.
2
1.1.1
1. Introduction
Global properties of galaxies in the near-ultraviolet to nearinfrared range
For many years observed galaxy properties, and correlations between them, have been
known to trace the underlying physics of these galaxies. This implies that observed properties of galaxies are valuable tools for studying internal physical mechanisms. For example,
one of the well known astronomical relations for elliptical (pressure supported) galaxies is
the ’Faber – Jackson’ relation (Faber & Jackson 1976), which represents a correlation between the galaxy’s central velocity dispersion and optical luminosity. This relation is a
consequence of the virial theorem and correlates two, out of the three, fundamental parameters that appear to represent the entire family of elliptical galaxies (the so called
’fundamental plane’). This ’fundamental plane’ combines the contributions of a galaxy’s
gravitational potential with its radius and luminosity (hence its surface brightness). On
the other hand, for spiral galaxies, which are rotationally supported systems, a tight correlation between the galaxy’s optical luminosity and its maximum rotational velocity exists.
This relation again reflects the underlying galaxy’s gravitational well and it is known as the
’Tully – Fisher’ relation (Tully & Fisher 1977). It is noteworthy that the ’Tully – Fisher’
relation was originally obtained via radio observations of the 21 cm neutral hydrogen (electron spin-flip) spectral line for a sample of spiral galaxies. One other, observationally
extremely useful, relation is the correlation between the galaxy’s morphology and its color
indices.
All of the above mentioned relations have significantly contributed to the advance of
our understanding of galaxies. However, as astronomy advanced (e.g. in instrumentation, observing strategies) our knowledge of galaxy properties and their interdependence
has been put on stronger footing thanks to the recent advent of multi-wavelength allsky surveys (e.g. Strateva et al. 2001; Ivezić et al. 2002; Blanton et al. 2001, 2003, 2005;
Hogg et al. 2003; Kauffmann et al. 2003a,b,c; Brinchmann et al. 2004; Baldry et al. 2004;
Balogh et al. 2004; Yip et al. 2004; Smolčić et al. 2004, 2006, 2007b; Obrić et al. 2006).
This is in particular true for the interplay between galaxy properties in the near ultraviolet
(NUV) to near infrared (NIR) wavelength range, extensively used in latter chapters, that
is discussed below.
The NUV – NIR properties of galaxies in the local (z < 0.3) universe have been extensively studied using multi-wavelength data obtained by various all-sky surveys (SDSS,
NVSS, FIRST, IRAS, ROSAT; see Appendix A for details on these surveys; Strateva et al.
2001; Kauffmann et al. 2003a; Brinchmann et al. 2004; Yip et al. 2004; Obrić et al. 2006;
Smolčić et al. 2006). The results have strongly converged towards a simple parameterization of the NUV – NIR spectral energy distribution (SED), as well as its correlation to spectroscopic features, of the overall population of galaxies. In general, colors of galaxies reflect
their dominant stellar populations and thus correlate with morphology (Humason 1936;
Hubble 1936). A strong bimodality in the distribution of local galaxies has been shown
to exist in NUV – NIR color space (Strateva et al. 2001; Baldry et al. 2004; Balogh et al.
2004), with the two peaks of the bimodal distribution corresponding roughly to early(E, S0 and Sa), and late- (Sb, Sc and Irr) type galaxies (see Fig. 1.1; Strateva et al.
1.1 Evolution of galaxies and their panchromatic properties
3
2001). A similar distribution has been found extending to at least z ∼ 1 (Lin et al. 1999;
Im et al. 2002; Bell et al. 2004; Weiner et al. 2005; Willmer et al. 2006) and possibly beyond (Giallongo et al. 2005).
This ’simplicity’ in the NUV – NIR properties of the overall galaxy population has been
confirmed in numerous other studies, using different approaches. For example, analyzing
170,000 SDSS local galaxy spectra using the principal component analysis (Karhunen-Loéve
transform) Yip et al. (2004) have found that more than 99% of the galaxies can be fully
described using a two-dimensional locus in the space spanned by the ratios of only the first
three eigencoefficients. Further, using UV (GALEX), optical (SDSS) and IR (2MASS) data
to construct the NUV – NIR broad-band SEDs for various types of galaxies (selected based
on their spectroscopic properties; Baldwin, Phillips, & Terlevich 1981) Obrić et al. (2006)
have demonstrated that the overall NUV – NIR SED is a nearly one-parameter family. In
addition, Smolčić et al. (2006) have shown that rest-frame optical colors, constructed using
the 3, 500 − 5, 800 Å wavelength range, strongly correlate with spectroscopic properties,
such as emission-line flux ratios commonly used to trace the hardness of the ionization
potential in a galaxy, which discriminates between emission from starbursts and accretion
onto supermassive black holes (Baldwin, Phillips, & Terlevich 1981).
80
60
60
40
40
20
20
0
0
1
2
3
4
1
2
3
4
4
1
2
3
4
80
60
60
40
40
20
20
Figure 1.1
u − r color distributions for the spectroscopically (left panels) and morphologically (right panels)
classified SDSS galaxies, adopted from
Strateva et al. (2001). The bimodal
distribution of the overall population of
galaxies is obvious. The division between red and blue types of galaxies
proposed by Strateva et al. (2001) is indicated by the vertical dashed line in
each panel (u − r = 2.22). There is also
a clear sub-division in color for different types of galaxies (indicated in each
panel).
0
0
1.1.2
1
2
3
About the evolution of galaxies
The idea that the two dominant types of galaxies in the universe, the ’blue’ (star forming)
and ’red’ (non-star forming) galaxies, do not represent two completely independent channels of galaxy formation, but rather different stages of the galaxy formation process itself,
4
1. Introduction
Figure 1.2
Evolution of the stellar
mass density of red and blue sequence
galaxies since z ∼ 1.
The diagram
was obtained using the COMBO-17 data
from Borch et al. (2006, courtesy of Frank
C. van den Bosch). Note that the stellar
mass density of blue galaxies is roughly
constant with redshift, while it substantially increases for red galaxies from high
to low redshift. Given that relatively few
stars form in red sequence galaxies this implies that suppression of star formation in
a fraction of previously blue, star forming,
galaxies is responsible for the stellar mass
growth of galaxies in the red sequence (see
text for details).
has already been proposed by Hubble (1936). Recently it has been observationally demonstrated that the stellar mass on the red sequence has increased by a substantial amount
(∼ 50%) since z ∼ 1, (Bell et al. 2004; Borch et al. 2006; Faber et al. 2007; Brown et al.
2007), while it stays roughly constant for blue cloud galaxies (see Fig. 1.2). Given that relatively few stars form in red sequence galaxies (Bell et al. 2004) this buildup has been argued
to be driven by quenching of star formation in a fraction of previously blue, star forming,
galaxies. A schematic outline (Faber et al. 2007) of the buildup of the red sequence is
shown in Fig. 1.3. In this scenario, proposed by Faber et al. (2007), the quenching of the
star formation in a blue cloud galaxy moves the galaxy to the red sequence by the ageing of
its stellar population which turns redder with time. Although the physical mechanism of
the star formation quenching process is not understood, it is assumed that star formation
is suppressed by a major merger event between two gas-rich blue galaxies (this is referred
to as ’wet’ merger) which doubles the mass of the initial merger constituents. However,
processes that do not include major mergers, such as ram pressure stripping and galaxy
harassment, may also cause the fuel for star formation to be removed from the galaxy and
thereby quench star formation. Once on the red sequence, the mass of the object may
grow further (although more slowly) through a sequence of dissipationless mergers with
gas-poor red galaxies (hence such mergers are called ’dry’ mergers).
1.1.3
Major mergers as the main drivers for galaxy evolution
Galaxy evolution through the process of wet (gas rich) mergers has been initially proposed
by Sanders & Mirabel (1996) in the context of a special class of local galaxies usually
referred to as ’infrared galaxies’. One of the most striking results of the Infrared Astron-
1.1 Evolution of galaxies and their panchromatic properties
5
Figure 1.3
Illustration of the
evolutionary scenario proposed for
the build-up of the red sequence
(adopted from Faber et al. 2007).
In this picture, major mergers between gas-rich star forming galaxies located in the blue cloud lead
towards the suppression of star
formation and double the mass
of the initial merging constituent.
After star formation is quenched
the stellar population ages, and
therefore reddens and migrates to
the red sequence. Once on the
red sequence, the object’s mass
can further grow through gas-poor
mergers with other ’red and dead’
galaxies.
omy Satellite (IRAS; Neugebauer et al. 1984) All-sky Survey has been the existence of
a substantial population of, previously unknown, ’infrared galaxies’ that may emit more
than 99% of their bolometric luminosity at IR wavelengths (see Sanders & Mirabel 1996
for a review). [The most extreme example of such galaxies is Arp 220 (Soifer et al. 1984).]
Sanders & Mirabel (1996) have postulated that these ultra-luminous IR galaxies (ULIRGs,
LIR > 1012 L⊙ ) represent an important intermediate stage in the formation of massively accreting black holes in the form of quasi stellar objects (QSO) and powerful radio galaxies,
suggesting further that wet mergers may be the major process driving their evolution. In
the context of the above described ’blue’ to ’red’ galaxy evolution scenario (Faber et al.
2007), (U)LIRGs would be placed roughly in the evolutionary transitional region between
the initially blue - pre-merger - star forming galaxies and the finally red, dead and gas-poor
galaxies.
Studies of ULIRGs based on the IRAS Bright Galaxy Sample (Sanders et al. 1988a,b;
Kim et al. 2002; Veilleux et al. 2002) have indicated that the ULIRG phase occurs near the
end of the merger between two gas-rich disk galaxies, when the disks overlap. The upcoming merger of the nuclei of the two galaxies is followed by a transition to an optically active,
quasi stellar object, phase. In particular, a five stage sequence was proposed for the evolution of luminous IR galaxies (LIRGs; LIR = 1011−12 L⊙ ) to QSOs (e.g. Sanders & Mirabel
1996; Sanders 2003). First, LIRGs, which are dominated by merging pairs of gas rich spirals, evolve to ”cool ULIRGs” (Tdust ∼ 30 − 60 K). In the latter systems the bulk of their
molecular gas, which fuels both star formation and black hole accretion, that was originally distributed throughout the disk, is funneled into the inner few kpc of the merging
system. These ”cool ULIRGs” tend to show HII-like emission in their optical spectra, and
may be considered to be powerful starbursts. As the merger advances, the ”cool ULIRGs”
transform into ”warm ULIRGs” (Tdust ∼ 150 − 250 K), which are post-starburst systems
and have Seyfert like optical spectra, hence they are dominated by the emission of the
accretion onto the central supermassive black hole. These then fade into IR-excess QSOs
which eventually turn into ”IR normal QSOs” (see Sanders 2003 for details and references
6
1. Introduction
therein).
The above proposed picture has grown stronger with the advent of new studies using
statistically significant samples of galaxies in, and beyond, the local universe. For example,
there is observational evidence in the local universe that the active galaxy phase, dominated
by the emission from the central supermassive black hole accretion, may be an intermediate
evolutionary phase: AGN (active galactic nuclei) largely tend to occupy the ’green valley’,
i.e. the sparsely populated region in NUV – NIR space between the blue cloud and red
sequence (see Fig. 1.4; Smolčić et al. 2006). The lower galaxy density in the ’green valley’
implies a much shorter life-time for these (compared to red/blue) galaxies yielding a fairly
rapid transition from the blue cloud into the red sequence (assuming the validity of the
blue-to-red galaxy evolutionary scenario). Further, using a compilation of observationally
Figure 1.4
The distribution of spectroscopically selected
(Baldwin, Phillips, & Terlevich 1981) AGN (dots) compared
to the overall galaxy distribution (contours) drawn from the
SDSS (DR1) ’main’ spectroscopic sample (see Smolčić et al.
2006 for details) in the space spanned by the Hδ absorption
line and the 4000 Å break. The strength of Hδ and Dn (4000)
are tracers of a galaxy’s young stellar population, and the
galaxy’s age, respectively (see Kauffmann et al. 2003a for a
comprehensive discussion of this point). This plane clearly
shows the observed galaxy bimodality; ’blue’ (star forming)
galaxies are located in the top-left peak, and ’red’ (non-star
forming) comprise the bottom-right peak of the bimodal distribution (shown in contours). Note that the active galaxies
(dots) preferentially occupy the interspace between the ’blue’
and ’red’ maxima, consistent with the so called ’green valley’, implying that AGN may be an intermediate evolutionary
phase in the build-up of the red sequence (see text for details).
determined red galaxy mass functions to study the rate of the buildup of the red sequence
as a function of mass and redshift Hopkins et al. (2007) have found that dissipationless
(gas-poor, dry) mergers account for the buildup of the red sequence at only the largest
masses (& 1011 M⊙ ) and low redshifts (z . 0.3). However, at higher redshifts (z & 0.5) the
buildup is dominated at all masses by gas-rich (wet) mergers. Overall, their observational
data support the hypothesis that mergers drive the transition from blue and star forming,
to red and ’dead’ galaxies, through massive starburst and active, AGN, phases, and that
gas-rich, wet, mergers are the dominating evolutionary process.
1.1.4
The advantage of using panchromatic properties
An important ingredient of the structure of galaxies is their interstellar medium comprised
of gas and dust. The effective absorption of photons by dust is inversely proportional to
wavelength. For example, Charlot & Fall 2000 find that the optical depth, τλ , may roughly
be approximated as a function of wavelength by τλ ∝ λ−0.7 (note that the attenuation due
to dust is then ∝ e−τλ ; see also Mathis et al. 1977). However, explaining the exact behavior
of dust is non-trivial and subject of many studies (e.g. Mathis et al. 1977; Charlot & Fall
2000; Calzetti 2001; Fischera et al. 2003). Due to this inverse wavelength dependence dustobscuration in galaxies substantially complicates the interpretation of short-wavelength,
1.1 Evolution of galaxies and their panchromatic properties
7
e.g. UV/optical, observations, while long-wavelength radiation, such as FIR to radio, stays
essentially unaffected by dust. This is of particular importance for both AGN (un-obscured
Type-I and obscured Type-II AGN in the context of the AGN unification theory; see e.g.
Padovani & Urry 1992) and intensely star forming galaxies that have been demonstrated to
contain larger dust reservoirs than modestly star forming galaxies (e.g. Bell 2003; Hopkins
2004). Further, the IR emission of a galaxy, thermal in origin, arises from the galaxy’s
interstellar dust heated by shorter wavelength radiation, such as e.g. by young hot stars
being formed in dust enshrouded regions or by the radiation from the accretion disk of the
AGN. For these reasons it is essential to observe the entire SED of galaxies in order to
fully understand their properties.
Since observations started being performed in different wavelength windows (tracing
different physical types of radiation), numerous ’unexpected’ correlations have been found.
To date, many of these relations are not fully understood giving rise to a new challenging
field of astrophysics: the panchromatic view on galaxy properties and evolution. In particular, tight correlations between IR and radio emission (e.g. Helou et al. 1985; Condon
1992; Obrić et al. 2006), as well as X-ray and radio emission (e.g. Brinkmann et al. 2000;
Ranalli et al. 2003), have been found separately for AGN and star forming galaxies. For
illustration, the IR – radio correlation is outlined below.
The IR – radio correlation
In extragalactic radio surveys two main galaxy populations are found: AGN and star
forming galaxies. The radio emission observed at 20 cm predominantly traces synchrotron
emission from electrons, accelerated to relativistic speeds, spiraling through magnetic fields
(see Chap. 1.2.2 for details). On the other hand, observed IR emission traces the thermal
radiation of the galaxy’s dust component. Since the early days of observations at IR
wavelengths, it has been realized that, for an IR selected sample of local galaxies, the far and
total IR emission tightly correlate with radio emission at 20 cm (Helou et al. 1985; Condon
1992). This implies that for the characteristic population of IR selected galaxies, knowing
the far or total IR luminosities, the radio luminosities can be predicted with a scatter of only
a factor of ∼ 2 (Condon 1992; Bell 2003; Obrić et al. 2006). Impressively, this correlation,
basically unchanged, has been shown to hold out to high redshifts (z ∼ 2, Garrett 2002;
Appleton et al. 2004; see also Chap. 3). Given that the two observational windows, IR and
radio, trace completely independent and different intrinsic physical mechanisms – thermal
vs. non-thermal radiation – the existence of such a tight correspondence is remarkable, and
has been argued to arise from the same radiation sources within the galaxies, implying that
both IR and radio emission trace the same physical state in galaxies, such as the emission
from star forming regions or the central supermassive black hole accretion. It is noteworthy
that historically it has been suggested that the IR – radio correlation only holds for star
forming galaxies, while ’monsters’, such as AGN or QSOs, significantly deviate from it (e.g.
Condon 1992). However, recently it has been demonstrated that a very similar correlation
(different by only ∼ 20%) also holds for ’modest’ monsters, i.e. low luminosity AGN such
as Seyfert and LINER galaxies (Obrić et al. 2006, see also Ch. 3). While there has been
8
1. Introduction
attempts to explain the origin of the IR – radio correlation for star forming galaxies (see
also Sec. 1.2.2), the origin of the correlation for AGN galaxies is still not understood.
1.2
The radio sky
There are many different (mainly wavelength dependent) approaches towards our full understanding of galaxy evolution, each one being equally important for building up the full
picture of the history of the universe. This thesis is focused on a radio view of the universe,
and in this section the history of radio astronomy, the origin of radio emission, the major
results of radio surveys and the remaining challenges are discussed.
1.2.1
A brief history of radio astronomy
The field of radio astronomy came to existence with Karl Jansky’s serendipitous discovery
(1933) of strong radio emission originating from the center of our galaxy. At that time
optical astronomy has already been well established. For example, Edwin Hubble first
proposed the now called ’Hubble law’, based on radial velocity and distance measurements
of 22 galaxies, four years earlier (Hubble 1929). Nonetheless, by the late 1950’s radio
observations have taken a key role in astronomy. Namely, they have led to the discovery
of a mysterious new class of objects: the quasi-stellar radio sources or shortened ’quasars’
(e.g. 3C273 from the Third Cambridge catalog; see below).
The most important early radio surveys were led by Cambridge and were performed at
158 MHz and 178 MHz with interferometers located at the outskirts of Cambridge, and
built by the Cambridge Radio Astronomy Group led by Martin Ryle and Antony Hewish
(Nobel prize winners for Physics in 1974). It is worth noting that the third Cambridge
catalog (3C; Edge et al. 1959; Bennett & Smith 1961) reached down to a limiting flux of
9 Jy beam−1 , while the 4C catalog (Pilkington & Scott 1965; Gower et al. 1967) shifted
this limit to 2 Jy beam−1 . Given the sensitivities of these surveys, their mainly observed
radio populations were quasars and radio galaxies.
Striking results have arisen from the first radio surveys. In particular, radio observations by Ryle & Clarke (1961) have provided evidence against the, at that time adopted,
Steady State model of the universe (opposing the ’Big Bang’ theory). They have found a
substantial discrepancy between the Steady State model prediction and the observed radio
source counts of extragalactic sources. The development of new powerful radio interferometers, the best example of which is the Very Large Array (VLA) located in New Mexico
(USA), allowed to study the radio sky in its full extent and to, at that time, unprecedented depth. The FIRST (Becker et al. 1995) and NVSS (Condon et al. 1998) all-sky
surveys (see Appendix A) at 1.4 GHz were performed with the VLA to limiting fluxes of
0.15 and 0.35 mJy beam−1 , respectively, reaching about four orders of magnitude deeper
than e.g. the 4C catalog. More recently deep pencil-beam (∼ 100 ′ ) surveys have also
been performed in the radio regime, reaching now as deep as ∼ 5 µJy beam−1 (SSA 13,
Fomalont et al. 2006; see also Tab. 2.1).
1.2 The radio sky
9
One relevant finding of past deep radio surveys (Condon 1984a,b; Windhorst et al.
1985a) is that extragalactic radio sources consist of two main populations: AGN and star
forming galaxies. The origin of the observed radio emission is described below.
1.2.2
The origin of radio emission in extragalactic sources
Radio emission observed from the two main extragalactic populations found in radio surveys, AGN and star forming galaxies, arises predominantly from synchrotron radiation of
relativistic electrons in magnetic fields (free-free emission starts dominating below ∼ 1 cm
in star forming galaxies; see below). In this section the mechanism of synchrotron radiation
is briefly outlined, and the origin of radio emission from AGN and star forming galaxies is
discussed separately.
A brief outline of synchrotron radiation
Nearly all of the radio emission from extragalactic radio sources at 1.4 GHz is synchrotron
radiation arising from relativistic electrons gyrating in a magnetic field. The observed
radio (1 cm – 100 m) spectrum of extragalactic sources is well described with a power
law: Fν ∝ ν −α , where Fν is the observed monochromatic energy flux at the specific frequency ν, and α is the spectral index. A pure power-law spectrum is a clear signature of
synchrotron radiation. As illustrated in Fig. 1.5 the synchrotron spectrum is produced by
the combined radiation emitted by individual electrons as they helically orbit around the
magnetic field lines. For a homogeneous radiation source with a constant magnetic field B,
a power law continuum spectrum can be generated by an initial power law distribution of
electron energies E: N(E)dE = N0 E −s dE, where α = 0.5 · (s − 1). Typically, the median
observed spectral index is 0.7 to 0.8 implying s ≈ 2.4. This however applies to frequencies
higher than the ’turn-over’ frequency, where synchrotron self-absorption is not important.
At frequencies lower than the ’turn-over’ frequency, the emitting gas becomes optically
thick, i.e. opaque to its own synchrotron radiation (this is known as the synchrotron selfabsorption) and the spectrum turns over and starts decreasing with decreasing frequency
following Fν ∝ ν 5/2 . At the highest frequencies on the other hand, observed radio spectra
sometimes curve downwards (i.e. α increases with ν). This is explained by the synchrotron
energy loss rate, which is proportional to E 2 , and implies that the highest-energy electrons
radiate away their energy most rapidly, thus depleting the high-energy end of the emitted
spectrum. For a comprehensive description of synchrotron radiation see e.g. Shu (1991,
Chapters 18 & 19).
Origin of radio emission in AGN galaxies
The radio morphology of radio luminous (not necessarily radio-loud) AGN can broadly be
described in terms of ’extended’ (i.e. spatially resolved) and/or ’compact’ (unresolved at
∼ 1′′ angular resolution) structures, both caused by synchrotron radiation. The extended
component generally appears as a double jet, originating from the optical counterpart, that
10
1. Introduction
Figure 1.5
The radio (synchrotron)
spectrum in terms of flux density vs. frequency Fν ∝ ν −α , as observed in radio
surveys, is shown in the large panel. Synchrotron radiation arises from charged
relativistic particles gyrating in a magnetic field, and the observed synchrotron
spectrum is a superposition of singleelectron synchrotron spectra (shown separately in the inset on the top-right).
Note that the slope of the spectrum
may change at low/high frequencies: at
low frequencies, where synchrotron selfabsorption becomes important the spectrum flattens, and turns over, and at the
largest frequencies it curves downward
(this is indicated by the dots in the spectrum, but not explicitely shown). See
text for more details.
ends in lobe-like features. The linear extent of the jets may be as large as megaparsecs.
The position of the optical counterpart is usually coincident with the compact radio source;
however, compact radio sources are often observed without large scale radio jets (e.g.
radio-quiet quasars; e.g. Kukula et al. 1998). The major difference between compact and
extended radio structures is that the extended component is optically thin to its own
synchrotron emission, whereas this is not true for the compact structures. The radio
synchrotron spectra of the compact sources are generally flat (α ∼ 0.2), while the extended
components have steep spectra (α ∼ 0.7). The accepted explanation for the flatness of
the synchrotron spectrum in compact radio sources is that the observed radio emission
arises from a superposition of many synchrotron spectra caused by different unresolved
synchrotron spectrum radiators, each of which having a different ’turn-over’ frequency (see
Sec. 1.2.2).
The radio synchrotron spectrum in AGN is believed to arise from processes related to
the accretion of material onto the galaxy’s central supermassive black hole. The accretion
process may be quantified by the accretion rate, Ṁ , relative to the Eddington accretion
rate, ṀE . The latter is defined as the maximum possible accretion rate for a spherically symmetric accreting black hole of mass M. Although the processes leading to radio
synchrotron emission in AGN are not fully understood, theoretically the most promising
generators are black holes that accrete at very low rates (Ṁ /ṀE . 0.1) and consequently
form the, so called, ’ion’ accretion torus. Such mechanisms may cause a strong magnetic
field anchored in the vicinity of the black hole (see Peterson 1997 for a detailed description).
Within this scenario, at such low accretion rates the accretion disk becomes optically thin,
and it is possible for a stable two-temperature structure, the ’ion torus’, to develop as the
inner regions of the disk cannot efficiently cool if the electrons and ions are thermally decoupled. The magnetic field of the central source will then be frozen into the ionized torus,
creating a rapidly rotating field with an axis parallel to the angular momentum vector
of the accretion disk. Ion tori are suspected of playing a major role in producing jets as
this strong field could collimate the outflow of charged particles. It is believed that radio
1.2 The radio sky
11
galaxies are low accretion mode AGN. It is worth noting though that it is believed that for
example radio-quiet quasars (that usually have compact radio morphologies; Kukula et al.
1998) are high accretion rate systems (see e.g. Jarvis & Rawlings 2004). However, it has
also been observationally shown that both accretion rate classes can produce similar radio
luminosities and structures (e.g. Blundell & Rawlings 2001).
Radio galaxies, i.e. extended radio sources that consist of a compact core and double jets
extending from the core out to Mpc scales, can be divided into two separate morphology
and luminosity categories, FR I & II Classes (Fanaroff & Riley 1974). FR I sources are
weaker radio galaxies, and are the brightest in the center with decreasing surface brightness
towards the edges of the radio structure. FR II galaxies are more luminous, limb-brightened
radio sources, often showing enhanced emission in so called ’hot spots’ at the edge of the
radio structure or embedded within the radio jets. The transition luminosity between the
two radio galaxy classes at 1.4 GHz may be considered to be 1025 W Hz−1 (Bridle & Perley
1984).
Origin of radio emission in star forming galaxies
Radio emission from star forming galaxies arises from a combination of non-thermal synchrotron radiation from relativistic electrons and free-free emission from HII regions (see
Condon 1992 for a review). Thermal reradiation of starlight by dust overtakes at frequencies above ∼ 200 GHz (∼ 1.5 mm), defining a practical upper bound for the ’radio’ regime.
The relative intensities of radio and dust radiation for the typical starburst galaxy M82
are shown in Fig. 1.6. The radio continuum accounts for less than 10−4 of the bolometric
luminosity. Note also that at 1.4 GHz observing frequency, the radio emission from star
forming galaxies is clearly dominated by synchrotron radiation, even out to the highest
redshifts.
The radio emission originating from star forming galaxies is thought to be caused by
Type II and Types Ib supernovae1 whose remnants are believed to accelerate most of the
relativistic electrons in these galaxies.2 The same supernovae ionize the HII regions as
well. Only stars more massive than ∼ 8 M⊙ produce Type II and Ib supernovae, and
these have lifetimes of . 3 × 107 yr, while the relativistic electrons probably live ∼ 108 yr.
Radio observations therefore probe very recent star formation in galaxies, and have several
1
Supernovae are classified into two main classes based on the presence of hydrogen spectral lines: SN I,
with no hydrogen lines, and SN II with hydrogen lines in their early spectra. Subclasses determined by
spectral (He, Si) evidence are denoted by lower-case letter suffixes. In particular Supernovae type II and
Ib are believed to be a stage of massive (> 8 M⊙ ) star evolution, while e.g. type Ia are thought to be
binary systems containing a white dwarf that accretes matter from the companion star. Radio emission
has not been observed from type Ia (Weiler et al. 1986).
2
A supernova explosion may be described as a sudden release of very hot gas into the inter stellar medium
(ISM) acting as a massive piston which moves supersonically through the ISM, and therefore causes a shock
front to develop ahead of the ejected material (e.g. Gull 1973). Supernovae become radio sources about
50 yr after the explosion as Rayleigh-Taylor instabilities develop at the boundary between the shock and
ambient ISM, and thereby accelerate the charged particles (most probably via Fermi acceleration; e.g.
Blandford & Cowie 1982).
12
1. Introduction
Figure 1.6
The observed radio to FIR SED of the typical starburst galaxy M 82 (symbols) modeled as the sum (black
solid line) of synchrotron (blue
dot-dashed line), free-free (red
dashed line), and dust (green
dotted line) components. Note
that at an observed frequency of
1.4 GHz the observed emission is
dominated by synchrotron radiation even at the highest redshifts
(z = 9 for illustration).
advantages. First, the contribution to radio emission of stellar populations older than
∼ 108 yr is insignificant. Second, the high (subarcsecond) positional accuracy of radio
observations allows unambiguous panchromatic cross-identifications, and third, only radio
and IR wavelengths directly trace the most intense starbursts, in such a way that the
observed flux densities are accurately proportional to intrinsic luminosities (as they are
not attenuated by dust).
A first sight drawback of radio emission as a star formation tracer is that radio data
alone would provide a poor quantitative constraint of star formation models. The free-free
radio emission emerges directly from HII regions containing the ionizing stars, and therefore the intensity of this emission is proportional to the production of Lyman continuum
(UV regime) photons which directly trace the young stars. However, isolating the freefree from synchrotron emission in radio observations is technically difficult given that the
flat spectrum free-free emission is usually weaker than the steeper spectrum synchrotron
emission (see Fig. 1.6). On the other hand, most of the observed synchrotron radiation in
star forming galaxies arises from & 107 yr old relativistic electrons that have propagated
significant distances from their parent supernovae remnants, in addition erasing any spatial
information of the origin of the star formation progenitor, and therefore not providing a
direct tracer of the galaxy’s star formation rate. However, the tight (F)IR – radio correlation, already described in Sec. 1.1.4, provides a strong constraint of models relating
radio emission to star formation. As a significant fraction of the bolometric luminosity
of a galaxy is absorbed by interstellar dust and re-emitted in the thermal IR regime, the
FIR luminosity provides a solid measure of the bolometric luminosity produced by young
stars (see e.g. Kennicutt 1998; Bell 2003). The (F)IR – radio correlation implies that a
one-parameter model specifying the (F)IR and radio luminosities in terms of recent star
1.2 The radio sky
13
formation rate can describe star forming galaxies.
Two calibrations of 1.4 GHz radio luminosity to star formation rate, developed by
Condon (1992) and Bell (2003), are commonly adopted (see also Sec. 1.2.3 for the latter;
see Appendix B.2 for details about the calibrations). The model developed by Condon
(1992) has originally been tuned to trace the high-mass (5 − 100 M⊙ ) star formation.
However, it has been generalized by Haarsma et al. (2000) to a broader stellar mass range
(0.1 − 100 M⊙ ) and to the commonly used Salpeter initial mass function (IMF; ψ(M) ∝
M −2.35 ). While Condon (1992) has based his model on the Milky Way supernovae frequency
in such a way that the reproduction of the FIR – radio correlation is the model’s output (not
input), Bell (2003) has based his radio SFR calibration on the total IR – radio correlation
(also using the Salpeter IMF and the 0.1 − 100 M⊙ mass range). The difference between
the two calibrations implies a factor of two uncertainty in the overall star formation rate
scale based on radio.
For comparison, the UV (1250 - 2500 Å) based star formation proxy directly traces the
integrated spectrum of the young stellar population (that is not dust attenuated) longward
of the Lyα limit, but short enough to minimize the spectral contribution of the old stellar
populations. Given different calibrations, the overall UV derived star formation rates are
uncertain to a factor of 2 (see Kennicutt 1998 for a review), comparable to radio. However,
an additional substantial uncertainty arises due to the significant obscuration of the UV
photons by dust (see e.g. Hopkins 2004 for a summary of dust-obscuration correction
methods). This is a significant uncertainty that does not affect the radio regime (as well as
IR). Further, for example the tracers based on recombination lines (e.g. Hα, Hβ, Pα, Pβ),
which track the re-emitted stellar luminosity shortward of the Lyman limit, and therefore
directly probe young massive stellar populations, have a scatter of ∼ 30% given different
calibrations; while the IR star formation proxy, which traces the bolometric luminosity
produced by young stars, has an intrinsic scatter of ∼ 50% (Bell 2003; see also Kennicutt
1998).
1.2.3
The properties of radio sources in the local universe
In this section the major past results about the properties of radio sources in the local
universe are discussed. These are mainly based on all-sky radio surveys combined with
surveys at other wavelengths.
The local radio luminosity functions
The FIRST and NVSS sky surveys have significantly expanded our knowledge of properties
of extragalactic radio sources in the local universe. A tremendous amount of results has
emerged by combining these radio data with optical SDSS and 2dF spectroscopy (obtained
over 1000’s of square degrees of the sky; Ivezić et al. 2002; Sadler et al. 2002a; Best 2004;
Best et al. 2005), as well as the IR IRAS (e.g. Obrić et al. 2006) and X-ray ROSAT (e.g.
Brinkmann et al. 2000) all-sky surveys (see Appendix A for details about all-sky surveys).
For example, the local 1.4 GHz radio luminosity function was constructed with high preci-
14
1. Introduction
sion for spectroscopically identified star forming and AGN galaxies over a broad range of
radio luminosities (ranging from the faintest to the brightest end; ∼ 1020 − 1026 W Hz−1 ),
finding a steeper slope for star forming than for AGN galaxies (Condon 1984a; Sadler et al.
1999; Jackson & Londish 2000; Chan et al. 2004; Best et al. 2005). This implies that star
forming galaxies dominate the local comoving number density of radio-selected sources
at low 1.4 GHz luminosity, while AGN dominate the high end; however there is no clear
division in luminosity between the two.
The 1.4 GHz luminosity function for star forming galaxies was shown to agree well with
the 60 µm luminosity function for star forming galaxies (see Best et al. 2005 and references
therein), however seemingly deviating at the low luminosity end. This was interpreted as
an indication that the radio – FIR correlation becomes non-linear at low luminosities
(< 1022 W Hz−1 ; see also Yun et al. 2001) implying that either radio or FIR luminosity
is not directly proportional to star formation. However, based on a compilation of ∼ 250
local galaxies observed from UV through IR to radio, Bell (2003) has not detected such a
curvature in the IR – radio correlation at low luminosities. He further showed that the IR
traces most of the star formation in luminous galaxies but it traces only a small fraction
of the star formation in faint galaxies. Inverting the above argument, Bell (2003) argued
that the linearity of the IR – radio correlation implies that both indicators underestimate
the star formation rate at low luminosities, and proposed revised star formation rate calibrations taking this effect into account. These calibrations of total IR luminosity to star
formation rate, and 1.4 GHz radio luminosity to star formation rate will be used in latter
sections.
Environmental dependence of star forming and AGN radio sources
The dependence on environment of radio luminous star forming and AGN galaxies has
been studied in full detail in the local (z . 0.3) universe (Best 2004). It was demonstrated
that the fraction of radio-selected star forming galaxies is lower in high-density environments (& 10 Mpc−1 ). This is consistent with the results from optical studies known
for many years: star formation rates are strongly suppressed in central regions of galaxy
clusters (e.g. Dressler et al. 1985; Balogh et al. 1998; Hashimoto et al. 1998; Carter et al.
2001; Lewis et al. 2002; Martini et al. 2002). On the other hand, radio luminous AGN
are preferentially located in galaxy groups and poor-to-moderate richness galaxy clusters,
avoiding the lowest density regions (. 0.2 Mpc−1 ). Strikingly, Best (2004) has found that
the ratio of absorption-line to emission-line AGN3 dramatically changes with environment:
essentially all radio luminous AGN in rich environments contain no spectral lines in emission (in the 3700 − 7900 Å range). This result was interpreted as a consequence of i)
the boosting of the AGN radio jet power (which correlates with emission line luminosity;
3
These two types of radio luminous AGN, emission- and absorption-line AGN, are simply distinguished
by the presence of emission lines in their optical spectra (see Chap. 3 for further discrimination of emissionline AGN from star forming galaxies which also contain emission lines in their spectra). Otherwise
absorption- and emission-line AGN have comparable host galaxy properties and cover a similar range
of radio luminosities.
1.2 The radio sky
15
Rawlings & Saunders 1991) due to the reduced adiabatic expansion losses caused by the
jet confining effect by the intra-cluster medium (Barthel & Arnaud 1996), and ii) the lack
of cold gas close to the AGN (that produces the emission lines) due to physical processes
such as tidal interaction, galaxy harassment, ram pressure stripping, that may remove the
gas out of galaxies, and preferentially happen in dense environments.
1.2.4
Challenges in studying evolution of the radio population
To study the evolution of the radio population, first its composition needs to be known as
it consists of physically distinct types of sources (e.g. starbursts and AGN), each possibly
having a different evolutionary path. The main difficulty with the interpretation of radio
continuum observations (i.e. non-optical observations in general) is the immediate need for
panchromatic, in particular optical, data in order to obtain i) the cosmological distance
(redshift) of the observed source, and ii) the dominating origin of its radio emission. As
the radio SED itself is a featureless power law (synchrotron spectrum; see Sec. 1.2.2),
source redshifts cannot be determined from radio observations alone. In addition, the
discrimination of the origin of radio emission requires panchromatic data. These challenges
have been overcome by a large amount in the local universe due to various all-sky surveys
at different wavelengths. However, panchromatic observations of the more distant universe
are essential to tackle the evolution of radio sources.
Over the course of time the sensitivity of radio surveys has been significantly lowered
allowing a better insight into the entire extragalactic radio population. However, for a long
time radio sources detected in the distant universe were AGN, rather than star forming
galaxies for reasons outlined below. In general, due to the difficulty in the past of obtaining
panchromatic data for the observed radio sources, and therefore not being able to probe the
third spatial dimension – the cosmological distance, radio astronomy diverted to modeling
the ’radio source counts’.
The differential radio source counts
The most straight-forward information that can be extracted from a radio survey, without
the need for panchromatic data, are the radio source counts. The slope of the counts is
essentially dictated by the relative contribution of different source populations (AGN vs.
star forming galaxies) at certain fluxes, which is the result of (possible) evolution of their
luminosity functions with redshift. Therefore, the source counts represent a first-order
observational constraint for evolutionary models of radio sources. The classical tool to
test for a constant space density of sources is the ’log N − log S’ test (where N stands
for the source space density, and S for the total flux density). The major advantage of
this test is that it does not require knowledge of the cosmological distance of individual
sources. [Note, however, that no information about evolution in luminosity, which has been
generally found to be stronger than density evolution (e.g. Hopkins 2004; Le Floc’h et al.
2005), can be extracted using this test.] If space was Euclidian, and the space density of
sources constant, then the differential radio source counts (N) would follow a power law
16
1. Introduction
with an exponent of −2.5. A generalization to non-Euclidian space implies a power law
with a higher exponent (i.e flatter slope in log N − log S; see e.g. Peterson 1997, Chapters
10 and 11).
Figure 1.7
The differential radio source counts, normalized to Euclidian space, shown for various radio surveys at 1.4 GHz (indicated
at the top left). The radio counts
have extensively been used in the
past to get an insight into the density evolution of radio sources (using the log N − log S test; see text
for details). It was shown that down
to ∼ 1 mJy from the highest fluxes,
the counts are dominated by radioloud FR II and FR I galaxies, while
the upturn below ∼ 1 mJy has been
attributed to the rise of a ’new’ population of objects (see text for details). However, the composition of
these submillijansky radio sources is
not well understood. Explaining the
composition of this faint radio population is one of the major topics of
this thesis.
The differential radio source counts derived by various surveys at 20 cm are shown in
Fig. 1.7. At the brightest end (& 100 mJy) the counts have been observed to be steeper than
predicted by the log N − log S model (i.e. at fainter fluxes more sources are observed than
predicted) implying a strong positive evolution (increasing number density with increasing
redshift; see also Dunlop & Peacock 1990). Based on a spectroscopically complete sample
of these bright radio sources Willott et al. (2002) have shown that they are dominated by
’radio loud’ objects with powerful radio-emitting jets, consistent with the Fanaroff & Riley
(FR) Class II objects (see Sec. 1.2.2).
At fainter flux levels, down to ∼ 1 mJy, the differential source counts follow a power law
consistent with the log N − log S model prediction, immediately excluding strong density
evolution (see also Clewley & Jarvis 2004). Based on panchromatic data it has been shown
that at these radio flux levels the dominant population still consists of ’radio loud’ AGN,
given their radio to optical flux ratios, however these objects are mainly FR I galaxies (e.g.
Windhorst et al. 1993; Waddington et al. 2001). If the same population of objects would
continue to dominate the differential source counts at fainter fluxes (. 1 mJy), they would
continue to monotonically decrease following a power law (see e.g. Jarvis & Rawlings 2004).
However, below ∼ 1 mJy there is a significant upturn in the slope (when normalized to
Euclidian space; see Fig. 1.7), and this has been attributed to the rise of a ’new’ population
of objects that does not significantly contribute at brighter flux levels (e.g. Condon 1984a;
Windhorst et al. 1985a; Seymour et al. 2004). This population of objects is often referred
to in literature as the ’submillijansky’ or ’microjansky’ radio population, where the first is
used in the remainder of this thesis.
1.2 The radio sky
17
The ’submillijansky’ radio population: controversy about its composition
Using optical spectroscopy for a fraction of sources in observed radio samples reaching to
submillijansky levels (see Chap. 3 for full details) this faint radio population has been
associated predominantly with spiral galaxies (Condon 1984a; Windhorst et al. 1985a;
Benn et al. 1993). Although already Benn et al. (1993) pointed out that spiral galaxies may be Seyferts (therefore AGN) or star forming galaxies, in later years it has often
been assumed that the submillijansky population is dominated only by star forming galaxies (e.g. Seymour et al. 2004), completely neglecting the contribution of AGN (especially
radio-quiet ones). Such a simple assumption, however, is not necessarily correct for the
following reasons. As stated above, past studies have well determined that radio-loud AGN
dominate the source counts above 1 mJy. However, radio-loud AGN form only 10% of the
overall AGN population, implying that 90% of (optically selected) AGN in the universe
remain radio-quiet (e.g. Goldschmidt et al. 1999; Ivezić et al. 2002). Jarvis & Rawlings
(2004) have first suggested that radio-quiet AGN may form a significant contribution to
the radio population at the submillijansky flux levels, and circumstantial observational
evidence verifying this has been found by Simpson et al. (2006).
To date studies of the faint radio population have not reached a concensus on the exact
composition of the submillijansky radio population for several reasons that have seriously
conspired. Namely, to reveal the nature of this population it is essential to characterize
the physical origin of the radio emission, i.e. accurately subdivide the sources into distinct
populations such as AGN- or star formation- dominated galaxies. However, the discrimination between these two types of objects is technically extremely challenging (especially
beyond the local universe). The only way this can reliably be done is to assemble a wealth
of panchromatic data, which is in itself a non-trivial and time consuming process. In this
thesis a new deep radio survey of a large area on the sky (VLA-COSMOS - Schinnerer et al.
2007; Chap. 2), observed over the entire electromagnetic spectrum (X-ray to radio; COSMOS - Scoville et al. 2007a), is presented (for its description see Sec. 1.3). This survey
allows for the first time a full-depth analysis using to date largest, and statistically complete
sample of the submillijansky radio population revealing its true nature (see Chap. 3).
The cosmic star formation history based on radio data
In the past decade the construction of large galaxy redshift surveys has allowed to estimate
the evolution of the cosmic star formation rate density (’cosmic star formation history’ –
CSFH hereafter; e.g. Madau et al. 1996; Lilly et al. 1996; Steidel et al. 1999a; Flores et al.
1999; Haarsma et al. 2000; Hopkins 2004; Le Floc’h et al. 2005; Caputi et al. 2007), which
can be interpreted as evolution of the total stellar mass in the universe generated through
star formation.
Initially, a large discrepancy has been found between the CSFH derived using different,
short- vs. long- wavelength, star formation tracers such as e.g. UV/optical vs. IR (e.g.
Sanders 2003) or UV/optical vs. radio (Haarsma et al. 2000). This has been attributed
to dust reservoirs within galaxies that attenuate large fractions of short-wavelength light
18
1. Introduction
produced by young stars (see also Sec. 1.1.4). Dust obscuration has been shown to be
most severe in the most intensely star forming galaxies (with high IR/radio luminosities and already assembled high stellar masses; e.g. Bell 2003) implying that UV/optical
star formation tracers are most affected by attenuation in the most efficient generators
of stellar mass. This precisely illustrates the already mentioned need for panchromatic
observations in terms of understanding galaxy evolution. In particular, the CSFH derived
using radio data is an important independent complement to the IR regime as both are
dust-unbiased star formation tracers. An important prerequisite, however, for the usage of
any of these two proxies is a robust discriminator between star forming and AGN galaxies
in the observed monochromatic sample as both synchrotron emission and heated dust may
have their origin in both, star formation and AGN related processes. This has been often
neglected in the IR regime (e.g. Le Floc’h et al. 2005) for reasons such as lack of panchromatic data and commonly low angular resolution of IR observations (& 5”) disabling an
insight into the IR morphology (e.g. nucleus vs. disk dominated radiation). On the other
hand, to date the radio-based CSFH has been derived using only an extremely small sample of ∼ 40 (spectroscopically selected) star forming galaxies out to high redshifts z ∼ 2
(Haarsma et al. 2000). Obviously, large uncertainties are associated with this estimate
carrying the potential for significant improvements (see Chap. 4).
Accounting for dust obscuration affecting short-wavelength star formation tracers using luminosity dependent corrections, the different wavelength based CSFHs have been put
into agreement within a factor of ∼ 3 scatter (out to z ∼ 1; see Hopkins 2004) demonstrating that the cosmic (volume normalized) star formation rate has declined by an order of
magnitude since z ∼ 1. IR studies have shown that LIRGs account for the majority of the
cosmic star formation rate density at z ∼ 1 (e.g. Le Floc’h et al. 2005; Zheng et al. 2006;
Caputi et al. 2007). With no attempt made to separate star forming from AGN ULIRGs
Le Floc’h et al. (2005) have suggested that at ∼ 2 the population dominating the cosmic
star formation rate density consists of ULIRGs. However, using only two narrow redshift
ranges centered at z ∼ 1 and z ∼ 2 Caputi et al. (2007) have cautioned that the fraction of
AGN dominated ULIRGs seems to increase with redshift, and that therefore the cosmic star
formation rate density is not dominated by star forming ULIRGs at z ∼ 2. Further, only
recently Daddi et al. (2007) have demonstrated that, at least at z ∼ 2, the AGN fraction
in MIR selected samples is a function of stellar mass and reaches 50 −60% at the high mass
end (> 4×1010 M⊙ ). Overall, it remains unknown how exactly the stellar mass assembly in
these most intensely star forming galaxies (& 100M⊙ yr−1 ), rarely found in space, evolves.
More detailed studies of this subject have mostly been hampered by practical challenges
related to observations of large comoving volumes out to high redshifts (needed to obtain
statistically large enough samples of both star forming and AGN ULIRGs), and simultaneously performing panchromatic observations with comparable sensitivity (in order to
efficiently separate the two classes of objects at all redshifts). In Chap. 4 of this thesis the
evolution of the stellar mass generation in the most intensively star forming galaxies since
∼ 5 Gyr (∼ 1/3 of the age of the universe) after the Big Bang (z = 1.3) is derived for the
first time using a large sample of star forming ULIRGs identified in the VLA-COSMOS
survey.
1.3 Studying galaxy evolution via (panchromatic) look-back surveys
1.3
1.3.1
19
Studying galaxy evolution via (panchromatic)
look-back surveys
Selection of sky area and wavelength range surveyed
Deep field surveys are the optimal tool for studying co-evolution of galaxies, such as starbursts and AGN. Because of their high sensitivity they allow constructing samples (using
certain selection criteria) at different redshifts and studying how their properties evolve
with cosmic time. As the ultimate goal of studying the universe via sky-surveys is to understand galaxy formation and evolution, a full panchromatic (X-ray to radio) coverage is
necessary for a unified picture.
In practice, sky surveys are always a compromise between area size and sensitivity.
A small-area implies possible ’cosmic variance’ difficulties, and low chances of observing
objects in rare phases. On the other hand, a larger area yields lower sensitivity, but
gains in sampling representative large scale structure (LSS) as well as rare objects. A
complex strategy is therefore required for mapping galaxy formation and evolution (i.e. the
’wedding-cake’ approach). The deepest, pencil-beam surveys [HDF - Hubble Deep Field
(Williams et al. 1996) and UDF – Ultra Deep Field (Beckwith et al. 2006)] are necessary
to probe the galaxy inventory to the faintest levels and greatest distance. However, field
of views of 2′ − 3′ correspond only to 4 − 6 comoving Mpc at z = 3. For example, large
differences, due to ’cosmic variance’, are seen between HDF-North and HDF-South in the
radio number counts (Dickinson et al. 2002; see Fig. 1.7). Simulations of LSS (ΛCDM)
show that the typical scale-length of the LSS bubbles and voids is ∼ 20 − 40 comoving
Mpc (see Scoville et al. 2007a and references therein). Therefore, the minimum scale that
must be covered in order to fairly sample the full range of environments at z ∼ 1 is ∼ 40
comoving Mpc at this redshift. This roughly corresponds to an angular field size of 1.4◦ in
a ΛCDM universe.
Surveys in the past have mainly been wavelength-driven allowing for studies of specific
kinds of galaxy populations. For example, optical surveys sample ’normal’ galaxies and
’monsters’; IR surveys have been specialized for trying to understand the broad galaxy
population called ’infrared galaxies’; surveys performed in the X-ray regime study the AGN
population (and galaxy clusters). However, to reach a full consensus of galaxy properties
and their evolution, understanding of the full galaxy SED is needed. Therefore, the optimal
survey strategy, based on physical ground, is panchromatism, i.e. observations of a sky-area
over the full electromagnetic spectrum.
1.3.2
The COSMOS and VLA-COSMOS surveys
The panchromatic (X-ray to radio) COSMOS survey (Scoville et al. 2007a) of an equatorial
1.4◦ × 1.4◦ field on the sky has been optimally constructed to encompass a large enough
area for sampling the full range of environments at z ∼ 1, as well as statistically complete
samples of objects in rare phases, to unprecedented sensitivity over the full electromagnetic
20
1. Introduction
spectrum. This panchromatic data-set is used as the basis of this thesis. A detailed
description of all aspects of this project can be found in the COSMOS ApJS special issue
(September 2007, Volume 172).
COSMOS stands out among other multi-wavelength surveys (see Fig. 1.8) because of
two major advantages: i) the large area provides enough sources to enable robust statistical analyses, even of low density objects in rare phases and ii) the entire field has been
observed to an unprecedented depth over the full wavelength range, allowing for detailed
in-depth panchromatic studies. The COSMOS field has been observed to date by the major
space- (Hubble, Spitzer, GALEX, XMM, Chandra) and ground-based observatories (see
Scoville et al. 2007a for details).
Figure 1.8
Comparison of the COSMOS 2◦ field with other fields observed with the Hubble Space
Telescope, such as GEMS (Galaxy Evolution from Morphologies and SEDs; Rix et al. 2004), GOODS
(Great Observatories Origins Deep Survey, Giavalisco et al. 2004) and HUDF (Hubble Ultra Deep Field,
Beckwith et al. 2006). For reference, the full Moon (0.5 deg in diameter) is also shown. The large area
of the COSMOS panchromatic project has several major advantages, compared to other surveys. First,
the full range of large scale structure at z ∼ 1 is sampled, opening a new dimension for galaxy evolution
studies. Secondly, for the first time in-depth studies of objects in rare phases (e.g. star forming ULIRGs)
out to high redshifts, and over the full electromagnetic spectrum, are possible due to the large comoving
volumes observed.
The radio observations of the COSMOS field, performed with the Very Large Array at
20 cm (see Chap. 2) are the current state-of-the-art in the radio regime, as they combine
high sensitivity and high spatial resolution over such a large area. The VLA-COSMOS field
contains ∼ 3, 600 sources brighter than ∼ 50 µJy beam−1 (4.5σ) at a resolution of 1.5′′ , and
probes an unique and key area of parameter space, bridging the gap between shallow, wide
1.3 Studying galaxy evolution via (panchromatic) look-back surveys
21
field surveys, such as FIRST (with about one million source entries), and ultra-sensitive
(∼ 5µJy), narrow field (∼ 15′ ) studies sampling only a few hundred sources with very ununiform noise (e.g. Fomalont et al. 2006; see also Tab. 2.1). Possible comparative surveys
are the Phoenix deep field, done with the ATCA (Hopkins et al.2003) or the VIRMOS 02hr
field (Bondi et al. 2003). However, both surveys have a lower angular resolution which
hampers the identification of the correct counterparts at other wavelengths.
One of the major advantages of the VLA-COSMOS survey is the large area observed
with fairly uniform noise, yielding a statistically significant sample of faint radio sources
reaching out to relevant depths. In particular, large comoving volumes observed at different
cosmic times allow sampling high numbers of the rare highest luminosity galaxies (equivalent to ULIRGs) which represent an important galaxy evolutionary stage (as described in
Sec. 1.1). Further, the most massive, radio selected, galaxies that locally strongly depend
on environment (as shown in Sec. 1.2) are observed over the full range of LSS. The additional advantage is the full panchromatic coverage of the field, allowing in-depth studies
of the radio population, with the potential of putting an end to the controversy about
the composition of the submillijansky radio population using a newly developed panchromatic method that efficiently separates radio-luminous AGN from star forming galaxies
(see Chap. 3). Further, the cosmic star formation history of the spatially rare objects
(ULIRGs) at the high-luminosity end can for the first time be robustly derived using highquality radio data (see Chap. 4).
The VLA-COSMOS 1.4 GHz survey of the 2◦ COSMOS field is presented in Chap. 2,
where the observations, data reduction, imaging and extensive tests are discussed. In
Chap. 3 the classification of the VLA-COSMOS radio population is presented, and the
composition of the submillijansky radio sources is studied using the full COSMOS panchromatic data-set. The cosmic star formation history since z = 1.3 is is constrained in Chap. 4
using the well defined sample of VLA-COSMOS intensively star forming galaxies. An indepth case study of a peculiar radio galaxy in a merging cluster environment is given in
Chap. 5. The major scientific results presented in this thesis and future prospects are
summarized in Chap. 6.
22
1. Introduction
Chapter 2
The VLA-COSMOS Survey: Source
catalog of the Large Project
In this Chapter the VLA-COSMOS Large Project is described, from initial observations
to the final source catalog which is publically available. This work has been published in
the COSMOS ApJS special issue as:
Schinnerer, E., Smolčić, V., Carilli, C. L., Bondi, M., Ciliegi, P., Jahnke, K., et al.,
2007, ApJS, 172, 46, The VLA-COSMOS Survey. II. Source Catalog of the Large Project1
Abstract
The VLA-COSMOS large project is described and its scientific objective is discussed. We
present a catalog of ∼ 3, 600 radio sources found in the 2◦ COSMOS field at 1.4 GHz.
The observations in the VLA A and C configuration resulted in a resolution of 1.5′′ ×1.4′′
and a mean rms noise of ∼ 10.5(15) µJy/beam in the central 1(2) ◦ . 80 radio sources are
clearly extended consisting of multiple components, and most of them appear to be doublelobed radio galaxies. The astrometry of the catalog has been thoroughly tested and the
uncertainty in the relative and absolute astrometry are 130 mas and <55 mas, respectively.
2.1
Introduction
The radio source counts above the milli-Jansky level are dominated by radio galaxies
and quasars powered by active galactic nuclei (AGN) in elliptical host galaxies. How1
Although second author in the published version, the work presented in this Chapter done by the
author of this thesis (VS) accounts for ∼ 80% full time work during more than the first half of the
PhD (> 1.5 years), and specifically includes ∼ 70% of the VLA-COSMOS data reduction; performing
extensive tests on the optimal imaging set-up (including self-calibration); imaging about 80% of the data;
performing and writing up the tests presented in the paper on the flux calibration and (relative and
absolute) astrometry, as well as correlating the FIRST, NVSS and VLA-COSMOS catalogs for comparison
of integrated flux densities of common sources; and producing 8/18 figures presented in the paper. The
source catalog was produced by MB and PC (INAF, Bologna, Italy). The VLA observational set-up was
done by ES (PI; MPIA, Germany) and CC (NRAO, Socorro), and the catalog verification by ES and KJ
(MPIA, Germany).
24
2. VLA-COSMOS Large Project
ever, deep radio surveys at 1.4 GHz show an upturn in the integrated source counts at
sub-mJy levels revealing the presence of a population of faint radio sources far in excess
of those expected from the high luminosity radio galaxies and quasars which dominate
at higher fluxes (Windhorst et al. 1985b; Hopkins et al. 1998; Ciliegi et al. 1999; Richards
2000; Prandoni et al. 2001; Hopkins et al. 2003; Huynh et al. 2005). While radio sources
with relatively bright optical counterparts are starburst galaxies (e.g. Benn et al. 1993;
Afonso et al. 2005), the ones with fainter optical counterparts are often redder as expected
for early type galaxies (Gruppioni et al. 1999). Recent detailed multi-wavelength followup of faint radio sources showed a mixture of active star forming galaxies and AGN hosts
(Roche et al. 2002; Afonso et al. 2006). The exact mixture of these different populations
(high-z AGN out to the highest redshifts, intermediate-z post starburst, and lower-z emission line galaxies) as a function of radio flux level is not very well established, especially
in the µJy regime.
In order to fully investigate the nature and evolution of the µJy population it is necessary to couple deep radio observations with high quality imaging and spectroscopic data
from other wavelengths covering as much of the electromagnetic spectrum as possible. The
international COSMOS (Cosmic Evolution) survey (Scoville et al. 2007a)2 provides such a
unique opportunity. COSMOS is a pan-chromatic imaging and spectroscopic survey of a
1.4◦ × 1.4◦ field designed to probe galaxy and SMBH (super-massive black hole) evolution
as a function of cosmic environment. One major aspect of the COSMOS survey is the
HST Treasury project (Scoville et al. 2007c), entailing the largest ever allocation of HST
telescope time. The equatorial location of the COSMOS field offers the critical advantage of allowing major observatories from both hemispheres to join forces in this endeavor.
State-of-the-art imaging data at all wavelengths (X-ray to centimeter, e.g. Hasinger et al.
2007; Schinnerer et al. 2007; Taniguchi et al. 2007; Capak et al. 2007; Bertoldi et al. 2007;
Aguirre et al. 2007; Schinnerer et al. 2004) plus large optical spectroscopic campaigns using the VLT/VIMOS and the Magellan/IMACS instruments (Lilly et al. 2007; Impey et al.
2007; Trump et al. 2007) have been or are currently being obtained for the COSMOS field.
These make the COSMOS field an excellent resource for observational cosmology and
galaxy evolution in the important redshift range z ∼ 0.5 − 3, a time span covering ∼75%
of the lifetime of the universe.
One major scientific rationale of the COSMOS survey is to study the relation between
the large scale structure (LSS) and the evolution of galaxies and SMBHs. In a ΛCDM
cosmology, galaxies in the early universe grow through two major processes: dissipational
collapse and merging of lower mass protogalactic and galactic components. Their intrinsic
evolution is then driven by the conversion of primordial and interstellar gas into stars,
with galactic merging and interactions triggering star formation and starbursts. Mergers
also can perturb the gravitational potential in the vicinity of the black hole, thus initiating or enhancing AGN activity. Several lines of evidence suggest that galaxy evolution
and black hole growth are closely connected; COSMOS offers the chance to observe this
connection directly. While there is general agreement over this qualitative picture, the
2
http://www.astro.caltech.edu/∼cosmos
2.2 Survey objective
25
timing/occurrence of these events and their dependence on the local environment remains
to be observationally explored (e.g. Ferguson et al. 2000). To study LSS it is essential to
obtain high spatial resolution data over the entire electromagnetic spectrum covering a
significant area on the sky, like 2◦ as in the case of the COSMOS survey. Also, surveys
of active galactic nuclei benefit from such a combination of areal coverage and depth.
For the radio observations at 1.4 GHz, it was essential to match the typical resolution for
optical-NIR ground-based data of ∼ 1′′ to fully exploit the COSMOS database. Therefore
observations with the NRAO Very Large Array (VLA) had to be conducted in the A-array
that provides a resolution of about 2′′ (FWHM) at 1.4 GHz. Mosaicking is necessary to
cover the large area of the COSMOS field. The VLA-COSMOS survey consists of the pilot
project (Schinnerer et al. 2004), the large project (presented here) and the ongoing deep
project (focusing on the central 1◦ ; Schinnerer et al., in prep.). The VLA-COSMOS pilot
project tested the mosaicking capabilities in the VLA A-array at 1.4 GHz in the wide-field
imaging mode and has provided the initial astrometric frame for the COSMOS field.
Here we present the source catalog derived from the 1.4 GHz image of the VLACOSMOS large project. The paper is organized as follows: after a brief description of
the survey objective (Sec. 2.2), the details of the observations and data reduction are presented in Sec. 2.3 and Sec. 2.4, respectively. In Sec. 2.5, we discuss our tests for flux and
astrometric calibration. The VLA-COSMOS catalog is described in Sec. 2.6, while the
context of the VLA-COSMOS survey within the COSMOS project is discussed in Sec. 2.7.
2.2
Survey objective
Unlike most existing deep survey fields, the COSMOS field is equatorial and hence has
excellent accessibility from all ground-based facilities (current and future such as [E]VLA
and ALMA). In addition, it has an extensive multi-wavelength coverage (Scoville et al.
2007a). This makes it an ideal field to analyze the (faint) radio source population as a
function of redshift, environment, galaxy morphology and other properties. The VLACOSMOS radio observations were matched to study a range of important issues related
to the history of star formation, the growth of super-massive black holes, and the spatial
clustering of galaxies. The ongoing spectroscopic surveys within the COSMOS project
are also targeting well-defined samples of radio sources as part of the overall program. In
addition, the VLA-COSMOS radio survey is providing the absolute astrometric frame for
the COSMOS field (Aussel et al., in prep), which is important given the field’s large size.
In this paper we describe in detail the observing procedure, and various tests on data
quality and characteristics (astrometry, fitted source parameters, etc.; see also the pilot
project paper by Schinnerer et al. 2004). The completeness tests and the number counts
of this survey are under-way (Bondi et al., in prep.) as well as the identification of optical
counterparts using the space- and ground-based COSMOS imaging data (Ciliegi et al., in
prep.). The full source catalog is available from the COSMOS archive at IPAC/IRSA3 .
Subsequent papers will consider important scientific issues such as: (i) the evolution of
3
http://www.irsa.ipac.caltech.edu/data/COSMOS/
26
2. VLA-COSMOS Large Project
radio-loud AGN as a function of environment, including comparison to X-ray AGN and
clusters (see also Smolčić et al. 2007a, Chap. 5), and a search for type-II radio QSOs,
and (ii) a dust-unbiased survey of star forming galaxies, as revealed in the sub-mJy radio
source population, including consideration of the evolution of the radio-FIR correlation
out to z ∼ 1 through comparison with the Spitzer data, and of extreme, high z starbursts
as seen in the MAMBO 250 GHz COSMOS survey (Bertoldi et al. 2007). In the following
sections we describe the goals of these two key science programs in more detail.
2.2.1
Survey area
The sub-mJy radio source counts provide one of the best indicators of the effect of cosmic
variance: number counts of sub-mJy radio sources in fields of order of ∼ 10′ in diameter
show a factor three variation (e.g. Hopkins et al. 2003), indicating that such field sizes are
inadequate to map cosmic large scale structure. Thus to properly sample the faint radio
source population and map out its cosmic structure to the largest relevant scales, it is
necessary to survey a large area at the same resolution and sensitivity. Proper studies of
source clustering require hundreds to thousands of sources. In order to enable detailed
studies of environmental effects on faint, distant radio source distributions and properties,
all as a function of redshift, several thousand sources are required as well.
Deep radio imaging of the 2◦ COSMOS field with ∼ 3, 600 sources allows one to probe
a - unique and - key area of parameter space. The combination of high sensitivity and
high spatial resolution over a large area (see Tab. 2.1) bridges the gap between shallow,
wider field surveys, such as FIRST (Becker et al. 1995) and NVSS (Condon et al. 1998)
with about one million source entries, and ultra-sensitive (≤ 5 − 7µJy), narrow field (single
VLA primary beam ∼ 30′ FWHM) studies of a few hundred sources, such as those by
Fomalont et al. (2006); Richards (2000). Surveys which are comparable in scope to the
VLA-COSMOS large project are the Phoenix deep field survey (PDS), undertaken with
the ATCA (Hopkins et al. 2003), and the VVDS 02hr field done with the VLA in B-array
(Bondi et al. 2003). These surveys produce a lower angular resolution and a slightly higher
rms (see Tab. 2.1).
2.2.2
Star forming galaxies
Tracing the evolution of the cosmic star formation history from optical surveys bears the
large uncertainty of dust corrections (e.g Steidel et al. 1999b). Deep VLA observations
of the COSMOS field can provide a unique, unobscured look at star forming galaxies
and highly extincted galaxies in the full range of environment, especially in combination
with the deep (sub)mm data (Bertoldi et al. 2007; Aguirre et al. 2007) and deep Spitzer
infrared imaging (Sanders et al. 2007) to which the high resolution of the VLA images
provides means to properly identify luminous infrared galaxies (see Fig. 2.1). The VLA
radio data will particularly be helpful to (a) trace the cosmological star formation history
and (b) test the FIR/radio correlation at high redshifts. The radio luminosity of local
galaxies is well-correlated with their star formation (SF) rate (Condon 1992), and needs,
2.2 Survey objective
27
Table 2.1 Radio Surveys at 1.4 GHz
Field
COSMOS (large)
COSMOS (pilot)
HDFN
SSA 13
FIRST
FLS
VVDS
ATHDFS
ATESP
PDS
ELAISa
Lockman
NVSS
a consists
Area
[◦ ]
2
0.837
0.35
0.32
10,000
5
1
0.35
26
4.56
4.22
0.35
34,000
rms
[µJy/beam]
10.5
25
7.5
4.8
150
23
17
11
79
12
27
120
350
resolution
[′′ ×′′ ]
1.5×1.4
1.9×1.6
2.0×1.8
1.8
5
5
6
7.1×6.2
14×8
12×6
15
15
45
# of objects
3643
246
314
810
1,000,000
3565
1054
466
2960
2090
867
149
1,700,000
Reference
this paper
Schinnerer et al. 2004
Richards 2000
Fomalont et al. 2006
Becker et al. 1995
Condon et al. 2003
Bondi et al. 2003
Norris et al. 2005, Huynh et al. 2005
Prandoni et al. 2001
Hopkins et al. 2003
Ciliegi et al. 1999
de Ruiter et al. 1997
Condon et al. 1998
of 3 fields of the ELAIS survey: N1, N2, and N3
unlike optical tracers, no correction for dust obscuration. Thus radio sources with correct
spectral identification (as star forming galaxies) can be independently used to estimate the
SF history (of the luminous sources).
Recent work by Haarsma et al. (2000) for three deep radio surveys confirms the trend
of rising star formation rate between z = 0 and z = 1, however their calculated star
formation rates are significantly larger than even dust-corrected optically selected star
formation rates. A key uncertainty is the contribution of AGN to the faint (< 1mJy)
radio population, with estimates ranging from 20% to 80% for surveys down to 40 µJy.
The (far)IR-radio correlation for star forming galaxies appears to hold out to high redshift
(Garrett 2002; Appleton et al. 2004). However, the number of star forming sources detected
at 1.4 GHz is small above z = 0.5. A thorough understanding of the IR-radio correlation
out to higher redshifts is important, as it has been widely used as a distance measure
for sub-mm sources without any optical counterparts (Carilli & Yun 2000; Aretxaga et al.
2005). Also, an important question for active star forming galaxies is the role of mergers,
in particular at higher redshift. The FIR imaging alone will lack sufficient resolution
to address this issue, while the optical imaging will suffer from the standard problem of
obscuration in these very dusty systems. Only arcsecond resolution radio data will allow
the determination of the spatial distribution of star formation in dusty starbursts on scales
relevant for merging galaxies (∼ 10 kpc).
2.2.3
Active galactic nuclei
Only a large field and deep radio survey can provide information about the evolution of
the currently highly uncertain faint-end of the radio luminosity function. The fundamental
problem in the study of the evolution of radio-loud AGN has been that samples are drawn
from either very wide field, but very shallow surveys, or very deep, but very small field
surveys. The former are limited at high redshifts to only extreme luminosity sources,
28
2. VLA-COSMOS Large Project
Figure 2.1 The sensitivity limit as a function of (intrinsic) 1.4 GHz luminosity (or power). The limit for
the VLA-COSMOS large project corresponds to the bold solid line. The expected luminosities for various
classes of galaxies are indicated by the solid horizontal lines. The expected radio power was calculated
using the local IR-radio relation (Condon 1992) and assuming a spectral index of α = 0.8. The horizontal
dashed-dotted line corresponds to the assumed dividing line between radio-quiet and radio-loud AGN. (See
text for details.)
while the latter are plagued by relatively small number statistics and number variance.
The VLA-COSMOS survey was designed to enable the study of the demographics and
evolution of AGN by encompassing a large cosmological volume and by providing good
statistics on both radio-loud and radio-quiet AGN as a function of redshift.
Only sub-mJy sensitivities over a wide area are adequate to detect relatively weak (FRI)
radio AGN to very high redshift (z ∼ 6) while providing a large number (∼ 1000) of AGN
sources. At lower redshift, z ∼ 1, a sensitivity of 1σ ≈ 10 µJy/beam is good enough to
detect a significant fraction of radio-quiet, optically-selected QSOs. Moreover, questions
regarding redshift evolution of FRI and FRII sources, their parent galaxy properties, and
environmental dependencies can be addressed independently for QSOs and radio galaxies.
Such observations are sensitive enough to reach the classic boundary between radio-loud
and radio-quiet AGN (log L1.4 GHz [W Hz−1 ] = 25) at z ∼ 4-5 (depending on the exact
2.3 Observations
29
Table 2.2 VLA-COSMOS Large Project Pointing Centers
Pointing #
F01
F02
F03
F04
F05
F06
F07
F08
F09
F10
F11
F12a
F13
F14
F15
F16
F17
F18
F19
F20
F21
F22
F23
a COSMOS
R.A. (J2000)
10:02:28.67
10:01:28.64
10:00:28.60
09:59:28.56
09:58:28.52
10:01:58.66
10:00:58.62
09:59:58.58
09:58:58.54
10:02:28.67
10:01:28.64
10:00:28.60
09:59:28.56
09:58:28.62
10:01:58.66
10:00:58.62
09:59:58.58
09:58:58.54
10:02:28.67
10:01:28.64
10:00:28.60
09:59:28.56
09:58:28.52
DEC (J2000)
+02:38:19.84
+02:38:19.84
+02:38:19.84
+02:38:19.84
+02:38:19.84
+02:25:20.42
+02:25:20.42
+02:25:20.42
+02:25:20.42
+02:12:21.00
+02:12:21.00
+02:12:21.00
+02:12:21.00
+02:12:21.00
+01:59:21.58
+01:59:21.58
+01:59:21.58
+01:59:21.58
+01:46:22.24
+01:46:22.24
+01:46:22.24
+01:46:22.24
+01:46:22.24
Remark
P1 in pilot project
P2 in pilot project
P3 in pilot project
P4 in pilot project
P5 in pilot project
P6 in pilot project
P7 in pilot project
field center
spectral index; see Fig. 2.1). Highly luminous radio-loud objects such as Cygnus A with
log L1.4 GHz [W Hz −1 ] ∼ 34 (Carilli & Barthel 1996) should be observable out to their epoch
of formation.
2.3
Observations
The goal of the large project of the VLA-COSMOS survey was to image the entire COSMOS
field with an as large as possible uniform rms coverage while minimizing the observing time
required. Since the observations had to be finished within one configuration cycle, special
requirements arose for the pointing lay-out and the observing strategy.
2.3.1
Lay-out of the pointing centers
The pointing lay-out was designed to maximize the uniform noise coverage while minimizing the number of pointings required to limit overhead due to slewing (∼30 s slewing
time for each change of pointing). A hexagonal pattern of the pointing centers provides
both a uniform sensitivity distribution and a high mapping efficiency for large areas (see
Condon et al. 1998). To minimize the effect of bandwidth smearing, we used – as already
tested in the pilot observations (Schinnerer et al. 2004) – a separation of 15′ between the
individual field centers. A total of 23 separate pointings was required to fully cover the
2◦ of the COSMOS field (see Tab. 2.2 and Fig. 2.2).
30
2. VLA-COSMOS Large Project
Figure 2.2 The pointing pattern of the VLA-COSMOS Large Project overlaid onto a DSS image of
the area of the COSMOS field. The heavy-outlined circles indicate the pointings observed in the VLACOSMOS pilot project (Schinnerer et al. 2004). Each pointing has a radius of 16.8′ corresponding to the
cut-off radius used for making the mosaic. The dashed line marks the outline of the COSMOS field covered
by ACS tiles from the HST-COSMOS survey (see Scoville et al. 2007c).
2.3.2
Correlator set-up and calibrators
We used the standard VLA L-band continuum frequencies of 1.3649 and 1.4351 GHz and
the multi-channel continuum mode to minimize the effect of bandwidth smearing (in the
A configuration). This results in two intermediate frequencies (IF) with two polarizations,
providing 6 useable channels of 3.125 MHz each, or a total bandwidth of 37.5 MHz (observed
with both polarizations). (Nominally, 7 channels are available, however, due to the largely
reduced sensitivity in the last channel, we only used channels 1 to 6.)
The quasar 0521+166 (3C 138) served as flux and bandpass calibrator and was observed
at the beginning of each observation. To allow for good correction of atmospheric amplitude
2.4 Data reduction and imaging
31
and phase variations, we selected the quasar 1024-008 which was already used in the pilot
observations (Schinnerer et al. 2004). 1024-008 is about 6.1◦ away from the COSMOS field
center and has a flux of about 1 Jy at 1.4 GHz. Its positional accuracy is better than
0.01′′ (VLA Calibrator Manual 2003); the positional difference is less than 0.001′′ between
coordinates listed in the VLA Calibrator Manual and its ICRF (International Celestial
Reference Frame; Fey et al. 2004) position.
The quasar 0925+003 at a distance of about 9◦ from the COSMOS field center was
observed to test the absolute astrometric accuracy of the observations. Its positional
accuracy is known to better than 0.002′′ , and its 1.4 GHz flux is similar to the one of
1024-008. It was also used to test the flux calibration (see Section 2.5).
2.3.3
Observing strategy
This project holds the status of a VLA Large Project, as it required 240 hrs of observing
time in the A configuration alone. The observations were scheduled in blocks of 6 hrs
centered at the Local Siderial Time (LST) of 10:00 hr. This ensured that the COSMOS
field was always above 40◦ elevation during our observations to keep the system temperature
of the L-band receivers low. These observing blocks were scheduled over 42 days between
September 23th, 2004 and January 9th, 2005 for the A configuration, and between August
26th, 2005 and September, 25, 2005 for the C configuration. The observing time for the C
configuration consisted of 4 observing blocks each 6 hrs long, except for the last observation
that was 1.5 hrs longer.
In order to minimize the impact of varying observing conditions – especially during
the A array observations – onto the mosaic we adopted the following scheme: (a) all 23
pointings were observed with about 6.5 minutes integration time twice each day, (b) the
starting pointing was changed each time, (c) the flux calibrator 0521+166 was only observed
at the beginning (since interpolation between days in case of a loss was acceptable4 (d) the
phase calibrator 1024-008 was observed every 28 to 35 minutes, and (e) the test calibrator
0925+003 was observed twice each day after about one-third and two-thirds of the available
observing time. The rotation of the pointings with observing days also resulted in a more
complete uv coverage, and therefore a rounder synthesized (i.e. DIRTY) beam.
2.4
2.4.1
Data reduction and imaging
Data reduction
The data reduction was done using the Astronomical Imaging Processing System (AIPS;
Greisen 2003) following the standard routines as described in the VLA handbook on Data
Reduction. For the flux calibration and the correction of the atmospheric distortions we
used the pseudo-continuum channel. Before and after this calibration, uv points (of the
4
During the observations this happened only once, and the flux of the phase calibrator 1024-008 was
fairly stable through the curse of observations (see Sec. 2.5.1).
32
2. VLA-COSMOS Large Project
two calibrators 0521+166 and 1024-008) affected by radio frequency interference (RFI)
were flagged by hand using the AIPS task ’TVFLAG’. As the data were obtained in the
multi-channel continuum mode, a bandpass calibration was performed on the ’Line’ data
after the flux and phase calibration of the pseudo-continuum channel had been transfered
to the ’Line’ data. In order to exclude remaining RFI in the source data (i.e. the individual
COSMOS fields), we checked all channels (per IF and polarization) for RFI using ’TVFLG’
and flagged affected points accordingly. During all A-array observations, significant RFI
(affecting ∼ 15% of the data) was found to be present on IF2 in channel 4 to 6. In addition,
all uv data points in the A-array data above an amplitude of 0.4 Jy were clipped, since
no such strong source is present in any individual field. The C-array observations were
affected by strong RFI and solar interference, so that only baselines larger than 2.5 kλ and
1 kλ were included from the data of the first three days and the last day of observations,
respectively. The clipping level was set to 0.45 Jy for the C-array data.
2.4.2
Imaging
We performed substantial testing for best imaging quality including the application of
self-calibration on the COSMOS fields themselves. It was found that no combination of
parameters for the self-calibration in the task ’CALIB’ would yield a significant improvement of the rms (of > 3%). A robust weighting of 0 provided the best compromise for
the combined A+C array data between a fairly Gaussian synthesized beam (Fig. 2.3),
and still good sensitivity, i.e. the deviation from Gaussianity only starts below ±10% of
the peak. This proved to be especially important for fields which contained bright sources
(with peak fluxes up to 10 mJy/beam) where tests showed that sidelobe artifacts are lowest
when using a robust weighting of 0. The nominal increase in the noise compared to natural
weighting is 1.265. However, the gain in better cleaning results around bright sources is
larger than this nominal increase. Thus in order to achieve an uniform as possible rms
across the entire COSMOS field, a robust weighting of 0 is used.
In order to avoid geometric distortions due to the non-planarity of the wide-field on the
sky, each field was divided into 43 facets of 2048×2048 pixels which were imaged using the
option DO3DIMAG in the AIPS task ’IMAGR’. The pixel scale of 0.35′′ /pixel has been
well matched to the A+C-array beam size of FHWM 1.5′′ × 1.4′′ (PA ∼ −50o ) for a robust
weighting of 0 (Fig. 2.3 and 2.4). For each field, a contiguous area of about 1◦ diameter
was covered by the facets. Additional smaller facets of 128×128 pixels were made using
the task ’SETFC’ for positions of NVSS sources with peak fluxes above 0.1 Jy and within
a radial distance of 1.5◦ from the pointing center. This ensured that sidelobes from strong
sources outside the central 1◦ were CLEANed as well.
Since most of the COSMOS fields are affected by the sidelobes of radio galaxies with
peak fluxes between 1 to 15 mJy/beam, best CLEANing results were obtained if CLEAN
boxes for individual sources were provided. This ensured that CLEANing of negative or
positive residuals was minimized. In order to derive the CLEAN boxes for each field, we
used the AIPS task ’IMAGR’ to interactively select the CLEAN boxes in all facets where
significant sources were present. This procedure was performed combining the data of
2.4 Data reduction and imaging
33
Figure 2.3 Cuts along the x- (a) and y-axis (b) of the synthesized (i.e. DIRTY) beam for different values
of the robust weighting: +3 (grey dashed dotted line), +1 (dashed dotted line), 0 (solid line), -1 (dashed
line), and -3 (grey dashed line). A value of 0 for the robust parameter gave the best compromise between
synthesized beam shape and rms noise (see text for details).
all polarizations and IFs into one single image to obtain the highest possible S/N image.
The resulting list of CLEAN boxes was saved. In addition, we required that CLEAN
components were subtracted from the uv data after a facet had been cleaned. This way,
CLEAN components in overlapping facets were not treated separately. In addition, this
requirement also reduced the effect of sidelobe bumps from strong sources in neighboring
facets.
We would like to note at this point that the reduction process of the VLA-COSMOS
Pilot and Large dataset was not exactly identical. While self-calibration was applied to
the Pilot data, this step was not done while reducing the Large survey data: after detailed
empirical testing of the improvements due to self-calibration in the VLA-COSMOS Large
project, we concluded that no significant improvement was achieved, likely due to the lack
of sufficiently bright sources in all parts of the entire COSMOS field. Since self-calibration
adjusts the observed visibility phases to model phases, it has the potential to alter the
position of a given source. However, it is expected that these effects cancel out when using
several sources within a given pointing.
For the final stage of CLEANing, it turned out that the well known ’beam squint’
of the VLA (i.e. slightly different pointing centers for R and L polarization), and the
slightly different frequency coverages required separate imaging of all polarizations and
34
2. VLA-COSMOS Large Project
Figure 2.4 Representative synthesized beam belonging to pointing field 12 for a robust weighting of 0.
a) Large field view with contours of 2.5, 5, 10, 20, 40 and 80% of the maximum. The dashed box outlines
the area shown in b). b) Zoom into the central part of the synthesized beam with contours of 2, 4, 8, 16,
32, and 64% of the maximum. (The corresponding negative contours are shown in light gray.) The first
peaks of the sidelobes are below 10% of the maximum, overall the shape of the synthesized beam is fairly
well-behaved given the declination of the COSMOS field.
IF combinations. The four separate ’IMAGR’ runs were performed with the same list of
CLEAN boxes in the automatic mode. The number of iterations was set to 100,000, with a
flux limit of 45 µJy/beam (∼ 1.5σ in a single image of a field) and a gain of 0.1 to optimize
the CLEANing of the facets. The 43 facets forming the contiguous area were combined
using the AIPS task ’FLATN’. The four separate images were then combined using the
AIPS task ’COMB’ to obtain a single image for each field. Due to the combination of
bandwidth smearing and a significant drop in sensitivity outside the radius of the Half
Power Beam Width, we decided to use a cut-off radius of 0.4 (corresponding to a radius of
16.8′ ) when combining the individual fields into the final mosaic using the task ’FLATN’.
The resulting image is shown in Fig. 2.5.
2.5 Tests
35
Figure 2.5 The COSMOS field as observed at 1.4 GHz. Bottom: The 2◦ COSMOS field with the
ACS coverage (from Scoville et al. 2007c) indicated by the gray box. The two green boxes outline the
regions shown in the top panels. Top: Two regions enlarged demonstrate the quality of the data from the
VLA-COSMOS large project. The left (right) panel represents the lower (upper) green box in the bottom
panel. Each panel has a size of 2.8′ × 2.8′ corresponding to about 0.1% of the total area.
2.5
Tests
We performed a number of tests to evaluate our flux (see Sec. 2.5.1) and astrometric
calibration (see Sec. 2.5.2) as well as the impact of the CLEAN procedure. For the last
point, we performed a Gaussianity test on the noise. The noise was extracted from a
roughly 16′ × 11′ box close to the COSMOS field center. The individual noise pixels
36
2. VLA-COSMOS Large Project
show a Gaussian distribution (Fig. 2.6). A Gaussian fit gives an rms of 10.09 µJy/beam
(σ) (corresponding to a FWHM of 23.76 µJy/beam). All noise distributions extracted
for various boxes across the part of the field that has an uniform background showed a
Gaussian distribution demonstrating that no artifacts have been introduced during the
CLEAN process.
Figure 2.6 Distribution of the noise. Pixel
values extracted from a 16′ × 11′ box close to
the COSMOS field center show a Gaussian distribution in agreement with our assumption of
Gaussian noise. The fitted Gaussian (dashed
line) has a rms of 10.09 µJy/beam (σ) (i.e. a
FWHM of 23.76 µJy/beam). Noise distributions extracted from different boxes located
through out the uniform part of the field look
similar.
2.5.1
Flux calibration
The second phase calibrator 0925+003 was observed twice each day to allow for assessment
of the absolute astrometry and the flux calibration. Most of the following tests were
performed on the A-array only data, since it covered a wide range in time. We imaged the
calibrator 0925+003 for each day, as well as the two observations per day separately. All
IFs were combined at once, since the source of interest is at the phase center and any effects
due to misalignment should be negligible. The images were cleaned with 1000 iterations.
The resulting typical resolution and rms were 1.96′′ ×1.60′′ (FWHM) and ∼ 870 µJy/beam,
respectively. The position and flux of 0925+003 were derived by Gaussian fitting using the
AIPS task ’JMFIT’ on the individual images.
For most of the days 0521+166 served as the flux calibrator. The trends of the peak
flux of 0925+003 and 1024-008 are not the same over the course of the observations in the
A configuration (Fig. 2.7) indicating no systematic effects in the flux calibration. Note
that the error in the flux estimation for calibrator 1024-008 is significantly higher on day
MJD 60038 (November 11th, 2004). This is due to strong interferences that could not be
entirely removed in the uv data points.
We compared the peak flux density values of 0925+003 of the two observations per day
(Fig. 2.8). The median offset is 4.5 mJy/beam which corresponds to less than 1% of the
2.5 Tests
37
Figure 2.7 Comparison between the flux of the two calibrators 1024-008 and 0925+003 as a function of
observing date. The dots show the peak flux density with indicated 3σ errors.
total flux density of 0925+003. The outliers correspond to days MJD 59990 (September
24th, 2004), 60011 (October 15th, 2004) and 60096 (January 8th, 2005). The rms in the
maps for those days is about 1.3 − 2.6 times the typical rms in the 0925+003 maps. The
higher noise is likely to be caused by worse weather conditions (e.g. it was snowing on
November 13th, 2004) and/or technical problems during observations (e.g. RFI, intermittent fluctuations of the system temperature TSY S , data corruption on particular antennas).
Thus we conclude that our flux calibration is within the errors expected.
2.5.2
Absolute and relative astrometry
Given the angular resolution of the combined A+C array data of 1.5′′ × 1.4′′ (FWHM), we
expect to achieve a positional accuracy of ∼ 0.15′′ (corresponding to 1/10th of the beam
for lower S/N cases when
size; see Fomalont 1999) for high S/N sources and ∼ FHWM
S/N
extracting the source position within the COSMOS field.
In order to assess the quality of the absolute astrometric calibration, all observations of
38
2. VLA-COSMOS Large Project
Figure 2.8 The peak flux density variations (dots) of the two observations per day for calibrator 0925+003
shown as a ratio of the measured peak flux densities. 3σ errors are indicated.
0925+003 were combined into a single image. A non-zero offset in RA and DEC of 53 mas
and 45 mas, respectively, has been found relative to the nominal position of 0925+003. This
offset is likely the result of the large angular separation of 14.5◦ between the two calibrators
(i.e. 0925+003 and 1024-008), which could lead to residual phase transfer errors due to,
for example, differential refraction corrections. We consider this offset as an upper limit
to our absolute astrometry error, since the (center of the) COSMOS field is only 6◦ away
from the phase calibrator 1024-008.
To test the quality of our relative astrometry, we extracted sources from each single
field and compared their positions to the ones extracted from the combined mosaic. We
searched for sources using the AIPS task ’SAD’ (Search And Destroy). On single fields
we ran ’SAD’ searching for sources with fluxes higher than 100 µJy/beam. ’SAD’ looks
for points above the specified flux limit and merges such points into contiguous “islands”.
Then it fits components within these “islands”. For our astrometric tests, we run ’SAD’
rejecting components within an island with both peak and integrated flux values lower
than 100 µJy/beam which corresponds to ∼ 7σ in a single field. On average ∼ 150 sources
were found per pointing. (In Sec. 2.6 we describe how ’SAD’ was run on the mosaic.)
After source extraction we only matched positions of objects which have a deconvolved
major axis of < 3′′ FWHM and are within a radius of ∼ 17′ from the pointing center
2.5 Tests
39
(which corresponds to our primary beam cut of 0.4) in the specific field. We analyzed the
offsets in right ascension (∆ RA) and declination (∆ DEC) in the central 0.87◦ where
the rms noise is basically uniform. The results are shown in Fig. 2.9. The offsets in
∆ RA and ∆ DEC are (−10 ± 127) mas and (−12 ± 131) mas, respectively. To search for
possible systematic effects, we analyzed the ∆RA and ∆DEC offsets in different parts of
the central 0.87◦ area. As seen from Fig. 2.10, there are no significant systematic effects
in our relative astrometry as a function of position within the COSMOS field.
Figure 2.9 The left panel compares the offset in RA (∆RA) with the offset in DEC (∆DEC) when
positions in single pointings are matched to positions in the combined mosaic (see text for details). The
reference position is the one extracted from the mosaic. The right panel shows the distributions of ∆RA
(thick dashed line) and ∆DEC (thin solid line). The total number of sources, mean and standard deviation
of the offsets are indicated.
To get a deeper insight into our astrometry we cross-correlated the COSMOS mosaic
source catalog with the VLA FIRST survey catalog (Becker et al. 1995). To minimize the
number of spurious matches, we used a search box size of 2′′ on a side. Only sources with
a major axis < 3′′ and COSMOS to FIRST fluxes comparable within 20%, i.e. 0.8 <
int
Sint
COSMOS /SFIRST < 1.2, were compared. Multiple component sources and FIRST sources
with side lobe flags (f lag = 1) were excluded. Our final sample of matched sources contains
only 28 objects. The mean offsets and the 1σ errors for ∆RA = RACOSMOS − RAFIRST and
∆DEC = DECCOSMOS − DECFIRST are (−110 ± 273) mas and (67 ± 232) mas, respectively.
Given the low number of matched sources and the FIRST survey’s astrometric accuracy of
500 mas (or more) for individual sources (White et al. 1997), we conclude that the inferred
positional offsets are within the source extraction errors of both surveys.
In addition, we compared the positions of radio sources extracted from the VLACOSMOS Pilot and the Large project. However, we consider this not a completely independent test, as the same phase calibrator was used for both projects. We find a median
40
2. VLA-COSMOS Large Project
Figure 2.10 Distributions of ∆RA (thick dashed line) and ∆DEC (thin solid line) for different parts in
the inner 0.87◦ area. The positions of the four panels in the diagram correspond exactly to the analyzed
area. The mean and standard deviation of the offsets and the total number of sources are indicated in
each panel. For clarity the pointing pattern of the VLA-COSMOS is shown in the background (dotted
circles).
offset of -50 mas and 90 mas in ∆RA and ∆DEC, respectively, while the rms scatter is
161 mas and 189 mas for the first and latter. The rms scatter is slightly higher than
the above derived accuracy of our relative astrometry (∼130 mas) using only the Large
project. However, this is expected as the rms and the beam size of the Pilot project is
larger: 25 µJy/beam vs. 10 µJy/beam and 1.9′′ × 1.6′′ vs. 1.5′′ × 1.4′′ . The derived astrometric differences between the Pilot and the Large projects are well within our errors (see
Sec. 2.4.2 for data reduction difference between both projects). Hence, we conclude that
our relative astrometric accuracy for the VLA-COSMOS Large project is ∼130 mas and
discard this higher rms scatter found from the comparison to the Pilot data.
Based on arguments presented above, we conclude that the overall astrometric errors
of our derived source positions are dominated by the uncertainty in the position extraction
2.6 The VLA-COSMOS catalog
41
(due to our beam size) of ∼ 130 mas. Our absolute astrometric accuracy is likely to be
better than 55 mas.
2.6
2.6.1
The VLA-COSMOS catalog
Source extraction
In order to select a sample of radio components from the largest imaged area above a
given threshold, defined in terms of the local signal to noise ratio, we adopted the following
approach. First the software package SExtractor was used to estimate the local background
in each mesh of a grid covering the whole surveyed area (see Bertin & Arnouts 1996, for
a general description of SExtractor). Different noise maps with mesh sizes ranging from
25 to 100 pixels were produced and examined. The fractional difference between the rms
measured in the SExtractor noise maps and the rms directly measured on the real map is
very small (∼2%) over the whole map (see Fig. 2.11). In the end, we adopted a mesh size
of 50 pixels corresponding to 17.5′′ which was found to be the best compromise between
closely sampling the variations in rms and avoiding contamination by larger radio sources.
The rms values range from about 9 µJy/beam in the inner regions to about 20 µJy/beam at
the edges of the mosaic with values as high as 30 − 40 µJy/beam around the few relatively
strong sources (see Fig. 2.12). The mean rms in the inner 1◦ is 10.5 µJy/beam, the mean
rms over the 2◦ area is 15.0 µJy/beam. The cumulative area as a function of rms is shown
in Fig. 2.13.
Figure 2.11 Fractional difference
between the directly measured rms
value in a 100 × 100 pixel box and
the corresponding value of the SExtractor noise map as a function of
the radial distance for three different noise maps with mesh sizes of 25,
50 and 75 pixels, respectively. The
x-positions have been shifted by 0.5′
for clarity.
As a next step, the AIPS task ’SAD’ was used to obtain a catalog of candidate components. ’SAD’ attempts to find all the components whose peaks are brighter than a given
flux level. In order to detect radio components down to the 30 µJy/beam level ’SAD’ was
run several times with different search levels (with a decreasing flux limit) using the resulting residual image each time. We recovered all the radio components with a peak flux
Speak > 30 µJy/beam (corresponding to roughly 3σ in the higher sensitivity regions). For
42
2. VLA-COSMOS Large Project
Figure 2.12 Sensitivity map of the area covered by the VLA Large Project derived using SExtractor with
a mesh size of 50 pixel. The rms is fairly uniform except for areas around strong radio sources. Lighter
shades indicate lower rms noise values. The contours correspond to rms levels of 10, 15, 20, 25, 30, and
40 µJy/beam. The dashed box outlines the area which was searched for radio components.
each component ’SAD’ provides peak flux, total flux, position and size estimated using a
Gaussian fit.
However, for faint components the Gaussian fit may be unreliable and a better estimate
of the peak flux (crucial for the selection based on S/N) can be obtained with a nonparametric second-degree interpolation using the AIPS task ’MAXFIT’. We ran ’MAXFIT’
on all the components found by ’SAD’ and selected only those components for which the
peak flux density found by ’MAXFIT’ was greater or equal to 4.5 times the local rms
as derived from the noise map. The (non-parametric) peak position and flux density as
2.6 The VLA-COSMOS catalog
43
Figure 2.13 Plot of the rms noise level vs. cumulative as well as fractional area covered. The full area
covered is 2◦ and is indicated in Fig. 2.12.
determined by ’MAXFIT’ were kept, as the so derived values should be less affected by
assumptions on the real brightness distribution.
Finally, we visually inspected the S/N mosaic image (Fig. 2.14) for components that
could have been missed by ’SAD’. The most likely reason for missing sources is that ’SAD’
only recovers components that can be fitted by a Gaussian fulfilling certain parameters.
Thus, if the fit for a potential component fails, this component is rejected from the catalogue
provided by ’SAD’. Therefore, the AIPS tasks ’JMFIT’ and ’MAXFIT’ were run on these
potential components to derive their properties.
In order to exclude 1-pixel wide noise peaks above the detection threshold (4.5σ), more
scrutiny was used for the 294 components fitted with both sizes smaller than the CLEAN
beam. Only those components (171) for which JMFIT was able to estimate an upper limit
to the source size greater than the CLEAN beam were kept while the remaining (123)
were identified as noise spikes and excluded from the catalogue. As a result of the whole
procedure a total of 3823 components have been selected (3204 from ’SAD’+’MAXFIT’
and 619 from the S/N image). A more complete analysis on the completeness and possible
biases affecting the catalogue will be described in a future paper along with the number
counts (Bondi et al., in prep).
2.6.2
Description of the catalog
Some of the components clearly belong to a single radio source (e.g. jets and lobes of an
extended radio galaxy), in other more complex cases we have also used the optical groundand space-based images to discriminate between different components of the same radio
source or separate radio sources. The final catalog (see Tab. 2.3; see below) lists 3643 radio
44
2. VLA-COSMOS Large Project
Figure 2.14 Map of the S/N of the VLA-COSMOS Large Project as constructed using the SExtractor
sensitivity map (Fig. 2.12). Lighter shades indicate lower S/N values. The dashed box shows the area in
which radio sources were identified (see also text).
sources of which 80 are multiple, i.e. better described by more than a single component.
These sources are identified by the flag ’mult=1’ (Tab. 2.3). For these sources, the listed
center is either the one of the radio core or the optical counterpart when either of these
could be reasonably identified or the luminosity weighted mean position. In addition, we
visually inspected weak (≤ 6σ) sources close to bright sources with significant sidelobes.
A total of 72 sources potentially lying on sidelobe spikes are flagged with ’slob=1’.
2.6 The VLA-COSMOS catalog
45
In Fig. 2.15 we plot the ratio of the total integrated flux density Stotal and the peak
flux density Speak as functions of the signal to noise ratio S/N (Speak /rms) for all the 3643
sources in the catalog. To select the resolved sources, we determined the lower envelope of
the points in Fig. 2.15 which contains 99% of the sources with Stotal < Speak , and mirrored
it above the Stotal /Speak = 1 line (upper envelope in Fig. 2.15). We have considered
the 1601 (44%) sources laying above the upper envelope resolved. The envelope can be
described by the equation
Stotal /Speak = 1 + [100/(Speak /rms)3 ]
The resolved sources are flagged in the catalog by ’res=1’. For the unresolved sources
the total flux density is set equal to the peak brightness and the angular size is undetermined.
Figure 2.15 Ratio of the total
flux ST to the peak flux SP as
a function of the signal-to-noise
ratio of the peak flux and the local rms. The solid line shows the
upper and lower envelopes of the
flux ratio distribution containing the sources considered unresolved (see text). Open symbols show sources considered resolved.
We calculated the uncertainties in the peak flux density Speak and integrated flux Stotal
using the equations given by Condon (1997) as outlined in e.g. Hopkins et al. (2003);
Schinnerer et al. (2004). For the positional uncertainties we used the equations reported
in (Bondi et al. 2003, their equations 4 and 5), using 130 mas as the calibration error in
right ascension and declination (see also Condon et al. 1998, their equation 27).
For each of the 80 sources fitted with multiple components (see Fig. 2.16) we list in the
multiple source catalog (see Tab. 2.4) (i) an entry for each of the components identified
with a trailing letter (A, B, C, . . . ) in the source name (from Tab. 2.3), and (ii) an entry
for the whole source as it is listed in the source table (Tab. 2.3). In these cases the total
flux was calculated using the task ’TVSTAT’, which allows the integration of map values
over irregular areas, and the sizes are the largest angular sizes. For these sources the peak
flux (at the listed position) is undetermined and therefore set to a value of ’-99.999’.
For each source we list the source name as well as its derived properties and their uncertainties. All 3643 radio sources are listed in right ascension order in Tab. 2.3 with the
46
2. VLA-COSMOS Large Project
following columns5 :
Column(1): Source name
Column(2): Right ascension (J2000.0)
Column(3): Declination (J2000.0)
Column(4): rms uncertainty in right ascension
Column(5): rms uncertainty in declination
Column(3): Peak flux density and its rms uncertainty
Column(4): Integrated flux density and its rms uncertainty
Column(5): rms measured in the SExtractor noise map
Column(9): Deconvolved source size – major axis θM,dec
Column(10): Deconvolved source size – minor axis θm,dec
Column(11): Deconvolved source – position angle PAdec (counterclockwise from North)
Column(12): Flag for resolved (1) and unresolved (0) sources
Column(13): Flag for source with multiple (1) or single (0) components
Column(14): Flag for potentially spurious source due to sidelobe (1), otherwise (0)
The individual components contributing to our multi-component sources are listed in
Tab. 2.4. The columns are the same as for Tab. 2.3. The (cumulative) peak and integrated
flux distribution of the sources in VLA-COSMOS large project are shown in Fig. 2.17.
5
Due to bandwidth smearing effects the peak flux and, hence, the integrated flux for unresolved sources
can be underestimated by up to (10-15)%. An analysis of this will be presented in Bondi et al. (in prep.).
Table 2.3 1.4 GHz Source Catalog of the VLA-COSMOS Large Project (abridged)
Name
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
COSMOSVLA
a
J095738.80+024203.2
J095738.97+021630.3
J095739.10+021503.1
J095739.23+024539.0
J095739.39+023655.5
J095739.44+021850.9
J095739.71+023103.5
J095739.81+013653.4
J095740.60+020145.1
J095740.99+024921.1
J095741.11+015122.6
J095741.25+024346.2
J095741.34+020346.1
J095741.52+023841.2
J095741.74+025004.0
J095741.89+020426.4
J095742.30+020426.1
J095742.61+022827.8
J095742.71+024540.4
J095743.04+015650.8
J095743.23+013851.0
J095743.40+015620.7
J095743.73+014132.5
J095743.87+023038.5
R.A.
(J2000.0)
09 57 38.800
09 57 38.972
09 57 39.097
09 57 39.229
09 57 39.390
09 57 39.441
09 57 39.712
09 57 39.814
09 57 40.602
09 57 40.986
09 57 41.107
09 57 41.250
09 57 41.338
09 57 41.525
09 57 41.737
09 57 41.895
09 57 42.305
09 57 42.612
09 57 42.711
09 57 43.044
09 57 43.228
09 57 43.400
09 57 43.729
09 57 43.872
Dec.
(J2000.0)
+02 42 03.19
+02 16 30.32
+02 15 03.05
+02 45 39.02
+02 36 55.47
+02 18 50.87
+02 31 03.53
+01 36 53.40
+02 01 45.13
+02 49 21.13
+01 51 22.58
+02 43 46.20
+02 03 46.13
+02 38 41.21
+02 50 03.96
+02 04 26.42
+02 04 26.07
+02 28 27.81
+02 45 40.41
+01 56 50.82
+01 38 51.05
+01 56 20.72
+01 41 32.47
+02 30 38.52
σR.A.
[′′ ]
0.19
0.19
0.19
0.19
0.19
0.18
0.13
0.17
0.20
0.18
0.13
0.19
0.22
0.18
0.19
0.17
0.13
0.20
0.17
0.15
0.17
0.34
0.18
0.15
σDec.
[′′ ]
0.19
0.19
0.19
0.19
0.19
0.18
0.13
0.17
0.19
0.18
0.14
0.19
0.22
0.17
0.19
0.17
0.13
0.19
0.17
0.15
0.17
0.19
0.17
0.14
Speak
[mJy/beam]
0.112 ± 0.024
0.112 ± 0.025
0.119 ± 0.024
0.126 ± 0.028
0.111 ± 0.024
0.133 ± 0.027
0.124 ± 0.027
0.156 ± 0.030
0.225 ± 0.035
0.154 ± 0.034
-99.990 ±-99.990
0.123 ± 0.025
0.152 ± 0.031
0.116 ± 0.023
0.160 ± 0.034
0.181 ± 0.031
11.371 ± 0.031
0.133 ± 0.029
0.134 ± 0.026
0.425 ± 0.030
0.139 ± 0.025
0.183 ± 0.030
0.121 ± 0.022
0.412 ± 0.026
Stotal
[mJy]
0.112 ± 0.024
0.112 ± 0.025
0.129 ± 0.024
0.126 ± 0.028
0.111 ± 0.024
0.133 ± 0.027
0.124 ± 0.027
0.156 ± 0.030
0.377 ± 0.105
0.154 ± 0.034
45.620 ± -99.990
0.123 ± 0.025
0.152 ± 0.031
0.116 ± 0.023
0.160 ± 0.034
0.181 ± 0.031
20.492 ± 0.228
0.133 ± 0.029
0.134 ± 0.026
0.747 ± 0.098
0.139 ± 0.025
0.289 ± 0.102
0.121 ± 0.022
0.727 ± 0.084
rms
[mJy/beam]
0.024
0.025
0.024
0.028
0.024
0.027
0.027
0.030
0.035
0.034
0.024
0.025
0.031
0.023
0.034
0.031
0.031
0.029
0.026
0.030
0.025
0.030
0.022
0.026
θM,dec
[′′ ]
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.52
0.00
53.00
0.00
0.00
0.00
0.00
0.00
1.88
0.00
0.00
2.11
0.00
2.64
0.00
1.98
θm,dec
[′′ ]
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
9.00
0.00
0.00
0.00
0.00
0.00
0.35
0.00
0.00
0.20
0.00
0.30
0.00
0.33
PAdec
[o ]
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
55.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
57.1
0.0
0.0
129.1
0.0
73.2
0.0
57.7
resa
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
1
0
0
1
0
1
0
1
Flags
slobb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Flag if source is – according to Fig. 2.15 – resolved (1) or unresolved (0) b Flag if source is potentially spurious due to sidelobe bump (1) or not (0) c Flag if source consists of multiple
components (1) or a single component (0) Catalog of radio sources at 1.4 GHz detected in the COSMOS field with a S/N≥4.5 in the VLA-COSMOS large project data (see Sec. 2.6). Radio
sources with multiple Gaussian fits are flagged (’mult=1’), their multiple components are listed separately in Tab. 2.4. The table is available in its entirety via the link to a machine-readable
version above and/or via the COSMOS archive at IPAC/IRSAa . A portion is shown here for guidance regarding its form and content.
a
http://www.irsa.ipac.edu/data/COSMOS/tables/
multc
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Table 2.4 Multi-components of sources in the VLA-COSMOS catalog (abridged)
Name
COSMOSVLA J095741.11+015122.6A
COSMOSVLA J095741.11+015122.6B
COSMOSVLA J095741.11+015122.6C
COSMOSVLA J095741.11+015122.6D
COSMOSVLA J095741.11+015122.6E
COSMOSVLA J095741.11+015122.6F
COSMOSVLA J095741.11+015122.6
COSMOSVLA J095755.84+015804.2A
COSMOSVLA J095755.84+015804.2B
COSMOSVLA J095755.84+015804.2C
COSMOSVLA J095755.84+015804.2
COSMOSVLA J095756.45+025155.6A
COSMOSVLA J095756.45+025155.6B
COSMOSVLA J095756.45+025155.6
COSMOSVLA J095800.80+015857.2A
COSMOSVLA J095800.80+015857.2B
COSMOSVLA J095800.80+015857.2
COSMOSVLA J095815.51+014923.7A
COSMOSVLA J095815.51+014923.7B
COSMOSVLA J095815.51+014923.7
a
R.A.
(J2000.0)
09 57 39.708
09 57 39.858
09 57 40.100
09 57 41.107
09 57 41.686
09 57 42.166
09 57 41.107
09 57 55.792
09 57 55.847
09 57 55.898
09 57 55.840
09 57 56.418
09 57 56.484
09 57 56.451
09 58 00.619
09 58 00.798
09 58 00.798
09 58 15.502
09 58 15.520
09 58 15.509
Dec.
(J2000.0)
+01 51 41.59
+01 51 43.67
+01 51 38.36
+01 51 22.58
+01 51 11.30
+01 51 03.17
+01 51 22.58
+01 58 05.76
+01 58 01.95
+01 58 04.18
+01 58 04.24
+02 51 56.26
+02 51 54.91
+02 51 55.59
+01 58 53.03
+01 58 57.15
+01 58 57.15
+01 49 24.61
+01 49 22.18
+01 49 23.75
σR.A.
[′′ ]
0.13
0.13
0.24
0.14
0.22
0.13
0.13
0.13
0.17
0.18
0.13
0.34
0.19
0.20
0.18
0.13
0.13
0.16
0.20
0.15
σDec.
[′′ ]
0.13
0.13
0.24
0.13
0.21
0.13
0.14
0.14
0.14
0.23
0.16
0.25
0.18
0.43
0.17
0.13
0.13
0.23
0.34
0.24
Speak
[mJy/beam]
1.971 ± 0.026
1.463 ± 0.026
0.227 ± 0.026
0.497 ± 0.025
0.314 ± 0.024
2.227 ± 0.024
-99.990 ±-99.990
0.791 ± 0.022
0.501 ± 0.022
0.531 ± 0.022
-99.990 ±-99.990
0.170 ± 0.031
0.167 ± 0.031
-99.990 ±-99.990
0.348 ± 0.019
7.204 ± 0.019
-99.990 ±-99.990
0.145 ± 0.014
0.080 ± 0.014
-99.990 ±-99.990
Stotal
[mJy]
8.612 ± 0.202
4.289 ± 0.151
10.694 ± 1.260
0.754 ± 0.069
8.037 ± 0.820
12.488 ± 0.229
45.620 ±-99.990
3.370 ± 0.155
1.657 ± 0.151
1.714 ± 0.214
6.450 ±-99.990
0.302 ± 0.111
0.167 ± 0.031
0.300 ±-99.990
3.684 ± 0.303
16.624 ± 0.183
18.875 ±-99.990
0.496 ± 0.083
0.080 ± 0.014
0.500 ±-99.990
rms
[mJy/beam]
0.026
0.026
0.026
0.025
0.024
0.024
0.024
0.022
0.022
0.022
0.022
0.031
0.031
0.031
0.019
0.019
0.019
0.014
0.014
0.014
θM
[′′ ]
3.36
2.64
13.17
1.62
11.14
3.73
53.00
3.51
3.85
5.89
21.96
2.84
0.00
3.75
7.16
1.89
10.00
3.49
0.00
3.75
θm
[′′ ]
2.56
1.85
7.03
0.50
5.54
2.49
9.00
2.07
1.86
2.06
6.86
0.54
0.00
1.43
2.59
1.58
3.00
1.62
0.00
1.43
PA
[o ]
124.8
84.7
133.9
114.2
130.0
134.7
0.0
150.5
108.6
31.4
0.0
122.4
0.0
0.0
51.5
156.5
0.0
5.7
0.0
0.0
resa
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
1
1
0
1
Flags
slobb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Flag if component is – according to Fig. 2.15 – resolved (1) or unresolved (0) b Flag if component is potentially spurious due to sidelobe bump (1) or not (0) c Flag if source consists of multiple
components (1) or one of its single components (0) List of individual components that made up the 80 radio sources that were fitted by multiple Gaussian. These multi-component sources are
flagged in Tab. 2.3 by a ’mult=1’. The table is available in its entirety via the link to a machine-readable version above and/or via the COSMOS archive at IPAC/IRSA. A portion is shown
here for guidance regarding its form and content.
multc
0
0
0
0
0
0
1
0
0
0
1
0
0
1
0
0
1
0
0
1
2.6 The VLA-COSMOS catalog
49
50
2. VLA-COSMOS Large Project
2.6 The VLA-COSMOS catalog
51
52
2. VLA-COSMOS Large Project
2.6 The VLA-COSMOS catalog
53
54
2. VLA-COSMOS Large Project
2.6 The VLA-COSMOS catalog
55
Figure 2.16 Radio sources fitted by multiple Gaussian components and identified as a radio group (see
Tab. 2.4). The source name is given at the top of the individual panels. The grey-scale is from -4σ to 10σ
of the local rms (Tab. 2.3). The contours start at 4σ in steps of 2n × σ with n = 2, 3, 4, 5, . . .. (The local
rms is listed in Tab. 2.3.) The beam is shown for reference in the bottom left corner.
2.6.3
Comparison to other surveys
We compared the catalog of the VLA-COSMOS large project with the catalogs of the
NVSS, FIRST and VLA-COSMOS pilot project. All three surveys were also conducted
56
2. VLA-COSMOS Large Project
Figure 2.17 Cumulative number distribution of the VLA-COSMOS sources as a function of peak (left)
and integrated (right) flux density. The shaded area corresponds to sources that are resolved (see text).
at 1.4 GHz, however the NVSS and FIRST surveys used the D- and B-array, respectively
(Condon et al. 1998; White et al. 1997).
Within the area searched for the VLA-COSMOS large project, the NVSS and FIRST
catalogs list 119 and 184 sources, respectively. About 10% of the sources in these catalogs have no counterpart in the VLA-COSMOS survey nor in the other survey, i.e. they
are unique to the catalogs of the NVSS or FIRST survey. Given the sensitivity of the
VLA-COSMOS survey this suggests that these sources are likely false detections6 as it
seems unlikely that all of them are highly variable sources. We cross-correlated the NVSS
and FIRST catalogs with the catalog of the VLA-COSMOS large project using a search
radius of 5′′ and 1′′ , respectively. Figure 2.18 compares the integrated fluxes derived for
the individual sources. The agreement between the values of the VLA-COSMOS and the
NVSS/FIRST survey is fairly good, except for a number of NVSS sources where our observations have probably resolved out a large extended flux component. (Note that some
of the VLA-COSMOS multi-component sources consist of more than one FIRST source,
explaining most of the large discrepancies in Fig. 2.18.)
For 30 sources from the VLA-COSMOS pilot project no counterpart is present in our
catalog of the large project. Given that the sensitivity of the large project is at least a
factor of 2.5 better, these sources are likely false detections. Thus the fraction of false
detections is about 10 % in the pilot catalog. The signal-to-noise ratio S/N of the sources
is below 4.3σ of the fitted peak flux and its calculated error. (This roughly corresponds
to a S/N of 5.5 and lower.) This is a factor of 2 more than expected from the algorithm
used which was set to a false detection rate of 5 % (Schinnerer et al. 2004). As all of the
false detection are lying in areas with a large gradient in the background (i.e. overlap areas
of the individual pointings at the edge of the field), this strongly suggests that the local
rms was underestimated in these areas and that the used mesh size of 47′′ was too large
6
The FIRST survey notes on their web-site (http://sundog.stsci.edu/) that sidelobe flagging near
the equator is not as reliable as for the northern part of the survey.
2.6 The VLA-COSMOS catalog
57
Figure 2.18 Comparison of the derived integrated flux in the VLA-COSMOS large project IVLA−COSMOS
and the NVSS and FIRST surveys Iexternal. The solid diagonal line represents a flux ratio of unity, while
the dashed lines show the ±20% lines. The vertical lines denote the (5σ) detection limit of the NVSS and
FIRST surveys (Condon et al. 1998; White et al. 1997). The counterparts to VLA-COSMOS sources lie
within radii of 5′′ and 1′′ for the NVSS and FIRST survey, respectively. The large discrepancies in the
derived integrated flux for several NVSS sources is likely due to the large difference in resolution (NVSS:
45′′ FWHM vs. VLA-COSMOS:∼ 1.5′′ FWHM), while the discrepancies in the integrated flux for the
FIRST sources are mainly due to the fact that these are part of multi-component VLA-COSMOS sources.
in these areas. (For the large project a mesh size of 17.5′′ is used, see Sec. 2.6.1.) We
also compared the measured peak and integrated fluxes of both VLA-COSMOS projects.
For sources in the pilot project with significant detection (S/δS > 4.5) the measured peak
(integrated) flux agrees within 20% for about 66% (50%) of the sources. However, the
flux measurements agree within the quoted errors for most sources. The agreement in the
integrated flux (also with the error) is lower for very bright sources (≥ 1mJy). This is very
likely due to the fact that the large project data is more sensitive to low level extended
structure due to its higher sensitivity as well as the shorter baselines from the C array
observations.
58
2.7
2. VLA-COSMOS Large Project
The VLA-COSMOS survey in the COSMOS context
All data obtained by the COSMOS collaboration will be made available to the public
via the COSMOS archive at IPAC/IRSA. The final reduced and calibrated data of the
VLA-COSMOS pilot project can already be found there. For the large project of the
VLA-COSMOS survey, the final reduced and calibrated A+C 1.4 GHz image covering the
entire COSMOS field as well as the source catalogs described here are available as well.
One unique aspect of the overall COSMOS survey is the large ongoing spectroscopic
effort (Lilly et al. 2007; Impey et al. 2007). Given the fortunate timing of observations,
source lists from the VLA-COSMOS survey do provide target lists for these spectroscopic
surveys. The Magellan-COSMOS survey (Impey et al. 2007) is targeting potential AGN
candidates (from the X-ray and radio surveys) down to an iAB = 23.0 mag. Most VLACOSMOS sources with optical counterparts fulfilling this criteria are being observed by
this survey. At the time of writing, for over 200 radio sources a spectral classification
has already been obtained, with an expected total of 500 sources (Trump et al. 2007). In
addition, the zCOSMOS survey (Lilly et al. 2007) is including VLA-COSMOS sources with
optical counterparts down to BAB = 25.0 mag in their target lists as compulsory targets.
Therefore, we expect that over 1,500 VLA-COSMOS sources will have optical spectra,
once the spectroscopic surveys are completed. These spectra do not only provide very
accurate redshifts, but also allow a better classification of the nature of the host galaxy
(AGN vs. star formation). Thus the VLA-COSMOS survey will provide the largest sample
of radio sources with spectral information in the redshift range z > 0.3. For comparison, in
the local universe, the largest samples of radio sources with optical spectra are the combined
2dFGRS+NVSS with 757 sources (Sadler et al. 2002a) and the combined SDSS+FIRST
with 5454 entries (Ivezić et al. 2002). Together with the information available from the
other wavelengths covering the X-ray to mm regime, COSMOS will provide a unique
dataset for the study of the faint radio source population.
Chapter 3
A new method to separate star
forming from AGN galaxies at
intermediate redshift:
The submillijansky radio population
in the VLA-COSMOS survey
In the previous Chapter the VLA-COSMOS Large Project which yielded to date the largest
sample of faint radio sources was presented. In this Chapter a new method is developed
that efficiently discriminates between the two main extragalactic radio populations – AGN
and star forming galaxies, and it is applied to the VLA-COSMOS radio sources. Further,
this new method, combined with the full COSMOS panchromatic (X-ray to radio) data,
is utilized to study the composition of the submillijansky radio sources. At the time of
writing this work has been submitted to ApJ as:
V. Smolčić, E. Schinnerer, M. Scodeggio, P. Franzetti et al., A new method to separate star forming from AGN galaxies at intermediate redshift: The submillijansky radio
population in the VLA-COSMOS survey
Abstract
We explore the properties of the submillijansky radio population at 20 cm by applying a
newly developed color-based method, in conjunction with complementary multi-wavelength
data, to separate star forming (SF) from AGN galaxies at intermediate redshifts (z . 1.3).
The main feature of our method is an efficient discrimination of SF galaxies and AGN
(i.e. LINERs, Seyferts, type 2 QSOs, absorption-line AGN) using only photometric – optical rest-frame – data which are available for large numbers of galaxies even at high and
intermediate redshifts. The method is based on the locally found tight correlation between rest-frame colors of emission-line galaxies and their position in the emission-line
based Baldwin, Phillips & Terlevich (BPT) diagram, which is commonly used for spec-
60
3. VLA-COSMOS faint radio population
troscopically separating low-luminosity AGN from SF galaxies. We extensively test our
classification method at intermediate redshifts, and show that it agrees remarkably well
with other independent classification schemes which rely on mid-infrared colors and optical
spectroscopy. Based on a large sample of local galaxies, drawn from the SDSS and NVSS
sky surveys, we infer the completeness and contamination of the samples of SF and AGN
galaxies, selected using only their rest-frame color properties. We show that in a radio
selected optical sample our method selects ∼ 85% and ∼ 95% complete samples of SF and
AGN galaxies, respectively. Further, although optical rest-frame colors are used for the
SF/AGN separation, our separation method is shown not to be biased against dusty starburst galaxies. This classification method is calibrated and tested here on a radio selected
optical sample, however it carries the potential to be generally applied to any given sub-set
of galaxies where SF and AGN galaxies are the two dominant sub-populations.
In order to quantify the properties of the submillijansky radio population, we analyze
∼ 2, 400 radio sources, detected at 20 cm in the VLA-COSMOS survey with a signal to noise
≥ 5. About 90% of these have submillijansky fluxes. We make use of the multi-wavelength
(X-ray to radio) observations of the COSMOS field, and apply our classification method
to ∼ 1, 560 of the radio sources that have optical counterparts brighter than iAB = 26.
We separate them into five classes of objects: 1) star candidates, 2) quasi stellar objects
(QSOs), 3) active galactic nuclei (AGN), 4) star forming (SF), and 5) high redshift (z > 1.3;
high-z) galaxies. Further, for the high-z galaxies, as well as the VLA-COSMOS radio
sources with no optical counterparts brighter than iAB = 26 we utilize optical, near- and
mid-infrared observed color properties to study their nature.
Our results show that in the VLA-COSMOS radio-optical sample only 2 objects (∼
0.1%) are consistent with having stellar properties, and less than 10% are identified as
type 1 QSOs. Out of 941 galaxies at z ≤ 1.3 340 are classified as star forming, and 601 as
AGN. We show that the 476 galaxies in the radio-optical sample above z = 1.3 consist of
a mixture of SF and AGN galaxies, and that this is also the case for the 830 radio sources
without identified optical counterparts (brighter than i = 26). Further, our results yield
that SF galaxies are not the dominant population at submillijansky flux levels, as often
previously assumed, but that they make up an approximately constant fraction of 30 − 40%
in the flux range of ∼ 50 µJy to 0.7 mJy. In summary, based on the entire VLA-COSMOS
radio population at 20 cm, we find that the radio population at these fluxes is a mixture of
roughly 30 − 40% of SF and 50 − 60% of AGN galaxies, with a minor contribution (∼ 10%)
of QSOs.
3.1
Introduction
The most straight-forward information that can be derived from extra-galactic radio sky
surveys are the radio source counts, which have been extensively studied in the last three
decades (Condon 1984a; Windhorst et al. 1985a; Gruppioni et al. 1999; Seymour et al.
2004; Simpson et al. 2006). If space was Euclidian, and there was no cosmic evolution
of radio sources, then the differential source counts would follow a power law with exponent of 2.5 (see e.g. Peterson 1997). Hence, the observed slope (and the change of the
3.1 Introduction
61
slope) of the radio source counts in different flux ranges provides an insight, although quite
indirect, into the global properties of extra-galactic radio sources, and their cosmic evolution. Past studies have shown that at 1.4 GHz fluxes above ∼ 100 mJy the source counts
are dominated by ’radio-loud’ AGN with luminosities above the Fanaroff & Riley (1974;
FR) break (∼ 2 × 1025 W Hz−1 ; Willott et al. 2002). Decreasing from about 100 mJy
to 1 mJy, the source counts follow a power law (e.g. Windhorst et al. 1985a), and are
mostly made up of ’radio-loud’ objects with luminosities below the FR break (FR Class I
sources). However, the differential source counts change their slope again, i.e. they flatten
below 1 mJy, and these sub-mJy radio sources have often been interpreted as a rising new
population of objects, which does not contribute significantly at higher fluxes (e.g. Condon
1984a).
To date the exact composition of this faint radio population (hereafter ’population mix’)
is not well determined, and it is rather controversial. Windhorst et al. (1985a) suggested
that the majority of sub-mJy radio sources are faint blue galaxies, presumably undergoing significant star formation. Optical spectroscopy, obtained by Benn et al. (1993),
supported this idea, and the source counts at faint levels were successfully modeled with a
population of intermediate-redshift star forming galaxies (Seymour et al. 2004). However,
spectroscopic results by Gruppioni et al. (1999) suggested that early-type galaxies were the
dominant population at sub-mJy levels. Further, it was recently suggested and modeled
that the flattening of the source counts may be caused by ’radio-quiet’ AGN (radio-quiet
quasars and type 2 AGN), rather than star forming galaxies (Jarvis & Rawlings 2004); observations support this interpretation (Simpson et al. 2006). Based from the combination
of optical and radio morphology as an identifier for AGN and SF galaxies, Fomalont et al.
(2006) suggested that at most 40% of the sub-mJy radio sources are comprised of AGN,
while Padovani et al. (2007) indicated that this fraction may be 60 − 80% (the latter based
their SF/AGN classification on a combination of optical morphologies, X-ray luminosities,
and radio–to–optical flux ratios of their radio sources).
Two main reasons exist for such discrepant results. First, the identification fraction
of radio sources with optical counterparts, which is generally taken to be representative
of the full radio population, spans a wide range in literature (20% to 90%) depending on
the depth of the available optical data. Second, the methods that were used to separate
AGN from SF galaxies have been very heterogeneous in the past, ranging from pure radio
luminosity or morphology cuts, through observed color properties to optical spectroscopy.
The two main populations of radio sources in deep radio surveys at 1.4 GHz (20 cm) are
active galactic nuclei (AGN) and star forming (SF) galaxies (Condon 1984a; Windhorst et al.
1985a). At this frequency the radio emission predominantly arises from synchrotron emission powered either by accretion onto the central super-massive black hole (SMBH) or by
supernovae remnants (e.g. Condon 1992). It was shown that radio properties such as the
distributions of mono-chromatic luminosities of SF and AGN galaxies (Seyferts, LINERs)
are comparable and overlapping (at least locally; e.g. Sadler et al. 1999). Hence, in order
to disentangle SF and AGN galaxies in the radio regime, observations at other wavelengths
are required.
Studies of extra-galactic radio sources in the local universe (z < 0.3) have been in-
62
3. VLA-COSMOS faint radio population
vigorated due to the recent advent of panchromatic photometric and spectroscopic allsky surveys, such as e.g. NVSS (Condon et al. 1998), FIRST (Becker et al. 1995), SDSS
(York et al. 2000), IRAS (Neugebauer et al. 1984), 2dF (Colless et al. 2001) which provide
additional panchromatic photometric (e.g. Simpson et al. 2006) and/or optical-IR spectroscopic (e.g. Sadler et al. 1999; Best et al. 2005) observations. For example, the panchromatic properties of radio sources were studied to full detail (Ivezić et al. 2002; Obrić et al.
2006), as well as the environmental dependence of radio luminous AGN and SF galaxies
(Best 2004), and their luminosity function (Sadler et al. 1999; Jackson & Londish 2000;
Chan et al. 2004; Best et al. 2005). Further, radio emission as a star formation rate indicator was well calibrated using a local sample (Bell 2003) and compared to other star
formation tracers (Hopkins et al. 2003).
However, it still remains to uncover the global properties of the intermediate-redshift
(z . 1.3) radio sources. For example, the cosmic star formation history of the universe
(i.e. the global star formation rate per unit comoving volume as a function of redshift)
was not determined with a high accuracy using radio data (see e.g. Haarsma et al. 2000;
Hopkins 2004), the radio luminosity function for SF and AGN galaxies at z > 0.3 is not
known, and the exact composition of the sub-millijansky radio population is still unknown
and a matter of debate (Condon 1984a; Windhorst et al. 1985a; Gruppioni et al. 1999;
Seymour et al. 2004; Jarvis & Rawlings 2004; Simpson et al. 2006).
In this work and in a number of accompanying papers, we will focus on these properties
of radio sources using the 1.4 GHz VLA-COSMOS survey (Schinnerer et al. 2007, Chap. 2).
The main aim of the current paper is twofold. First, we develop a method based only on
multi-wavelength photometric data to efficiently separate SF from AGN galaxies in the
VLA-COSMOS 20 cm survey. Secondly, we use this classification to derive the composition
of the sub-mJy radio population. In Sec. 5.2 we describe the COSMOS multi-wavelength
data, and in Sec. 3.3 we present the cross-correlation of the sources detected at 1.4 GHz with
catalogs at other wavelengths. In Sec. 3.4 we describe our source classification methodology
and introduce our ’rest-frame color based classification method’ (see below), which we
calibrate and extensively test using a large well-characterized sample of local galaxies. We
present the classification of the VLA-COSMOS 1.4 GHz radio sources with identified optical
counterparts in Sec. 3.5, and in Sec. 3.6 we compare our classification method with other
classification schemes proposed in the literature. In Sec. 3.7 we study the ’population mix’
in the VLA-COSMOS radio survey, based on the entire sample of VLA-COSMOS radio
sources. We summarize our results in Sec. 5.6.
Throughout the paper we report magnitudes in the AB system, and assume the following cosmology: H0 = 70, ΩM = 0.3, ΩΛ = 0.7. We define the radio synchrotron spectrum
as Fν ∝ ν −α , and assume α = 0.8 if not stated otherwise. Hereafter, we refer to our
method to classify the VLA-COSMOS radio sources into five sub-types of objects (star
candidate, QSO, AGN, SF, high-z galaxy) as “classification method”, and to our method
to disentangle only the SF from AGN galaxies, based on rest-frame color properties, as
“rest-frame color based selection method”.
3.2 The multi-wavelength data set
3.2
63
The multi-wavelength data set
In this section we describe the COSMOS multi-wavelength data used for the work presented
here.
3.2.1
Radio data
The COSMOS field was observed at 1.4 GHz (20 cm) with the NRAO Very Large Array (VLA) in A- and C- configuration (VLA-COSMOS Large Project; for details see
Schinnerer et al. 2007, Chap. 2). The final map, encompassing 2◦ , has a resolution of
1.5′′ × 1.4′′ , and a mean rms of ∼ 10.5 [15] µJy/beam in the central 1 [2]◦ .
The VLA-COSMOS source catalog reports the peak and total (i.e integrated) flux
density for each object. For extended sources the total flux is derived by integrating
over the object’s size (see Schinnerer et al. 2007, Chap. 2 for details), while for unresolved
sources it is set to be equal to the peak flux. Bondi et al. (2007) have shown that bandwidth
smearing effects (i.e. chromatic aberration), combined with the pointing layout of the VLACOSMOS observations, systematically decrease the measured source’s peak flux to ∼ 80%
of its true value, while the total flux remains unaffected. Therefore, to correct for this, all
peak fluxes in the catalog need to be increased by 25%. However, such an effect further
entails a necessary re-definition of the sources in the field considered to be unresolved (cf.
Fig. 14 in Schinnerer et al. 2007, Chap. 2 and Fig. 2 in Bondi et al. 2007). Therefore,
to properly correct for bandwidth smearing effects, we have re-selected the unresolved
sources following Bondi et al. (2007), and set their total fluxes to be 1.25 times their
peak (respective integrated) fluxes. Throughout the paper we will use the integrated flux,
corrected for bandwidth smearing where needed, as the representative flux for each source.
In order to minimize the number of possible spurious radio sources (∼ 50% below 5σ),
we select only objects from the catalog that were detected at a signal to noise of > 5, and
are located outside regions contaminated by side-lobes from nearby bright sources. This
yields 2388 (out of 3643; i.e. ∼ 65%) sources, 78 of which consist of multiple components.
3.2.2
Near-ultraviolet, optical and infrared imaging data
The NUV to MIR imaging data and photometry for the COSMOS survey used here include
data taken during 2003–2006 with ground – (Subaru, KPNO, CTIO, CFHT) and space
(HST, Spitzer) – based telescopes, covering a wavelength range from 3500 Å to 8 µm,
described them in more detail below.
Ground based data
The data reduction of the COSMOS ground-based observations in 15 photometric bands
ranging from NUV to NIR, and the generation of the photometric catalog, is presented in
Capak et al. (2007) and Taniguchi et al. (2007). Here we make extensive use of the COSMOS photometric catalog. The photometric catalog was selected using the 0.6′′ resolution
64
3. VLA-COSMOS faint radio population
i+ image. However, the photometry was obtained from the PSF (point-spread function)
matched images, which degrades the resolution to ∼ 1.8′′ . The median 5σ depths in AB
magnitudes in the catalog for the u∗ , BJ , g + , VJ , r + , i+ , i∗ , z + and Ks bands1 are 26.4,
27.3, 27.0, 26.6, 26.8, 26.2, 24, 25.2 and 21.6, respectively (see also tab. 2 in Capak et al.
2007). It is noteworthy that the detection completeness of the catalog is above 87% for
objects brighter than i = 26.
Space based data
The HST/ACS observations, which covered 1.8◦ of the 2◦ COSMOS field, are described
in Scoville et al. (2007c) and Koekemoer et al. (2007). The F814W band imaging has a
resolution of 0.09′′ and a 5σ point-source sensitivity of IAB = 28.6 (see also Capak et al.
2007). The ACS source catalog, which we utilize here, was constructed by Leauthaud et al.
(2007), with special care given to the separation of point-sources from extended objects.
The Spitzer observations of the COSMOS field in all seven bands (3.6, 4.5, 5.8, 8.0, 24,
70, 160 µm) are described in Sanders et al. (2007). The 3.6 – 8 µm band catalog is available
to full depth for the entire field. The resolution in the 3.6, 4.5, 5.8, 8.0 µm bands is 1.7′′ ,
1.7′′ , 1.9′′ , and 2′′ , respectively. The catalog was generated using SExtractor on the four
IRAC channels in dual mode, with the 3.6 µm image as the detection image. The 5σ depth
for point-sources at 3.6 µm is 1 µJy, corresponding to an AB magnitude of 23.9. In this work
we also make use of the MIPS 24 µm catalog obtained from the shallow observations of the
entire COSMOS field during Cycle 2 of the S-COSMOS program (see Sanders et al. 2007
for details). The resolution and 5σ depth of the catalog are 6′′ and 0.3 mJy, respectively.
The latter corresponds to an AB magnitude of 17.7. For the purpose of this paper, we will
use only sources that were detected at 24 µm at or above the 3σ level corresponding to
their local rms.
3.2.3
X-ray data
The full 2◦ COSMOS field was observed with the XMM-Newton satellite EPIC camera for
a total net integration time of 1.4 Ms (for a description of the XMM-COSMOS survey see
Hasinger et al. 2007). The limiting flux of the XMM-COSMOS survey is 10−15 erg cm−2 s−1
and 5×10−15 erg cm−2 s−1 in the soft (0.5−2 keV) and hard (2−10 keV) bands, respectively.
The X-ray point-source detection is described in Cappelluti et al. (2007), and the optical
identifications of the X-ray sources for the first 12 observed XMM fields (over a total of
1.3◦ ) are presented by Brusa et al. (2007). For the analysis presented here we utilize the
catalog with 1865 optical counterparts of the XMM-COSMOS point sources, drawn from
the full 2◦ XMM-Newton mosaic (Brusa et al. in prep).
1
The ’+’ super-script and ’J’ sub-script designate the Subaru filters, while the ’*’ sign stands for CFHT
filters.
3.2 The multi-wavelength data set
3.2.4
65
Photometric redshifts
The COSMOS photometric redshifts (Ilbert et al. in prep) utilized here are based on an
a large amount of deep multi-color data (Taniguchi et al. 2007): 6 broad optical bands
obtained at the Subaru telescope (u+ , g + , r + , i+ , z + ) and 2 at CFHT (u∗ and i∗ ), 8 intermediate and narrow band filters from the Subaru telescope (IA427, IA464, IA505, IA574,
IA709, IA827, NB711, NB816), deep Ks-band data from the WIRCAM/CFHT camera
(McCracken et al., in prep), and 3.6µm and 4.5µm data from the SPITZER IRAC camera
(Sanders et al. 2007). The photometric redshifts are estimated via a standard χ2 fitting
procedure (Arnouts et al. 2002) using the code Le Phare2 written by S. Arnouts & O. Ilbert.
A major feature of this method is the calibration of the photometric redshifts using a spectroscopic sample of ∼ 1000 bright galaxies (IAB < 22.5) obtained as part of the zCOSMOS
survey (Lilly et al. 2007). We follow exactly the same calibration method as described in
Ilbert et al. (2006): a) a calibration of the photometric zero-points, and b) an optimization
of the template SEDs (spectral energy distributions). This calibration method allows us to
remove systematic offsets in the estimates of the photometric redshifts. A direct comparison between the photometric redshifts and the zCOSMOS
spectroscopic redshifts shows
∆z
that the photometric redshifts reach an accuracy of σ 1+z
∼ 0.014 at i < 22.5. The fraction of catastrophic failures is less than 1% at i < 22.5. Such an accuracy and robustness
can be achieved thanks to both the intermediate bands and deep NIR photometric data.
The photometric redshifts for the entire COSMOS population will be described in full detail in Ilbet et al. (in prep). The galaxies in the sample used here are radio selected, i.e.
they are not randomly drawn from the global COSMOS population. Therefore, in Fig. 3.1
we show the comparison of the photometric and spectroscopic redshifts for a sub-sample
of our VLA-COSMOS
sources with available spectroscopy (see next section). The accu
∆z
= 0.027, which is somewhat lower than the accuracy for the full sample of
racy is σ 1+z
COSMOS sources, however it is still satisfactory.
As photometric redshift codes generally take into account only galaxy SED models, the
photometric redshifts for broad line AGN are usually poorly estimated, and alternative
ways for their redshift computations have to be applied. At the time of writing, no accurately estimated photometric redshifts for broad line AGN exist for the COSMOS project.
The photometric redshifts for broad line AGN will be presented in a future publication
(Salvato et al. in prep).
3.2.5
Optical spectroscopic data
The ongoing COSMOS optical spectroscopic surveys (Trump et al. 2007; Impey et al. 2007;
Lilly et al. 2007) provide to date 657 spectra, with good redshift estimates, for objects
in the VLA-COSMOS 1.4 GHz radio sample described in Sec. 3.2.1. We augment this
spectroscopic data set with available spectroscopic information for 65 galaxies from the
SDSS DR4 “main” spectroscopic sample, 13 objects from the SDSS DR5 quasar catalog
(Schneider et al. 2005), 2 sources from the 2dF survey, as well as for 27 objects taken
2
www.lam.oamp.fr/arnouts/LE PHARE.html
66
3. VLA-COSMOS faint radio population
Figure 3.1
Comparison of
the photometric and spectroscopic redshifts for the VLACOSMOS radio sources with optical counterparts (see Sec. 3.3.1)
for which spectroscopy is available. The shown distribution
was limited to spectroscopic redshifts ≤ 1.3, consistent with the
redshift range used in this work.
Note the excellent accuracy of
the photometric redshifts.
with the MMT 6.5 m telescope, and presented by Prescott et al. (2006). Thus, a total of
764 spectra is available. However, as a number of sources were spectroscopically observed
multiple times, we have spectroscopic information for 520 unique sources in our radio
sample. Throughout the paper, we use the spectroscopic redshifts, where available. We
also use this sub-set of radio sources with observed optical spectra as a control sample to
verify the presented classification method.
3.3
VLA-COSMOS 1.4 GHz radio sources at other
wavelengths
In this section we define the ’matched’ radio source sample, a sample of radio sources
with optical counterparts cross-correlated with the panchromatic COSMOS observations,
as well as the ’remaining’ radio source sample, both of which will be used throughout the
paper. First, we restrict the full VLA-COSMOS radio source sample to objects which have
optical counterparts (Sec. 3.3.1). Then we positionally match these objects with sources
detected in the MIR (Sec. 3.3.2) and X-ray (Sec. 3.3.3) spectral ranges. In Sec. 3.3.4 we
describe the remaining radio sources that are either without identified or with identified
but flagged optical counterparts.
3.3.1
Positional matching of the COSMOS radio and
NUV/optical/NIR catalogs
The VLA-COSMOS Large Project source catalog contains 2388 radio sources detected at
a signal to noise > 5 and outside side-lobe-contaminated regions (see Sec. 3.2.1). 78 of
these consist of multiple components. For the purpose of this paper we match these radio
sources with sources that have also been detected in the optical regime, and are reported
in the COSMOS photometric catalog (Capak et al. 2007). In order to obtain a sample
with reliable radio-optical counterparts, we positionally match the radio sources only with
3.3 VLA-COSMOS 1.4 GHz radio sources at other wavelengths
67
optical sources brighter than i = 26. The reason for this is illustrated in Fig. 3.2, where
we show the distance between the radio sources and their nearest optical counterparts as
a function of the i band magnitude. As the median distance rapidly increases for i > 26
(most probably introducing a significant number of false match associations) we apply a
cut of i = 26 to the NUV-NIR photometric catalog before matching the radio and optical
catalogs.
Figure 3.2 Distance as a function of i band
magnitude for radio sources in the VLACOSMOS survey (with S/N ≥ 5) positionally matched to the closest optical counterpart
(small dots). The large dots show the median
distance for each magnitude bin (the width of
the bin is indicated by horizontal lines) and
the corresponding interquartile range (vertical lines). Note that the median distance is
significantly larger beyond i = 26, presumably introducing a significant number of false
match associations. The shaded area indicates
the allowed matching region, which is set by
the matching radius (0.5′′ ) and the magnitude
cut-off (i = 26) used to find secure optical
counterparts for the radio sources in the sample (see text for details).
Hence, to find the corresponding optical counterpart for each radio source (excluding
multi-component sources, which are separately addressed below), we search for the nearest
optical neighbor within a radial distance of 0.5′′ . The search radius was chosen in such a way
that it balances a high completeness of true matches and a low false-match contamination
rate: A cut-off of 0.5′′ essentially includes all true matches in the sample, with a false
association rate (computed from the source density in the matched catalogs) of only ∼ 4%.
The high completeness and low contamination are due to the excellent astrometric accuracy
of both the COSMOS radio and optical data. Our matching yielded 1749 radio sources
with securely identified optical counterparts. However, 252 (∼ 15%) of these are located
in masked-out regions (i.e. around bright saturated stars) in the photometric catalog.
Thus, their NUV-NIR photometry, as well as the photometric redshift computation has a
significantly reduced accuracy. We exclude these objects from our main sample.
The multi-component radio sources in the VLA-COSMOS survey consist of radio sources
which could not be fitted using a single Gaussian function (see Schinnerer et al. 2007,
Chap. 2). The radio morphologies of such sources can be fairly complex (e.g. single or
double lobed radio galaxies), and this makes it substantially more difficult to associate
such radio sources with the appropriate optical counterparts (see e.g. Ivezić et al. 2002;
Best et al. 2005). In order to avoid any biases which may be caused using an automatic
association procedure, the optical counterparts of the VLA-COSMOS multi-component
radio sources were determined visually. The 1.4 GHz catalog contains 78 multi-component
sources detected at or above 5σ, and 65 were securely associated with an optical counter-
68
3. VLA-COSMOS faint radio population
part with i < 26, however 4 are located inside masked-out areas (around bright saturated
stars) in the photometric catalog, and we therefore exclude them from the main sample.
In summary, the applied matching criteria yield 1814 (∼ 76% of the 2388 radio sources
with S/N ≥ 5) radio sources with secure optical counterparts down to i = 26, 65 of which
are multiple component sources. The most accurate NUV-NIR photometry (i.e. excluding
flagged regions around saturated objects) was obtained for 1558 (∼ 86% of 1814), 61 of
which are multiple-component radio sources. Hereafter, we refer to this latter sample
of 1558 radio sources, which make up ∼ 65% of the radio sources with S/N ≥ 5, and
that were matched to the NUV-NIR catalog, as the ’matched’ radio source sample. For
reference, the 1.4 GHz total flux distributions for the complete radio sample, the matched
radio sample, and the sub-sample with available spectroscopy is shown in the top panel in
Fig. 3.3. The distribution of the i band magnitude for the matched radio sample, as well
as the spectroscopic sub-sample, is shown in the bottom panel in Fig. 3.3. It is also worth
noting that our cross-correlation is consistent with the results of the maximum likelihood
ratio technique applied to VLA-COSMOS sources (Ciliegi et al. in prep), however our
restrictions for the masked-out regions in the photometric catalog, as well as the optical
magnitude limit, are more conservative, as the analysis presented here strongly relies on
accurate NUV to NIR photometry.
A further data set that we use in the analysis presented here is the HST/ACS pointsource information. We extract this information for each radio source in our matched
sample by positionally matching the optical counterparts of the radio sources with pointsources identified in the HST/ACS F814W source catalog (Leauthaud et al. 2007). Using
a matching radius of 0.5′′ yields 47 objects in our matched radio sample classified as point
sources based on the HST/ACS F814W images. The mean distance between the matched
objects is only (0.12 ± 0.07)′′ .
3.3.2
Radio – optical sources with IRAC and MIPS detections
We cross-correlate the matched radio source sample with the S-COSMOS – IRAC catalog
using a maximum allowed distance to the optical counterparts of our radio sources of 0.5′′ .
[Note that such a cross-correlation allows for a maximum distance between the radio and
MIR sources to be 1′′ .] Such an adopted search radius essentially selects a complete radio
– optical – MIR sample with a false match association for the MIR sources of . 1% with
the optical counterparts, and . 4% with the radio counterparts. In summary, out of 1558
radio sources in the matched radio sample, 1448 (93%) have secure MIR counterparts.
The 24 µm fluxes for all our radio sources were obtained from the COSMOS field
observations using the S-COSMOS – MIPS shallow survey with a resolution of 6′′ . Although
a relaxed search radius of 5′′ was used to find the radio – 24 µm counterparts, the median
distance is only 0.19′′ with an interquartile range of 0.16′′ . About 50% (799 out of 1558)
sources in the matched radio sample have a MIPS counterpart at 24 µm with a signal to
noise at or above 3.
3.3 VLA-COSMOS 1.4 GHz radio sources at other wavelengths
69
Figure 3.3 Top panel: The distribution of the total 1.4 GHz (20 cm) flux density for i) the complete
radio source sample (S/N ≥ 5; dark-grey shaded histogram), ii) the matched radio sample (grey-shaded
histogram) and iii) the spectroscopic sub-sample (light-grey shaded histogram). The inset shows the
cumulative distribution for the three samples computed as a function of decreasing total flux. Note that
the spectroscopic sub-sample fairly represents the faint radio population as a function of the total flux.
Bottom panel: Distribution of the i band magnitude (Subaru where available, otherwise CFHT) for sources
in the VLA-COSMOS matched radio source sample, and the spectroscopic sub-sample in the same notation
as in the top panel.
70
3.3.3
3. VLA-COSMOS faint radio population
Radio – optical sources with point-like X-ray emission
Using the maximum likelihood ratio technique Brusa et al. (2007) presented the optical
identifications of the X-ray point-sources (Cappelluti et al. 2007) detected in the XMMCOSMOS survey (Hasinger et al. 2007). Here we utilize their identifications to match the
sources in our matched radio sample with detected X-ray point sources. Out of 1558 radio
sources with optical counterparts, 179 (12% of 1558) are identified as point-sources in the
X-ray bands. 17 of these have multiple counterpart candidates as defined by Brusa et al.
(2007). In these cases, if we assume that the radio sources are physically associated with the
X-ray sources, then the radio data, which have a significantly better astrometric accuracy,
can be used to constrain more precisely the optical counterpart of this given object. A
visual inspection of the 17 sources, classified as having ambiguous identifications by Brusa
et al. (in prep), strongly suggests that their most probable optical counterparts, reported
in the X-ray – optical catalog, are real associations. Hence, we proceed in our analysis
taking all 179 X-ray detected point sources to be true counterparts of the objects in the
matched radio sample.
3.3.4
Radio sources with photometrically flagged or without optical counterparts at other wavelengths
In Sec. 3.3.1 we have defined the matched radio sample which consists of 1558 1.4 GHz
sources that have optical counterparts out to an i band magnitude of 26, and within
a radial distance of less than 0.5′′ . These sources were also required to have the most
accurate NUV-NIR photometry, i.e. counterparts within flagged regions due to saturation
and belending effects in the NUV-NIR images were excluded. Thus, 830 radio sources
remain with no identified optical counterparts within these limits, 256 (i.e. ∼ 30%) of which
have counterparts with i ≤ 26 that lie in masked-out regions. Hereafter, we will refer to
this sample of sources as the ’remaining radio source sample’. We positionally match these
sources to the IRAC catalog using a maximum allowed distance of 1′′ , and find 610 (∼ 75%)
matches. Based on Poisson statistics and the source density of the MIR sources, such a
search radius essentially includes all true matches with a false contamination rate of . 4%.
It is worth noting that more than one half of the remaining 25% of the radio objects were
independently identified as possible spurious sources, based on visual inspection, while the
other half are either located in blended regions in the IRAC images or slightly further away
than the allowed 1′′ from the position reported in the IRAC catalog (the morphology of
the IRAC sources being often extended). Thus, we consider these ∼ 75% of radio – MIR
matches representative of the entire remaining radio population. Out of the 830 sources
318 (i.e. ∼ 40%) have MIPS 24 µm detections (S/N ≥ 3), and 31 (∼ 4%) have XMM
point source counterparts (these 31 objects are a sub-sample of the 256 objects in the
flagged regions). In Sec. 3.7 we analyze the properties of these remaining sources, and
their contribution to the ’population mix’ in the VLA-COSMOS survey. The summary
of the multi-wavelength cross-correlation of the VLA-COSMOS radio sources is given in
Tab. 3.1.
3.4 Classification Methodology
71
Table 3.1 Multi-wavelength cross-correlation of VLA-COSMOS 1.4 GHz radio sources
total radio sample
matched radio sample∗
stars
QSOs
SF galaxiesi
AGN galaxiesi
high-z galaxiesii
remaining radio sample∗∗
total zspec
2388 520
1558 447
2
0
139
31
340 150
601 262
476
4
830
73
IRAC1
2058
1448
2
122
322
579
423
610
MIPS2
1117
799
2
78
280
267
172
318
XMM3
210
179
0
43
16
98
22
31
1
Detected in the Spitzer/IRAC 3.6 µm band.
Detected in the Spitzer/MIPS 24 µm band within the shallow
MIPS COSMOS survey at or above a signal to noise of 3.
3
X-ray point-sources associated with optical counterparts as described in Brusa et al. (2007)
∗
Radio source sample positionally matched to optical sources with AB
i ≤ 26 and outside masked-out regions in the NUV-NIR photometric catalog.
i
Galaxies at redshift ≤ 1.3.
ii
Galaxies at redshift > 1.3.
∗∗
Radio sources a) without optical counterparts with i ≤ 26 (∼ 70%), or b) with optical counterparts
(i ≤ 26) that lie in masked-out areas in the photometric catalog (∼ 30%).
2
3.4
Classification Methodology
Extra-galactic radio sources consist of two main populations: star forming and AGN galaxies. We further divide the AGN class into two main sub-classes: QSOs (often unresolved
in optical images, with broad emission lines in their spectra and high optical luminosity)
and objects where the AGN does not dominate the entire SED, such as Type 2 QSOs,
low-luminosity AGN (Seyfert and LINER galaxies) and absorption-line AGN (resolved in
optical images, with both broad, narrow or no emission lines in their optical spectra).
Throughout the paper, we will mostly refer to the latter sub-class only as ’AGN’.
The well-calibrated tools for disentangling SF galaxies from low-luminosity AGN (Seyfert
and LINERs) are spectroscopic diagnostic diagrams in the optical, which have been originally proposed for local galaxies by Baldwin et al. (1981). Baldwin et al. have shown that
two emission line flux ratios ([OIII 5007]/Hβ vs. [NII 6584]/Hα; hereafter BPT diagram)
are sufficient to efficiently separate galaxies dominated by star formation from those dominated by the emission caused by a central super-massive black-hole. Their method has
been later revised (Veilleux & Osterbrock 1987; Kewley et al. 2001), and expanded to other
emission line flux ratios, easily attainable for higher redshift (z . 0.7) galaxies (Rola et al.
1997). These diagnostics have been extensively used in the past for a successful separation
of SF and AGN galaxies in the local universe (Sadler et al. 1999; Kauffmann et al. 2003a;
Brinchmann et al. 2004; Obrić et al. 2006; Smolčić et al. 2006). However, the main drawback of spectroscopic observations is that they are very expensive in terms of telescope
time, especially when large numbers of faint objects need to be observed. Therefore, al-
72
3. VLA-COSMOS faint radio population
ternative methods, which are based only on photometric data, have to be invoked for the
separation of SF from AGN galaxies without the need for time-consuming spectroscopy.
Here we develop such a method (hereafter ’rest-frame color based classification method’),
which we apply in the next sections to the VLA-COSMOS galaxies in the matched radio
sample in order to identify SF and AGN galaxies. The basic idea of our rest-frame color
based classification method is outlined as follows.
Recently, based on 99,000 galaxy spectra from the Sloan Digital Sky Survey (SDSS)
“main” spectroscopic sample from the First Data Release (a flux limited sample, rPet <
17.77, over 1360◦ ; hereafter “SDSS main galaxy sample”) it was shown that the overall
NUV to NIR SED of galaxies is a one-parameter family, and further that spectral diagnostic parameters, such as line strengths, appear to be well correlated with the overall galaxy’s
SED (see Obrić et al. 2006; Smolčić et al. 2006). In particular, Smolčić et al. (2006) have
found a tight correlation between rest-frame colors of emission-line galaxies and their position in the emission-line based BPT diagram. This correlation thus provides a powerful
tool for disentangling SF from AGN galaxies using only photometric data, i.e. rest-frame
colors, and we utilize it as the key of our rest-frame color based classification method.
3.4.1
Calibration of the rest-frame color based classification
method in the local universe
The rest-frame colors P1 and P2
Smolčić et al. (2006) used the SDSS main galaxy sample to synthesize rest-frame magnitudes in the modified Strömgren photometric system (uz,vz,bz,yz encompassing the wavelength range of 3200 – 5800Å; Odell et al. 2002)3 , and study the rest-frame color properties
of these galaxies. In order to optimally quantify the distribution of galaxies in the restframe color-color space, they defined a set of principal component axes (P 1,P 2), where P 1
measures the position along the galaxy locus, and P 2 the position perpendicular to it (see
Fig. 4 in Smolčić et al. 2006). As the galaxy locus is slightly curved, the functional form
of the rest-frame colors is given separately for the blue and red ends, with a boundary at
vz − yz = 0.646. Hence, for galaxies with vz − yz ≤ 0.646 (P 1,P 2) are given as:
P1 =
0.911 (c1 − 0.646) + 0.412 (c2 − 0.261)
(3.1)
P 2 = −0.412 (c1 − 0.646) + 0.911 (c2 − 0.261),
(3.2)
and for galaxies with vz − yz > 0.646 as:
P1 =
3
0.952 (c1 − 0.646) + 0.307 (c2 − 0.261)
(3.3)
P 2 = −0.307 (c1 − 0.646) + 0.952 (c2 − 0.261),
(3.4)
The filter response curves of the modified Strömgren system are available at http://www.mpiahd.mpg.de/COSMOS/
3.4 Classification Methodology
73
where c1 = vz − yz and c2 = bz − yz. We use these equations to derive the rest-frame
colors P 1 and P 2 for the galaxies in the matched radio sample. However, for the rest-frame
color based classification method we utilize only the P 1 color, which strongly correlates
with emission line properties of AGN and SF galaxies (see Sec. 3.4.1).
The local sample
In order to obtain insight into the efficiency of the rest-frame color based classification
method, we construct a large sample of well-known low-redshift galaxies (0.01 < z < 0.3),
whose properties are assumed to present well the properties of the galaxies in the VLACOSMOS matched radio sample. The local sample was generated from the SDSS main
galaxy sample, positionally matched to sources detected in the 1.4 GHz NVSS survey. Additionally, a sub-sample of galaxies detected with IRAS was constructed (see Obrić et al.
2006 for details about the cross-correlation of the SDSS, NVSS, IRAS catalogs). The
SDSS/NVSS sample contains 6966 galaxies and the SDSS/NVSS/IRAS sample 875 galaxies with available SDSS optical spectroscopy. The computation of rest-frame colors (P 1,P 2)
for these galaxies is presented in Smolčić et al. (2006). It is noteworthy to mention that
given a) the detection limits, and b) the areal coverage of the NVSS and VLA-COSMOS
surveys, both surveys observe approximately the same populations of objects, although
over different redshift intervals (see Fig. 1 in Schinnerer et al. 2007, Chap. 2). Assuming
that evolutionary effects with redshift do not significantly alter the reliability of the identification method presented below, at least out to z = 1.3, this makes the local sample of
galaxies representative of the galaxies in the VLA-COSMOS matched radio sample.
Based on spectral line properties we separate the local sample into three classes of
objects: AGN, star-forming galaxies and composite objects, where the latter are considered
to have a comparable contribution of both star formation and AGN activity. This is done
as follows. First, galaxies with emission lines in their spectra are separated into these three
classes using their position in the BPT diagram, shown in the bottom panel in Fig. 3.4,
where we also indicate the chosen boundaries between these classes4 (see Kewley et al.
2001; Kauffmann et al. 2003c). Second, as the galaxies that have no emission lines in their
spectra cannot be star forming (see also Sec. 3.4.1 for a discussion of this point), and as
all of the objects in the SDSS/NVSS sample are observed to have 1.4 GHz emission which
arises either from AGN or star formation activity in a galaxy, we define galaxies without
emission lines in their spectra as AGN (see also Best et al. 2005 who classified these types
of objects as absorption line AGN).
The completeness and contamination of the photometrically selected samples
of star forming galaxies and AGN
In the top panel in Fig. 3.4 we show the distribution of ∼ 3, 400 SDSS/NVSS emission
line galaxies in the (P 1,P 2) rest-frame color-color diagram. The color code is determined
4
Note that the classification of AGN, SF, and composite galaxies based on the BPT diagram changed
compared to the one used in Smolčić et al. (2006).
74
3. VLA-COSMOS faint radio population
by the position of a galaxy in this plane. The bottom panel shows the BPT diagram for
the same galaxies with the colors adopted from the upper panel. For these radio luminous
galaxies, a strong correlation exists between their rest-frame optical colors and emission
line properties, in particular between P 1 and log ([NII 6584]/Hα). Such a correlation was
already shown in Smolčić et al. (2006) for the full “main” spectroscopic galaxy sample,
however here we show that it also holds for a radio-selected optical sample. We want to
stress that the SDSS/NVSS galaxies with no emission lines in their spectra have typically
red P 1 colors, with a median P 1 value of 0.46 and an interquartile range of 0.13 (see also
e.g. Fig. 12 in Smolčić et al. 2006).
Smolčić et al. (2006) argued that the P 2 color is a proxy for the galaxies’ dust content,
with higher values of P 2 corresponding to higher dust attenuation. However, they also
showed that the dynamic range of P 2 (although very narrow) could not be reproduced
entirely using stellar population synthesis models (Bruzual & Charlot 2003), which we
strongly rely on throughout this work (see Sec. 3.4.2). Hence, we will not use P 2 as a
component for our rest-frame color based classification method; we will rather base our
selection on the P 1 color allone.
In order to assess the completeness and contamination of the photometrically selected
samples of star forming galaxies and AGN only by imposing a rest-frame color cut, we
show in Fig. 3.5 the differential and cumulative distributions of P 1 for the SDSS/NVSS star
forming, composite and AGN galaxies. Here the classification is based on the BPT diagram
for the galaxies with emission line in their spectra, while all galaxies without emission lines
are included in the AGN class. These ’absorption line AGN’ constitute the major fraction
of all the AGN (∼ 80%). The top panel shows the P 1 histograms for these three types of
objects, normalized by the total number of objects. In the middle panels in Fig. 3.5 we
show the fractions for the SF, AGN and composite galaxies for the selection of SF (left
panel) and AGN (right panel) galaxies. The fractions were computed in such a way that
for each P 1 value (P 1i) the distributions were normalized to the total number of galaxies
in the sample with P 1 ≤ P 1i (for SF galaxies; left panel), or P 1 > P 1i (for AGN galaxies;
right panel). In this way, we obtain the fraction of SF, AGN and composite galaxies within
the full sample that was selected only by applying a P 1 color-cut. Further, in the bottom
panels in Fig. 3.5 we show the cumulative distributions of the SF (left panel) and AGN
(right panel) galaxies, scaled to the total number of SF and AGN galaxies, respectively, thus
showing their completeness as a function of P 1. As a compromise between completeness
and contamination of the photometrically selected samples of galaxies, we choose a colorcut of P 1=0.15 as the dividing photometric value between star forming galaxies and AGN.
Thus, selecting galaxies with P 1≤0.15, and defining them as the ’photometrically selected
sample of star forming galaxies’, leads to a sample that contains ∼ 20% AGN, ∼ 10%
composite objects, and ∼ 70% ’real’ (i.e. spectroscopically identified) SF galaxies (see
middle left panel in Fig. 3.5). The latter make-up ∼ 85% of all ’real’ SF galaxies (see
bottom left panel in Fig. 3.5). On the other hand, using the color cut as defined above to
generate the ’photometrically selected AGN galaxy sample’ (P 1>0.15) leads to a sample
that contains ∼ 5% SF galaxies, ∼ 15% composite objects, and ∼ 80% ’real’ AGN galaxies
(see middle right panel in Fig. 3.5), where the latter make-up ∼ 90% of all ’real’ AGN
3.4 Classification Methodology
75
SDSS-NVSS emission line galaxies
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
AGN
Star-forming
-1.5
-1
-0.5
0
0.5
Figure 3.4 Top panel: The distribution of ∼ 3, 400 SDSS emission line galaxies, in the redshift range of
0.01 to 0.3, drawn from the DR1 “main” spectroscopic sample, which were also detected by the 1.4 GHz
NVSS survey, in a diagram constructed with the principal rest-frame colors (P 1,P 2). Each dot corresponds
to one galaxy, and the color code is determined by the position in this plane. The bottom panel shows the
distribution of these galaxies in the (BPT) diagram (Baldwin et al. 1981). The lower dashed line separates
the regions populated by star-forming and composite galaxies (Kauffmann et al. 2003c). The upper dashed
line separates the regions populated by composite and AGN galaxies (Kewley et al. 2001). The dots are
colored according to their P 1 and P 2 values given in the top panel. Note the strong correlation between
the rest-frame colors and the emission line flux ratios (see text for details).
76
3. VLA-COSMOS faint radio population
galaxies (see bottom right panel in Fig. 3.5). We will use these estimates in the following
analysis to statistically correct the photometrically selected SF and AGN samples in the
VLA-COSMOS survey.
Is the rest-frame color based selection method biased against dusty starburst
galaxies?
One of the main advantages of radio observations is that the intrinsic physical properties that drive the radio emission can be derived without any need for dust-extinction
corrections (as radio emission passes freely through dust). In particular, radio observations provide a dust-unbiased view of star-formation (see Condon 1992 for a review; see
also Haarsma et al. 2000). Hence, it is important to address whether our rest-frame color
selection technique misses out dusty starburst galaxies. In order to do this we study a
sub-sample of SDSS/NVSS galaxies that were also detected with the IRAS satellite at IR
wavelengths.
As shown in Sec. 3.4.1, our rest-frame color cut of P 1≤0.15 selects ∼ 85% of all the
spectroscopically identified SF galaxies. In the following we address the composition of
the ’missed’ ∼ 15% of the SF galaxies, that are missed by the imposed rest-frame color
criterion, in order to show that our selection does not introduce any biases, for example
against the most luminous IR galaxies which would imply that the galaxies with the highest star-formation rates would not be selected in the SF sample. About 30% of the SF
galaxies with P 1>0.15 have IRAS detections, which is consistent with the fraction of IRAS
detections in the SF sample with P 1≤0.15. This already suggests that the composition of
the missed SF galaxies is not significantly different from the composition of the selected
SF galaxies. Further, the fractions of luminous and ultra-luminous IR galaxies (see below)
in the spectroscopically classified SF samples with P 1≤0.15 and P 1>0.15 appear to be
consistent with each other. Thus, the rest-frame color based classification method does
not introduce a bias against dusty starburst galaxies.
However, we further investigate this using the 875 galaxies detected by the SDSS,
NVSS, and IRAS sky surveys. The BPT diagram for ∼ 830 SDSS/NVSS/IRAS emission
line galaxies is shown in Fig. 3.6, color-coded using the (P 1,P 2) plane. It is noteworthy
that in the entire SDSS/NVSS/IRAS sample only 48 objects (i.e. ∼ 5%) were identified
as absorption line AGN, i.e. having no emission lines in their optical spectra. It is possible that very high dust obscuration may suppress the detection of emission lines in the
optical spectrum. In order to investigate this, we performed a visual inspection of the 48
color-composite images of these objects obtained by SDSS and searched for signatures of
HII regions. This analysis suggests that at the most 30% of these galaxies may possibly be
undergoing star formation (e.g. possible galaxy merger, or extended morphology). Therefore, only a negligible fraction of less than 1.5% of the SDSS/NVSS/IRAS galaxies may be
so heavily dust-obscured that no emission lines would be detected in their optical spectra.
In the top panel of Fig. 3.7 we show the differential distribution of P 1 for the complete
sample of 875 SDSS/NVSS/IRAS galaxies separated into AGN, star-forming and composite
3.4 Classification Methodology
Figure 3.5
77
Differential and cumulative distributions of the rest-frame color P 1 for ∼ 7, 000 SDSS
galaxies, in the redshift range of 0.01 to 0.3, drawn from the DR1 “main” spectroscopic sample, that
have 1.4 GHz NVSS detections. The galaxies were spectroscopically classified as star forming (SF; thick
line), AGN (dashed line) or composite (dot-dashed line; see text for details). The top panel shows the P 1
histograms for these three types of objects. The middle left panel shows the fraction of these three types
of galaxies as a function of P 1. These distributions are normalized in such a way that for each P 1 value
the fraction of the three types of galaxies within the total selected sample can be read off. The bottom left
panel shows the cumulative distribution of P 1 for SF galaxies, thus showing to which completeness the
’real’ (i.e. spectroscopically classified) SF galaxies are selected for any given P 1 color-cut. The bottom and
middle right panels are analogous to the bottom and middle left panels, but for AGN galaxies. Note also
that the cumulative distributions for AGN were computed as a function of decreasing P 1. The vertical
(thin solid) lines in the middle and bottom panels designate the value of P 1, chosen to separate SF galaxies
from AGN.
78
3. VLA-COSMOS faint radio population
SDSS-NVSS-IRAS emission line galaxies
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
AGN
Star-forming
-1.5
-1
-0.5
0
0.5
Figure 3.6 Analogous to Fig. 3.4 but for ∼ 830 SDSS/NVSS/IRAS emission line galaxies.
galaxies as described in Sec. 3.4.1, and in the middle panel we show the corresponding
fraction (analogous to the middle left panel in Fig. 3.5). Using a rest-frame cut-off of 0.15
the selected sample of star forming galaxies, detected in the IR regime, contains ∼ 70%
’real’ (i.e. spectroscopically identified) SF galaxies (that make up ∼ 85% of all ’real’ SF
galaxies; see bottom panel), 10% AGN, and 20% composite objects. This is fairly consistent
with the properties of the entire SDSS/NVSS sample, and that the selection criteria for
SF and AGN galaxies adopted on the basis of the analysis of the full SDSS/NVSS sample
works almost equally efficiently also for the IRAS detected sub-sample.
3.4 Classification Methodology
79
In order to assess the fraction of dusty starburst galaxies that we omit using the restframe color selection, in the bottom panel of Fig. 3.7 we show the cumulative distributions of the spectroscopically identified star forming galaxies as a function of P 1 for i) all
star forming galaxies, ii) luminous IR galaxies (LIRGs; LIR = 1011−12 L⊙ ) and iii) ultraluminous IR galaxies (ULIRGs, LIR > 1012 L⊙ ). [The total IR luminosities were computed
following Sanders & Mirabel (1996).] A P 1 cut of 0.15 misses only ∼ 10% and ∼ 5% of
luminous and ultra-luminous starburst galaxies, respectively. In order to test this issue
further, we have synthesized the P 1 color for the ’standard’ dusty starburst galaxies M 82
(a typical LIRG), and Arp 220 (the prototypical ULIRG) using spectral templates given
in Polletta et al. (2006; see also Silva et al. 1998). The P 1 colors for M 82 and Arp 220
are 0.078 and 0.149, respectively, implying that M 82 would not have been missed by the
rest-frame color based classification method, while Arp 220, although close to the adopted
limit in P 1, still lies within our selection criterion. Based on the tests presented above we
conclude that our rest-frame color based classification method is not significantly biased
against dusty starburst galaxies.
3.4.2
Application of the rest-frame color based classification
method to the multi-wavelength photometry of the VLACOSMOS radio – optical galaxies
In Sec. 3.4.1 we have presented our rest-frame color based classification method, which
we have calibrated using a local galaxy sample, drawn from the SDSS and NVSS sky
surveys. Using galaxies from this sample, that were also detected by the IRAS satellite in
the IR, we have shown that the method is not significantly biased against selecting dusty
starburst galaxies (star forming LIRGs and ULIRGs). This is an important point for a
radio sample as radio emission is an efficient tracer of star formation, and of particular
importance especially in the most dusty systems. Therefore, having studied the rest-frame
color based classification method to a broad extent, and assuming that evolutionary effects
with redshift do not significantly affect the reliability of the classification, we can safely
apply it to the galaxies in the VLA-COSMOS 1.4 GHz matched radio sample (Sec. 3.5.3) in
order to separate SF from AGN galaxies at intermediate redshifts. However, first we need
to derive the rest-frame P 1 color from the observed SED of the VLA-COSMOS galaxies.
We do this via high-resolution SED fitting, described in Sec. 3.4.2, and we test the accuracy
of the rest-frame color synthesis in Sec. 3.4.2, and its effect on the selection of SF and AGN
galaxies in Sec. 3.4.2.
Derivation of rest-frame colors
In order to estimate the rest-frame colors P 1 and P 2 for each galaxy in the matched radio
sample that we do not classify as a star or QSO (see Sec. 3.5), we use the GOSSIP (Galaxy
Observed Simulated SED Interactive Program) software package (Franzetti 2005; PhD
Thesis), designed for fitting a galaxy’s SED to a set of chosen spectral models. The SED of
the galaxies in our sample, that we use for fitting, extends from 3500 Å to 2.5 µm and we fit
80
3. VLA-COSMOS faint radio population
Figure 3.7 Differential and cumulative distributions of the rest-frame color P 1 for 875 SDSS galaxies
from the DR1 “main” spectroscopic sample, that have NVSS and IRAS detections. The galaxies were
spectroscopically classified as star forming (SF; thick line), AGN (dashed line) or composite (dot-dashed
line; see text for details), and their P 1 histograms are shown in the top panel. The middle panel shows
the fraction of P 1 for SF, AGN and composite galaxies, analogous to the middle left panel in Fig. 3.5.
The vertical thin solid line shows the chosen P 1 color cut-off. The bottom panel shows the cumulative
distribution of P 1, normalized to SF galaxy counts (thick line). Also shown are the P 1 distributions of
SF galaxies with IR luminosities greater than 1011 L⊙ and 1012 L⊙ consistent with LRIGs and ULIRGs,
respectively. Note that our rest-frame color based classification method is not strongly biased against
dusty starburst galaxies.
3.4 Classification Methodology
81
to each observed SED a realization of ∼ 100, 000 spectra built using the Bruzual & Charlot
(2003) stellar synthesis evolutionary models. The library of model spectra was parameterized similarly to Salim et al. (2007). Previous versions of this library have been used in
Kauffmann et al. (2003a) and Kong et al. (2004). Star formation histories have been parameterized by an underlying continuous star formation history (decaying exponentially),
and randomly superimposed bursts. We thus cover ages between 108 and 1.35 × 1010 years,
specific star formation rates between 10−15 yr−1 and 3.93×10−8 yr−1 and metallicities from
a tenth to twice solar. The approach of fitting the multi-wavelength broad-band SED to
obtain physical parameters has been validated at low redshifts in Salim et al. (2007).
For each object in our sample the model spectra in our library are redshifted to the
galaxy’s measured redshift (spectroscopic where available, otherwise photometric), then
each spectrum is convolved with the observed filter response function5 , and then fitted
to the available observed photometric data, using a direct χ2 minimization procedure.
Output parameters, such as e.g. rest-frame colors, stellar mass or the 4000 Å break, are
taken from the best fit model spectrum. In order to derive physically meaningful output
parameters, we restrict the fitting procedure to models that have an age smaller than that
of the Universe at the galaxy’s redshift.
Tests on the derived rest-frame colors
In this section we test the accuracy of the synthetic magnitudes and colors derived via
SED fitting (see previous section) in order to assess the expected uncertainties of the P 1
color synthesis for the galaxies in the VLA-COSMOS matched radio sample.
The accuracy of the synthesized (relative to observed) magnitudes and colors is summarized in Fig. 3.8 for all galaxies in the matched radio sample (objects that were not
classified as stars or QSOs; see Sec. 3.5). The colors and magnitudes are reproduced with
a satisfying accuracy (∼ 0.07 and ∼ 0.04, respectively). The synthesized VJ shows a slight
systematic offset (∼ 0.05), which may arise from the incomplete knowledge of the filter
response curve and/or the presence of strong spectral emission lines which are not taken
into account in the model spectra. Nonetheless, we can conservatively conclude that the
overall accuracy of the synthetic magnitudes and colors is ∼ 0.1. It is also noteworthy
that P 1 is derived from a rest-frame spectral range of 3800 − 5800Å, which corresponds to
observed V , r, i and z band ranges for redshifts of roughly 0.1 to 0.9. Thus the obtained
color and magnitude accuracies in these observed spectral ranges mimic adequately the
rest-frame spectral range of interest.
In order to test the accuracy of the derived rest-frame colors further, we synthesize
P1 for a sample of ∼ 1700 local SDSS/NVSS galaxies. For these galaxies the P1 colors,
computed from their spectrum (with an accuracy of better than 0.03 magnitudes; see
Smolčić et al. 2006), are also available. Thus, comparing the rest-frame color synthesized
via SED fitting with the reference rest-frame color derived from the spectrum gives us
a direct measure of the accuracy of our synthetic P 1 color. In Fig. 3.9 we show the
5
The COSMOS filter response curves can be found here: http://www.astro.caltech.edu/∼capak/cosmos/filters
82
3. VLA-COSMOS faint radio population
Figure 3.8 Comparison of synthesized and observed magnitudes (top panel) and colors (bottom panel)
for all VLA-COSMOS objects from the matched radio sample that were not classified as stars or QSOs.
In each panel the median, and the upper and lower limit of the 68% confidence interval are indicated. The
reproduced color accuracy is ∼ 0.1.
derived accuracy for all ∼ 1700 galaxies (top panel). The mean color difference is 0, with
the expected root-mean-square-scatter of ∼ 0.1. It is also worth noting that the SED
fitting procedure is not exactly the same in the case of SDSS and VLA-COSMOS galaxies
(different filter response curves, wavelength, magnitude and redshift range), and that the
spectroscopically derived P 1 color carries its own uncertainties. Therefore, the derived
accuracy in Fig. 3.9 may be considered as an upper limit at the bright magnitude end
(rPet < 17.7). Combining the results from Fig. 3.8 and Fig. 3.9 (top panel) we conclude
that the synthesized rest-frame color P 1 is accurate to ∼ 0.1 mag.
In the bottom panel in Fig. 3.9 we show the difference of the P 1 color as a function
of the synthesized (GOSSIP) P 1 color for the SDSS/NVSS galaxies. A slight systematic
trend is present as a function of the derived P 1 color. In order to obtain an insight into the
origin of these systematics, we have visually investigated the SDSS spectra for a sub-set
of these galaxies with large (> 0.2), and insignificant (< 0.01) offsets in P 1. We found
that galaxies with large offsets are galaxies with strong emission-lines in the rest-frame
wavelength range of 3800 − 5800 Å (e.g. Hβ, OII, OIII), which is used to derive the
P 1 color, while this is not the case for the galaxies with insignificant offsets. The latter
tend to be either absorption line systems, or galaxies with weak emission-lines. Therefore,
3.4 Classification Methodology
83
we conclude that the observed systematic trend presumably arises from the presence of
strong emission lines in the galaxies’ SEDs, which are not taken into account in the BC03
model spectra. In our further analysis of the galaxies in the VLA-COSMOS matched radio
sample, we use the median offset, shown in the bottom panel in Fig. 3.9, to correct the P 1
color derived for the galaxies in the matched radio sample, and we consider the synthetic
P 1 color to be accurate to ∼ 0.1 mag.
Figure 3.9 The comparison of the P 1 color synthesized from the SDSS spectrum (P1SDSS that has
a typical error of 0.03) and via SED fitting (P1GOSSIP ) for ∼ 1700 SDSS/NVSS galaxies from the DR1
“main” spectroscopic sample (top panel). The P 1 color is reproduced with a spread of ∼ 0.1. The bottom
panel shows the difference in the P 1 color as a function of P1GOSSIP. The thin solid lines show the
upper P 1 limits of 0.6, imposed for this analysis. Note the slight systematic trend in the derived P 1
color as a function of P1GOSSIP (the large dots are the median offsets, with indicated 1σ error bars). The
trend presumably arises due to the presence of strong emission-lines in the rest-frame wavelength range of
3800 − 5800 Å, that are not taken into account in the BC03 models. We use these offsets to correct for
these systematics in the derivation of P 1 for the clean radio sample (see text for details).
84
3. VLA-COSMOS faint radio population
Uncertainty of the rest-frame color based classification method due to the uncertainties of the synthetic P 1 color
Our rest-frame color based classification method is a purely photometric technique designed
for disentangling SF from AGN galaxies based only on the distribution of their rest-frame
color P 1. In the previous section we have shown that the synthesized P 1 color is accurate
to ∼ 0.1 mag. However, an error of 0.1 mag for 68% of the galaxies, and 0.2 for 95%,
may substantially alter the SF/AGN selection, introducing the largest uncertainty for the
galaxies that have P 1 colors close to the chosen boundary of P 1=0.15. Nonetheless, we
can account for these uncertainties in a statistical manner, by Monte Carlo simulations, as
follows.
We simulate the P 1 error distribution using a randomly drawn Gaussian distribution
with a standard deviation of 0.1 centered at zero (see Fig. 3.9). These errors are then
added to the galaxies’ P 1 colors derived from the best fit template in the SED fitting,
and the SF/AGN selection (see Sec. 3.5.3) is applied. By repeating this procedure 10,000
times we obtain a robust statistical distribution of the number of selected SF and AGN
galaxies. The mean numbers of galaxies classified in this way as SF or AGN are 356 and
585, respectively, with a root-mean-square scatter of only 7. We reach the same result if
we model the error distribution using two separate Gaussian distributions for blue and red
galaxies.
Applying our SF/AGN selection using the P1 distribution obtained from the best fit
template in the SED fitting yields 340 SF and 601 AGN galaxies (see Sec. 3.5.3). Thus,
∼ 5% less SF galaxies are selected. This is easily understood as the blue tail of the P 1
distribution contains a smaller number of galaxies than there are in the prominent red peak
(see e.g. Fig. 3.5). Therefore, a normal error distribution will systematically scatter more
galaxies to the blue P 1 region, than to the red one. We conclude that the photometric
errors of the synthesized P 1 color introduce a number uncertainty of ∼ 5% in favor of SF
galaxies. Although ∼ 5% is not significant, it is necessary to keep this bias in mind in the
analysis of the ’population mix’ of submillijansky radio sources (Sec. 3.7.2).
3.4.3
Towards the classification of the VLA-COSMOS matched
radio sample: Outline and nomenclature
In Sec. 3.4.1 we have presented the calibration and effectiveness of the rest-frame color
based classification method for separating galaxies dominated by star formation from those
dominated by AGN activity. For this we have used the SDSS “main” spectroscopic sample –
a pure galaxy sample that by definition does not contain any star-like objects (Strauss et al.
2002). This, obviously, implies that the same effectiveness of the method can only be
reached if the rest-frame color based classification method is applied to a comparable
sample, i.e. galaxies only. However, our VLA-COSMOS matched radio sample consists not
only of galaxies, but also of stellar like sources, where the latter are either stars or quasi
stellar objects (QSOs). Therefore, we classify the sources in the matched radio sample
into five sub-types – a) star candidates, b) quasi stellar objects (QSOs), c) active galactic
3.5 Classification of VLA-COSMOS sources in the matched radio sample
85
nuclei (AGN), d) star forming (SF), and e) high redshift (z > 1.3; high-z) galaxies. The
latter three sub-types compose our “VLA-COSMOS galaxy sample”. The properties of
each sub-type are summarized as follows.
Stars: Point-sources in the optical, with their SEDs best fit using a stellar template.
OSQs: Point-sources in the optical (stellar-like SEDs are excluded; see above). This
criterion essentially requires that the emission of the nucleus in the optical strongly
dominates over the emission of the host galaxy. Thus, this sample predominantly
contains broad line AGN (i.e. type-1 AGN), with power law spectra in the optical.
AGN: Galaxies (not point-sources) whose rest-frame color properties are consistent
with properties of AGN (P 1 > 0.15, X-ray luminosity above 1042 erg s−1 if X-ray
detected). This selection requires that the optical emission either shows signs of both,
the emission from the central AGN as well as the emission from the underlying host
galaxy, or only the latter. Thus, this sample essentially includes Seyfert and LINER
types of galaxies, as well as absorption line AGN, and we limit it to redshifts of
z ≤ 1.3.
SF galaxies: Galaxies whose rest-frame color properties are consistent with properties of star forming galaxies (P 1 ≤ 0.15). Thus, the emission of these galaxies is
dominated by the emission originating from regions of substantial star formation.
This sample is also limited to redshifts ≤ 1.3.
high-z galaxies: Galaxies (not point-sources) with redshifts beyond z = 1.3.
3.5
Classification of VLA-COSMOS sources in the
matched radio sample
In this section we present the classification of the entire VLA-COSMOS matched radio
sample into star candidates (Sec. 3.5.1), QSOs (Sec. 3.5.2), SF, AGN and high-z galaxies
(Sec. 3.5.3).
3.5.1
Star candidates
In order to identify star candidates in the VLA-COSMOS matched radio source sample, we
make use of the COSMOS stellar catalog (Tasca et al. in prep), that was constructed from
the HST/ACS catalog (Leauthaud et al. 2007) using stellar templates to fit the entire SED
of each source. In Fig. 3.10 we show the color-color distribution for ∼ 2, 000 objects in
the COSMOS field securely classified as stars (with photometric errors better than 0.05),
which form well defined loci in the broad-band color-color diagrams. Cross-correlating our
matched radio sample with the COSMOS stellar catalog yields only 2 objects detected in
the radio regime that are consistent with having stellar properties. The color properties
86
3. VLA-COSMOS faint radio population
of these objects are shown in Fig. 3.10. Within the error-bars they are consistent with the
main stellar loci. Note, however, the r + -i+ color excess of one of the star candidates in the
r + -i+ vs. g + -r + color-color diagram (middle panel in Fig. 3.10), which suggests consistency
with properties of e.g. cataclysmic variables (e.g. Szkody et al. 2002, 2003), or unresolved
binary star systems containing a white dwarf and a late type star (e.g. Smolčić et al.
2004). The best fit stellar templates for these objects were taken from the PHOENIX
library (Hauschildt et al. 1997) and represent dwarfs with effective temperatures in the
range of 4100 to 5000 K and log (g) in the range of 3 to 3.5. The 1.4 GHz total fluxes for
these two objects are 126 and 152 µJy, and the corresponding i band AB magnitudes are
25.34 and 23.28, respectively. It is noteworthy that both objects have IRAC counterparts,
but no associated X-ray emission. We consider these two sources to be star candidates,
however a more detailed analysis (using for example spectroscopy), which is beyond the
scope of this paper, would be needed to verify this. Such a low fraction of identified stars is
consistent with star detection rates in other deep radio surveys (e.g. Fomalont et al. 2006).
The two star candidates in our radio sample form only ∼ 0.1% of the VLA-COSMOS
radio sources, and we exclude them from our sample for further analysis.
Figure 3.10 Color-color diagrams for COSMOS stars and VLA-COSMOS QSOs (see text for details).
The stars (black dots) form a narrow, well defined, locus in each diagram. The star symbols (yellow) show
2 objects detected in the VLA-COSMOS radio survey, identified as star candidates (see text for details).
VLA-COSMOS objects classified as QSOs by our selection criteria (blue squares) are also shown, and they
occupy the ’standard’ QSO regions in these diagrams (see text for details). The crosses (red) show objects
classified as a QSO by our technique, yet their spectra were identified as red galaxies. QSO evolutionary
tracks are shown to guide the eye (green curved lines).
3.5.2
Quasi stellar objects
Identification based on morphology
In order to identify QSOs in our matched radio sample we rely on an optical classification,
rather than using X-ray emission, due to the much higher sensitivity of the observations in
3.5 Classification of VLA-COSMOS sources in the matched radio sample
87
the optical (5σ sensitivity limit in the Subaru i band is 26.2; see Capak et al. 2007). For example, if one would select AGN relying purely on e.g. X-ray – to – optical flux ratios, which
are generally greater than 0.1 for both broad and narrow line AGN (e.g. Maccacaro et al.
1988; Alexander et al. 2001), with our optical limit of i = 26 (corresponding to r + of ∼ 27)
the depth of the X-ray point-source detection would have to be about 2 orders of magnitude
deeper than it currently is in order to select a complete sample of AGN. Further, a clear
distinction between broad and narrow line AGN would not be possible. Hence, here we
identify a QSO by requiring that a given source in the matched radio sample is optically
compact. In Fig. 3.11 we show the fitted i band FWHM of the sources in the COSMOS
photometric catalog (Capak et al. 2007) as a function of their i band magnitude. Pointsources (black squares), selected from the HST/ACS catalog (Leauthaud et al. 2007), form
a locus in this plane, separated from the area occupied by extended sources. However, the
point-source locus is fairly scattered, especially at faint magnitudes, and thus makes a single automatic cut at a certain FWHM value inefficient. For this reason, we classify sources
within the FWHM range of 1.85′′ − 2.05′′ as QSOs only if their optical HST morphology
was visually confirmed to be ’point-source dominated’. However, we consider all sources
below FWHM of 1.85′′ to be QSOs. We further supplement this sample with 12 objects
that were classified as point-sources in the HST/ACS catalog, but do not satisfy the above
criteria.
In summary, out of 1558 objects we identify 139 (i.e. 11.5%) as QSOs. In Fig. 3.10 we
show their broad-band (u∗ ,g + ,r + ,i+ ) color-color properties. As expected, the non-stellar
emission of the selected objects confines them to regions typical for QSOs, which are separated from the main stellar loci in these diagrams (e.g. Brusa et al. 2007; Richards et al.
2002). A minor fraction of these objects lie on the stellar loci. However, in the BzK
diagnostic diagram, which is an efficient tracer for stars (Daddi et al. 2004) these sources
are offset from the stellar locus, verifying their non-stellar nature.
Figure 3.11 FWHM of the optical counterparts of the matched radio sources (small
dots) as a function of i band magnitude,
taken from the COSMOS photometric catalog (Capak et al. 2007). Grey squares represent the HST/ACS point sources in the sample. Unresolved sources form a separated locus
in this diagram. We identify QSOs by requiring that a source is optically compact by choosing an absolute separation value of FWHM .
1.85′′ (below lower dash-dotted line; light-grey
shaded area). In the range of FWHM ∈
(1.85, 2.05]′′ (between upper and lower dashdotted lines; dark-grey shaded area) we identify objects as QSOs only if they were visually
confirmed to be point-source dominated (see
text for details).
As AGN dominated systems usually have soft X-ray to optical flux ratios in the range
88
3. VLA-COSMOS faint radio population
of about 0.1 to 10 (e.g. Maccacaro et al. 1988; Alexander et al. 2001), we can use the Xray to optical flux properties of the identified QSOs to further test our selection criteria.
43 objects in our QSO sample were detected as X-ray point-sources, and their X-ray to
optical flux ratios are consistent with the expected values. The median r + magnitude
for these sources is 21.3. For the remaining QSO candidates, that were not detected in
the X-rays, the median r + is 24.4. Therefore, these sources are also consistent with the
expected X-ray to optical flux ratios, however beyond our X-ray point-source detection
limit (10−15 erg cm−2 s−1 in the soft band). We conclude that the independent analysis of
the X-ray properties of our selected QSOs verifies the validity of our selection.
Spectroscopic verification
A sub-sample of 31 objects of the 139 previously identified QSOs have available optical spectroscopy with secure classifications (Trump et al. 2007; Prescott et al. 2006; Colless et al.
2001; Schneider et al. 2005), and only 3 of these objects were classified as red galaxies
(Trump et al. 2007), while all the others have AGN classifications. The 3 galaxies classified
as ellipticals were identified as QSO candidates by our method based on visual/morphologic
classification, which suggests the presence of dominating nuclear emission. The color properties of two of theses objects (see red crosses in the top panel in Fig. 3.10) are also
consistent with the color properties of quasars.6 Thus, we conclude that the selected QSO
sample is not significantly (. 10%) affected by contamination of non-QSO objects.
In order to assess the completeness of the selected QSO sample, we search for objects
that are spectroscopically classified as QSOs and ’missed’ by our classification method.
Our criteria yielded 139 objects classified as QSOs in the matched radio sample, and in the
remainder of the sample (i.e. the 1417 sources that were not classified as star candidates
or QSOs) spectroscopic classifications are available for 397 objects. Out of these, 9 were
spectroscopically classified as QSOs. Two SDSS examples, for which COSMOS HST/ACS
imaging is available, are shown in Fig. 3.12. They obviously show extended optical emission,
and a substantial light component arises from the host galaxy itself. The median redshift
of these 9 objects is only 0.4. It is noteworthy that all of these objects have X-ray point
source detections, and all except one have X-ray luminosities higher than 1042 erg s−1 .
Therefore these galaxies will be selected into our AGN class, hence not contaminating the
SF galaxy sample (see Sec. 3.5.3). As the spectroscopic sub-sample fairly represents the
full matched radio sample (see Fig. 3.3), we conclude, based on the above analysis, that
the sample of identified QSOs is about 80% complete. As expected, the incompleteness is
mostly due to relatively low redshift, low-luminosity AGN.
3.5.3
Star forming and AGN galaxies
In the previous sections we have identified 2 star candidates and 139 QSOs in the matched
radio sample. We will refer to the 1417 remaining sources in the matched radio source
6
For example, in the u∗ -g + vs. g + -r+ color-color diagram (top panel in Fig. 3.10) red galaxies would
occupy the upper right quadrant (see e.g. Strateva et al. 2001).
3.5 Classification of VLA-COSMOS sources in the matched radio sample
89
flux [1017 erg/s/cm2/Å]
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Figure 3.12
HST/ACS stamps (4′′ on the side; left column) and the corresponding SDSS spectra
(smoothed using 20 Å wide bins; right column) for two sources that were spectroscopically classified as
QSOs, but missed by our QSO selection method. The morphology of these sources is clearly extended
(in particular the source in the top panel is possibly a merging system with double nuclei visible, and
extending beyond the shown 4′′ ).
sample as the ’galaxy sample’. Before applying the rest-frame color based classification
method to our VLA-COSMOS galaxies in order to separate SF from AGN galaxies, we
restrict the galaxy sample to 941 galaxies with redshifts ≤ 1.3, as a) the photometric
redshifts are less reliable beyond this redshift, and b) the library of BC03 model spectra
that we use for the SED fitting may not be appropriate for fits beyond this redshift as
the distribution of priors was set to optimally match this intermediate redshift range.
Hereafter, we will call the sample of 476 galaxies with redshifts greater than 1.3 high
redshift (high-z) galaxies.
We perform an SED fit using GOSSIP (as described in Sec. 3.4.2) for each of the 941
objects in the matched radio ’galaxy’ sample out to z = 1.3. The distribution of the restframe color P 1 for these galaxies is shown in Fig. 3.13. The distribution is very similar
to that of the local sample (see top panel in Fig. 3.5) with a peak at P 1∼ 0.4 (AGN)
and a prominent tail towards bluer values (SF galaxies). We inspected the behavior of
the median value of the synthesized P 1 color for the entire z ≤ 1.3 galaxy sample as
a function of redshift, and we found no significant evolution in the median color. We
reached the same conclusion analyzing the median P 1 colors of the SF and AGN subpopulations. This implies that a fixed cut in the color can safely be applied to the entire
galaxy population out to z = 1.3.
The SED fitting was performed via a χ2 minimization procedure. The median value
of the reduced χ2 of the SED fits, computed using the best fit model spectrum, is 0.6
with an interquartile range of 2. Only 10% of the fitted objects have reduced χ2 values
above 5, and only 5% above 10. A visual inspection suggests that these galaxies are
90
3. VLA-COSMOS faint radio population
either nearby galaxies, which are resolved and often saturated in the Subaru i band, or
QSO contaminants. While the latter predominantly have blue P 1 colors, the synthesized
P 1 color for the first class of galaxies still appears to be a valid tracer for the SF/AGN
separation, and therefore we do not reject them from the sample.
Figure 3.13 The distribution of the
synthesized rest-frame P 1 color, corrected for the systematics (see bottom
panel in Fig. 3.9), for the objects in
the matched radio galaxy sample with
z ≤ 1.3. Note, that the expected distribution from our local sample, showing
a prominent tail towards bluer values
of P 1, is reproduced. The dash-dotted
line shows our chosen boundary for separating star forming from AGN galaxies.
In order to select SF and AGN galaxies we require that the synthesized P 1 color is
≤ 0.15 and > 0.15, respectively. However, to improve our selection at this point we make
use of the X-ray properties in the soft band of the 114 galaxies that were detected as X-ray
point sources. Namely, if the soft X-ray luminosity of an object is greater than 1042 erg s−1
we consider it to be an AGN, regardless of its P 1 color. Note that this criterion is expected
to reduce the contamination of the SF sample by objects with blue rest-frame colors, such
as QSOs missed by our selection. In summary, our selection yields 340 SF and 601 AGN
galaxies in our matched radio galaxy sample with z ≤ 1.3. We analyze these galaxies
further in Sec. 3.7.
3.6
Comparison with other selection methods
In this Section we compare our classification method, that we have applied to an intermediate redshift population, with other classification methods used for both local and intermediate redshift populations in the literature (Lacy et al. 2004; Stern et al. 2005; Best et al.
2005). We also study the 24µm properties of our radio sources, and their correlation to
the 1.4 GHz emission.
3.6.1
3.6-8 µm color – color diagnostics
QSOs, whose UV to NIR continuum is dominated by a power law, tend to be redder than
other types of galaxies in the MIR. Hence, they occupy a distinct region in MIR color
space, and several color-color criteria were suggested for their selection (Lacy et al. 2004;
Stern et al. 2005). In Fig. 3.14 we compare our classification method with those proposed
3.6 Comparison with other selection methods
91
in the MIR using a sub-sample of the matched radio sources that were also detected with
IRAC (∼ 90% have IRAC counterparts; see Sec. 3.3.2). We indicate the QSO (dots), AGN
(thin contours) and star forming (thick contours) galaxies selected using our method. The
dashed lines in the top and bottom panel in Fig. 3.14 show the color-color criteria proposed
by Lacy et al. (2004) and Stern et al. (2005), respectively, for the selection of broad-line
AGN. As expected, the majority of objects selected as QSOs by our method falls within
this region, reassuring the efficiency of the classification method presented here. There
are several QSO candidates outside these regions, which is not surprising as the suggested
’quasar regions’ do not select a 100% complete sample of QSOs, and a certain amount
of outliers is expected (see Stern et al. 2005 for a discussion of this point). In Sec. 3.5.2
we have inferred that our selected sample of QSOs is not significantly contaminated by
different types of objects, which is affirmed by this independent analysis.
Stern et al. (2005) showed that at redshifts of . 1 galaxies span a large range in the
m5.8 −m8.0 color, which is consistent with the horizontal extent of our selected star forming
and AGN galaxies (see bottom panel in Fig. 3.14). However, typical low luminosity AGN
and starburst galaxies cannot be clearly divided using these diagnostic diagrams (see also
color evolutionary tracks in Fig. 3.23). Nonetheless, elliptical galaxies (which correspond to
our class of absorption line AGN) tend to occupy the bottom left regions in both diagrams,
and close to these regions the distributions of our identified AGNs peak. On the other hand,
the peak of the distribution of our selected star forming galaxies in these diagrams is clearly
displaced from the one for AGN. This independently confirms that indeed two different
populations of galaxies are selected using our rest-frame color based classification method.
Further, Stern et al. (2005) showed that narrow line AGN appear spread out in both
the QSO and galaxy regions, which is also a result of our selection method [note that the
selected AGN are present in both regions]. The last point we want to stress is that the
star forming galaxy locus in these diagrams is also consistent with the expected colors, as
a ’contamination’ by star forming galaxies of the QSO locus is expected, especially close
to the boundary. In summary, the classification method presented here agrees remarkably
well with the expected properties of QSOs, AGN and star forming galaxies at intermediate
redshifts in the MIR range encompassing 3.6-8 µm.
3.6.2
24 µm properties: The 24 µm – radio correlation
A tight mid-IR (as well as far- and total- IR) – radio correlation is expected for star forming
galaxies, while ’radio-loud’ AGN are expected to strongly deviate from it (e.g. Condon 1992;
Bell 2003; Appleton et al. 2004). The 60 µm – radio correlation for low-luminosity AGN
was studied by Obrić et al. (2006) in the local universe. Based on a selection utilizing the
BPT diagram they have shown that also low -luminosity AGN follow a tight FIR – radio
correlation, however with a slightly different slope and a larger scatter than SF galaxies.
In this section we investigate the 24 µm – radio correlation for our selected SF and AGN
galaxies. In particular, if our SF/AGN separation method is successful, then a difference
in the 24 µm compared to 20 cm properties is expected to be seen for the two populations.
Our rest-frame color based classification method has identified 340 SF galaxies. Out of
92
3. VLA-COSMOS faint radio population
Figure 3.14 IRAC color-color diagrams in two representations (left and right panel) used as diagnostic
tools for the separation of QSOs (broad line AGN) from ’normal’ galaxies (Lacy et al. 2004; Stern et al.
2005). Filled circles display the 122 QSOs selected by our morphologic method that have IRAC detections;
thin and thick contours show the 579 AGN and 322 star forming galaxies separated by our rest-frame color
based classification method, respectively, that have IRAC counterparts. The contour levels in both panels
are in steps of 7 starting at 7. The dashed lines in the left and right panels show the empirically selected
regions for identifying broad-line AGN proposed by Lacy et al. (2004) and Stern et al. (2005), respectively.
these 82% (280) were detected at 24 µm with a signal to noise ≥ 3. On the other hand,
out of 601 selected AGN only 44% (267) have a MIPS 24 µm detection with S/N ≥ 3.
In Fig. 3.15 we show the 24 µm vs. 1.4 GHz luminosity7 (top panel) for our SF and
AGN galaxies, where the 24 µm data was not k-corrected. A correlation between the two
luminosities exists for both types of objects detected at 24 µm, although on average for a
given L1.4GHz the 24 µm luminosity is slightly lower for AGN than for SF galaxies (see also
below). For the SF and AGN galaxies that were not detected at 24 µm we have computed
upper limits of the 24 µm luminosity using the detection limit of the S-COSMOS MIPS
shallow survey which is 0.3 mJy. These limiting luminosities are also shown in Fig. 3.15.
Note that for AGN galaxies, as 56% of them are not detected at 24 µm, the scatter in the
correlation is significantly increased by these objects.
To quantify the correlation, we derive the classical q parameter (e.g. Condon 1992) as
the logarithm of the 24 µm to 1.4 GHz observed flux ratios. This parameter essentially
measures the slope of the correlation, and in the bottom panel in Fig. 3.15 we show it as
a function of L1.4GHz for our SF and AGN galaxies, with indicated upper limits (derived
as described above). The q parameter seems to show a decreasing trend with increasing
radio luminosity. However, this trend is dominated by the objects that have only estimated
upper limits, and therefore may be mimicked by the flux limits of the samples. A more
detailed analysis of this issue is beyond the scope of this paper.
The distribution of the q parameter for SF and AGN galaxies is shown in Fig. 3.16, for
galaxies detected at 24 µm and those which only have upper limits. The median q value
for SF galaxies is 0.82 ±0.05 with a scatter of ∼ 0.3 when all objects (also the upper limits)
7
The computation of 1.4 GHz radio luminosity is given in Appendix B.1.
3.6 Comparison with other selection methods
93
Figure 3.15 Top panel: 24 µm luminosity vs. 1.4 GHz luminosity for our selected SF (grey squares)
and AGN (black dots) galaxies. Upper 24 µm luminosity limits for the radio SF (18%) and AGN (56%)
galaxies that were not detected at 24µm are indicated by grey and black arrows, respectively. Bottom
panel: The q parameter, defined as the logarithm of the 24 µm to 20 cm flux ratio, as a function of 20 cm
luminosity for SF (grey squares) and AGN (black dots) galaxies. Upper limits are indicated by arrows, as
in the top panel. Note the much larger scatter in q for AGN than for SF galaxies.
are taken into account. On the other hand, the median q value for the AGN galaxies is
0.51 ± 0.02, significantly lower than for SF galaxies. We also find a larger spread in q
(∼ 0.4) for the AGN population. Note, however, that the spread quoted here should be
considered somehow tentative, especially for AGN, as the exact q values for the fraction
of objects not detected at 24 µm are not known. Nonetheless, this does not affect the
estimates of the median values. Our q parameter derived for SF galaxies is remarkably
consistent with the one inferred by Appleton et al. (2004) at 24 µm. Combining Spitzer –
MIPS and VLA observations in the First Look Survey with optical spectroscopy, they have
found a q value of 0.84 with a spread of 0.28 (with no k-corrections applied). They have
also shown that the FIR luminosities of AGN tend to be lower for a given radio luminosity,
94
3. VLA-COSMOS faint radio population
Figure 3.16 The distribution of the q
parameter (see also Fig. 3.15) for SF (top
panel) and AGN (bottom panel) galaxies.
In both panels the solid histograms show
the distribution of the 1.4 GHz sources detected at 24 µm while the dash-dotted histograms show the distribution of the upper
limits of q obtained for the radio sources
that were not detected at 24 µm (see text
for details). The solid vertical line in each
panel designates the median value of the
entire distribution, also indicated in the
top left in each panel.
consistent with our findings here.
Finally, in Fig. 3.17 we show the q parameter as a function of redshift for our SF and
AGN galaxies. q does not depend on redshift, both for SF and AGN galaxies, implying that
the MIR – radio correlation with the same slope is valid out to high redshifts (z ∼ 1.3).
This result is again consistent with those presented in Appleton et al. (2004).
Figure 3.17
q as a function of redshift for SF (grey squares) and AGN (black
dots) galaxies. Note that q does not depend on redshift, implying that the 24 µm–
radio correlation holds out to high redshift
(see text for details).
3.6 Comparison with other selection methods
95
In summary, the above results have shown that the 24 µm – radio correlation has
different properties for our selected SF and AGN galaxies, which verifies the efficency of
our rest-frame color based classification method.
3.6.3
Selection based on optical spectroscopic properties, radio
luminosity and stellar mass
Best et al. (2005) defined a sample of ∼ 3000 local (0.01 < z < 0.3) galaxies from the SDSS
DR2 “main” spectroscopic sample matched with sources above 5 mJy detected in the NVSS
survey. They further divided the radio – optical source sample into AGN and SF galaxies
making use of the galaxies’ location in the plane spanned by the 4000 Å break [Dn (4000)]
and radio luminosity [L1.4GHz ] normalized by stellar mass [M∗ ]. In Fig. 3.18 we compare
our rest-frame color based classification method with the one utilized by Best et al. (2005)
in the local universe. The Dn (4000) vs. log (L1.4GHz /M∗ ) distribution for all galaxies in
the matched radio source sample is shown in Fig. 3.18. The 1.4 GHz luminosity for these
galaxies was derived as described in Appendix B.1, and Dn (4000) and M∗ from the best
fit template from the SED fitting (see Sec. 3.4.2). The average errors are indicated. The
dashed line corresponds to the separation between SF and AGN proposed by Best et al.
(2005), and the two types of symbols designate the SF (squares) and AGN (dots) galaxies
identified by our rest-frame color based classification method. We want to note that Best
et al. calibrated their separation method using a slightly different selection of objects in the
BPT diagram (see their Fig. 9) with respect to the one we use here. Therefore, a perfect
correspondence between our and the Best et al. method is not to be expected, even if our
derived quantities were absolutely accurate. The area in the Dn (4000) vs. log (L1.4GHz /M∗ )
plane where the major disagreement is expected, due to the different selections in the BPT
diagram, is in the range of 1.4 <Dn (4000) < 1.6, and 11 < log (L1.4GHz /M∗ ) < 12. This
is the region where a larger fraction of Seyfert and LINER galaxies is located (see Fig. 9
in Best et al. 2005), and, different from Best et al., we define these galaxies exclusively as
AGN. In this region in Fig. 3.18 we indeed see the largest disagreement between the two
classifications. Further, the existence of objects with Dn (4000) < 1.3 that we classify as
AGN is not surprising, but it rather reflects the dual properties of composite objects, which
in this case were classified as AGN by our rest-frame color based classification method.
We also want to note that the average error in the synthesized Dn (4000) [derived from
comparison with the spectroscopic and synthetic Dn (4000) in the local sample] is fairly
large, and thus prevents a more detailed comparison between the two selection methods.
Overall, given the error bars and the difference in the basic selections of the two methods,
as well as the fact that our Dn(4000) values are not spectral measurements on the data,
but values taken from the best fit template, we conclude that our rest-frame color based
classification method agrees well with the one proposed by Best et al. (2005) in the local
universe.
In summary, our rest-frame color based classification method for separating SF from
AGN galaxies agrees well with other selection schemes, proposed in the literature, which
96
3. VLA-COSMOS faint radio population
are based both on MIR colors and optical spectroscopic diagnostics.
Figure 3.18 The 4000 Å break, Dn (4000), vs. 1.4 GHz radio luminosity normalized by stellar mass,
L1.4GHz /M∗ for galaxies in the matched radio sample. M∗ and Dn (4000) were synthesized via SED fitting
by GOSSIP. The black and grey symbols show objects classified by our rest-frame color based classification
method as AGN and star forming galaxies, respectively. The dashed line corresponds to the separation
proposed by Best et al. (2005). The average errors of the synthesized quantities are shown in the top
right. A good consistency between the two selection methods exists, given the large error bars, as well as
a slightly different selection rational used here and in Best et al. (2005a; see text for details).
3.7
Discussion: The composition of the faint radio
population
In previous Sections we have presented, tested, and discussed in detail the photometric
classification method which we used to separate the matched radio source sample into stars,
QSOs, star forming, AGN and high-z galaxies. In this Section we discuss the properties of
the ’population mix’ in the VLA-COSMOS survey: In Sec. 3.7.1 we describe the redshift
and luminosity distributions of the selected SF and AGN galaxies, and in Sec. 3.7.2 we
study the contribution of different source types to the sub-mJy radio population. We
show, based on the matched radio sample, as well as on the remaining radio sources with
no optical counterparts (brighter than i = 26), that star forming galaxies do not dominate
the sub-mJy sources, but that the majority of these sources is rather comprised of AGN
and QSOs.
3.7 Discussion: The composition of the faint radio population
3.7.1
97
The redshifts and luminosity distributions of SF and AGN
galaxies out to z = 1.3
The rest-frame color based classification method yielded 340 star forming and 601 AGN
galaxies out to redshifts of 1.3. In the top panel in Fig. 3.19 we show the redshift distribution for these galaxies using redshift bins of 0.217 in width. We use such wide redshift bins
to assess the average properties of the radio population, reducing the effects of fluctuations
due to the strong and narrow overdensities which are known to exist in the COSMOS field
(Scoville et al. 2007b; Finoguenov et al. 2007; Smolčić et al. 2007a, see Chap. 5). Poisson
errors are indicated for each bin. The deficit of galaxies at the low-redshift end reflects
the relatively small comoving volume sampled by the 2◦ area of the COSMOS field at
these redshifts. The decline in the number of sources at the high-redshift end, on the other
hand, reflects the detection limit of the VLA-COSMOS survey. The redshift distribution
of the number of star forming galaxies seems to be more uniform than the one for AGN, in
particular the relative number of star forming galaxies compared to AGN rises at higher
redshifts (z & 1). This may be explained by the relatively high number density of ULIRGs
expected at these redshifts (Le Floc’h et al. 2005; Caputi et al. 2007) in conjunction with
the VLA-COSMOS detection limit which at these redshifts allows to sample only radio
luminosities larger than 3 × 1023 WHz−1 (see Fig. 3.20 below). Further, as the comoving
volume surveyed at z ∼ 1 is larger than the one surveyed locally, the probability of detecting a ULIRG is also increasing at these redshifts. Effects of cosmic variance as a function
of redshift cannot be excluded, however they should be smaller than for other deep radio
surveys that typically probe significantly smaller areas.
In the bottom panel in Fig. 3.19 we show the fractional contribution of the SF and AGN
galaxies to the z ≤ 1.3 matched radio population, as a function of redshift. On average,
we find that the mean fractional contribution of SF and AGN galaxies to the z ≤ 1.3
matched radio population is (34 ± 14)% and (66 ± 14)%, respectively. This is strikingly
similar to the relative numbers of SF and AGN galaxies in the local Universe. Namely, if
we apply the adopted P 1 color cut to the SDSS/NVSS galaxy sample (see Sec. 3.4.1), we
find that ∼ 32% of the galaxies are star forming, and ∼ 68% are AGN. If, as shown by the
tests described in Sec. 3.4.1, our rest-frame color based classification method can reliably
be applied also to high redshift galaxies, then the similarity of the SF and AGN fractions
suggests that the two populations have similar evolutionary properties out to z ∼ 1.3.
In Fig. 3.20, we show the 1.4 GHz luminosity as a function of redshift for the selected
SF and AGN galaxies out to z = 1.3. We also indicate the expected luminosity ranges for
Milky Way type galaxies, LIRGs, ULIRGs and HyLIRGs, which were derived using the
total IR – radio correlation (Bell 2003). It is noteworthy that the majority of galaxies with
luminosities typical for HyLIRGs (LIR > 1013 L⊙ ) was classified by the rest-frame color
based classification method as AGN, consistent with the expected properties of these galaxies (e.g. Veilleux et al. 1999; Tran et al. 2001). This point is seen more clearly in Fig. 3.21,
where we show the distribution of the 1.4 GHz luminosity for the selected star forming and
AGN galaxies. The median luminosities are ∼ 1.6 × 1023 W Hz−1 and ∼ 3.2 × 1023 W Hz−1
for SF and AGN galaxies, respectively. Although the median luminosities of the two popu-
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3. VLA-COSMOS faint radio population
Figure 3.19 Top: Redshift distribution of i) the galaxies in the matched radio sample out to z = 1.3
(thick line), ii) the 340 selected star forming galaxies (light-grey shaded histogram) and iii) 601 identified
AGN (dark-grey shaded histogram). Bottom: The fractional distribution of the SF and AGN galaxies
compared to the total number of galaxies in the matched radio sample out to z = 1.3 as a function of
redshift. In both panels Poisson errors are indicated.
lations are different only by a factor of 2, there are some significant differences at both high
and low radio luminosity. At high luminosities there is the strong decline of the number
of SF galaxies with luminosities above ∼ 1024 W Hz−1 , while AGN show an extended tail
towards the brightest 1.4 GHz luminosities. Such a behavior is consistent with results from
local studies, which suggested that ’normal’8 galaxies tend to have L1.4GHz . 1024 W Hz−1
(e.g. Condon 1992). It is noteworthy, that our SF and AGN galaxies were identified completely independently from their radio luminosity, yet their 1.4 GHz luminosities match
8
’Normal’ galaxies, in terms of radio properties, are broadly defined as galaxies whose radio emission
is not powered by a super-massive black hole. These galaxies are a mix of spiral, dwarf irregular galaxies,
peculiar and interacting systems, as well as E/S0 galaxies with ongoing star formation (see Condon 1992
for a review).
3.7 Discussion: The composition of the faint radio population
99
Figure 3.20 1.4 GHz luminosity as a
function of redshift for the 340 selected
star forming galaxies (grey squares) and
601 AGN (black dots). The horizontal (dot-dashed) lines correspond to
1.4 GHz luminosities typical for various
classes of galaxies, obtained using the
total IR – radio correlation (Bell 2003).
The solid curved line corresponds to the
VLA-COSMOS 5σ limit of ∼ 50 µJy.
Note also that the VLA-COSMOS survey is sampling the entire LIRG and
ULIRG populations out to redshifts of
∼ 0.4 and ∼ 1, respectively.
the expectations based on local studies. At low luminosity (. 2 × 1022 W Hz−1 ; below the
typical LIRG radio luminosity) the fraction of SF galaxies increases and the numbers of
SF and AGN galaxies are similar to each other.
Figure 3.21
Distribution of the
logarithm of the 1.4 GHz luminosity
for the selected star forming (lightgrey shaded histogram) and AGN
(dark-grey shaded histogram) galaxies. The median log (L1.4GHz ) values for the star forming and AGN
samples are given in the top left
corner, and the average error in
log (L1.4GHz ) is shown in the bottom
left of the panel.
Further, the luminosity distribution shown in Fig. 3.21 agrees well with local results,
which have shown that L1.4GHz for star forming and (absorption and emission line) AGN
shows overlapping distributions, and consequently no clear separation (e.g. Sadler et al.
1999; Jackson & Londish 2000; Chan et al. 2004). In the local universe Sadler et al. (1999)
inferred a median L1.4GHz for SF and AGN galaxies to be ∼ 3 × 1022 W Hz−1 and ∼
3×1023 W Hz−1 , respectively. Hence, our median value for the luminosity of VLA-COSMOS
AGN (3.2 × 1023 W Hz−1 ) out to z = 1.3 matches the one inferred locally, however for SF
galaxies (1.6 × 1023 W Hz−1 ) it is higher than that derived by Sadler et al. (1999). The
100
3. VLA-COSMOS faint radio population
latter is easily understood as the combined effect of the higher median redshift (∼ 0.7) of
the galaxies in our flux limited sample (thus not probing low L1.4GHz ) and of the higher
level of star formation activity, which is observed going from redshift 0 to 1 (Madau et al.
1996; Hopkins 2004) also implying higher L1.4GHz (Condon 1992; Bell 2003).
3.7.2
The ’population mix’ in the VLA-COSMOS survey
In this Section we study the contribution of different sub-populations to the the total submJy radio population. The key question we want to answer is: Is the sub-mJy population
dominated by any particular sub-population, which may be the main cause for the observed
flattening of the differential radio source counts below 1 mJy (for VLA-COSMOS source
counts; see Bondi et al. 2007)?
Is the matched radio sample at sub-mJy levels dominated by star forming
galaxies?
In order to obtain an insight into the ’population mix’ of the faint radio sources in the
matched radio source sample, in Fig. 3.22 we show the distribution of the 1.4 GHz total
flux, Ftot , for the SF and AGN galaxies in the matched radio sample out to z = 1.3, as
well as for the identified QSOs.9 Note, that the remaining z > 1.3 galaxies in the matched
radio sample are defined as high redshift (high-z) galaxies. The flux bins in Fig. 3.22 are
0.15 mJy wide. Such wide bins allow us to study the average behavior of the galaxies in
the sub-mJy population with decreasing fluxes. Our main aim is to answer one of the more
controversial questions in radio astronomy: Is the sub-mJy radio population dominated by
star forming galaxies or any other distinct sub-population?
From the top panel in Fig. 3.22 it is obvious that at fluxes above ∼ 0.7 mJy we are
hampered by low number statistics (the total number of sources in each bin is below 20).
Therefore, for the purpose of this paper we will focus only on sources with fluxes below
0.7 mJy down to the VLA-COSMOS 5σ limit of ∼ 50 µJy.
The middle panel in Fig. 3.22 shows the fractional distribution of the identified populations in the matched radio sample, with indicated Poisson errors. Our findings are as
follows. QSOs contribute to the matched radio sample at a constant level of about 10%.
AGN galaxies below z = 1.3 show a decreasing trend with decreasing fluxes, with their
contribution to the matched radio sample dropping from ∼ 60% to less than 40%. The
SF galaxies at z ≤ 1.3 in the flux range of 50 µJy to 0.7 mJy contribute fairly constantly
at the given fluxes, with an average contribution of about 20%. Note that the possible
increment from 0.7 mJy to 50 µJy of only ∼ 10% is not significant. In Sec. 3.4.2 we have
inferred that the photometric errors in the synthesized P 1 color introduce a positive bias of
∼ 5% for SF galaxies. As this bin contains the lowest number of SF galaxies, it is the most
affected by this bias. In Sec. 3.4.1, based on the local SDSS/NVSS sample of galaxies, we
have shown that our rest-frame color based classification method selects ∼ 70% of ’real’ SF
9
Photometric redshift information for QSOs in the COSMOS survey is not available at this point.
3.7 Discussion: The composition of the faint radio population
101
galaxies, which make-up ∼ 85% of the complete sample of SF galaxies, while the photometrically selected AGN sample is contaminated by SF galaxies at the 5% level. Assuming
that the percentages of completeness and contamination, derived from the analysis of the
SDSS/NVSS sample of galaxies, can be safely applied also to our VLA-COSMOS sample,
we can use them to correct the observed fractions. Even with the correction the fractional
contribution of SF galaxies at z < 1.3 in the matched radio population essentially stays
the same, i.e. about ∼ 20% at fluxes in the range from 50 µJy to 0.7 mJy. Contrary
to previous studies (e.g. Seymour et al. 2004; Benn et al. 1993), our results show that SF
galaxies at intermediate redshifts are not the dominant population at sub-mJy flux levels.
However, it may be possible that a significant number of SF galaxies at z > 1.3 exists,
which may contribute strongly to the sub-mJy population. We investigate this possibility
in the bottom panel in Fig. 3.22 and in Fig. 3.23, and show below that this is not the case.
About 30% of the matched radio sample consists of galaxies at redshifts beyond z = 1.3
(high-z; see bottom panel in Fig. 3.22). The contribution of these galaxies marginally
increases from 20% at 0.7 mJy to 35% at 50 µJy. Although we were not able to apply
our rest-frame color based classification method to these galaxies in order to classify them,
we can nevertheless draw some conclusion about their nature by studying their multiwavelength properties. Therefore, in Fig. 3.23 we plot the MIR diagnostic diagram, for
the high-z galaxies and compare their properties to the classified sources in the matched
radio sample. We overlay non-evolving color tracks for the typical starburst galaxy M82,
an elliptical 13 Gyr old galaxy, and a Seyfert-2 type SED, obtained from a composite
spectrum of 28 randomly chosen Seyfert galaxies (Polletta et al. 2006). As expected, the
properties of high-z galaxies are consistent with properties of higher redshift (z > 1.5)
galaxies. However, the region they occupy in this MIR color-color diagram is occupied in a
similar way by different sub-populations, such as star forming, Seyfert-type, and passively
evolving galaxies, in the redshift range from about 1.5 to 3 (see color tracks in Fig. 3.23).
Further, it is possible that a fraction of these sources are broad line AGN as they are located
within the area outlined by dashed lines, which was proposed by Stern et al. (2005) for
the selection of AGN Type-1 (see also Caputi et al. 2007). This is further strengthened by
the 22 high-z sources which were also identified as X-ray point sources, and lie within the
Type-1 AGN selection region. Further, given the high redshift of these sources, combined
with the XMM detection limit, it is highly unlikely that any of these sources may be
star forming (see e.g. Fig. 14 in Trump et al. 2007). This analysis alone already indicates
that the matched radio sources beyond z = 1.3 are most probably a mixture of different
sub-populations.
The population mix in the high-z sample is further affirmed by the distribution of the
high-z galaxies in the BzK diagram (shown in Fig. 3.24), which is commonly utilized
for the selection of z > 1.4 galaxies, and separates well passively-evolving galaxies from
those originally defined as ’star forming’ at z > 1.4 (Daddi et al. 2004). Form Fig. 3.24
it becomes obvious that the BzK criterion does not select a pure ’star forming’ sample at
z > 1.4, as initially postulated, but a sample that is comprised of both SF galaxies and lowluminosity AGN. In addition, the X-ray detected high-z sources, which may be classified
102
3. VLA-COSMOS faint radio population
Figure 3.22 Top: The distribution of the 1.4 GHz total flux for the star forming (vertically hatched
histogram) and AGN galaxies (light-grey shaded histogram) in the matched radio sample out to z = 1.3
(see also Fig. 3.21). Shown is also the distribution for the selected QSOs (dark-grey shaded histogram),
for which good redshift information is not available at this point. ’MRS’ stands for the ’matched radio
sample’. Middle: The relative contribution of SF, AGN galaxies out to z = 1.3, and QSOs to the matched
radio sample. Note that the missing fraction of sources consists of the high redshift (high-z) objects, which
we define as galaxies in our matched radio sample with z > 1.3. Bottom: The cumulative distribution of
SF, AGN galaxies out to z = 1.3, QSOs and high-z galaxies in the matched radio sample. The indicated
error bars in the middle and bottom panels are derived from Poisson statistics. Note also the different flux
scales in the top and the middle/bottom panels.
3.7 Discussion: The composition of the faint radio population
103
Figure 3.23
MIR color – color digram (analogous to Fig. 3.14 , bottom panel) for the full matched
radio sample. Gray dots represent sources classified using our classification method (i.e. SF and AGN
galaxies out to z = 1.3, and QSOs), while the black dots show the high-z sources (galaxies in the matched
radio sample beyond z = 1.3). Open black sqares represent the high-z sources that have an XMM point
source counterpart. The dashed lines are indicated to guide the eye, and correspond to the AGN type-1
selection region, proposed by Stern et al. (2005). The curved green, blue, and red lines correspond to the
color-color tracks, obtained from SEDs of the starburst galaxy M 82, a composite of 28 Seyfert 2 galaxies,
and a 13 Gyr old elliptical galaxy, respectively, in the redshift range 0.1 – 2.5.
as AGN with high confidence as discussed above, also lie within this region. Therefore,
the SED color tracks combined with the distribution of the high-z galaxies (black dots in
Fig. 3.24), and the X-ray detected subsample suggest that our z > 1.3 matched sources
are a fair mixture of SF and AGN galaxies, yielding that the high-z galaxies continue to
consist of different galaxy populations at higher redshifts.
For reference, the i band magnitude distribution for the identified sub-samples in the
matched radio sample is shown in Fig. 3.25. Note that the high-z galaxies are the faintest
optical sources, consistent with the expectations for high redshift sources drawn from a
flux limited sample. However, we want to mention that even in the most extreme case that
all of the high-z galaxies were star forming, still the fractional contribution of star forming
galaxies to the matched radio source population would not dominate over the contribution
of AGN and QSOs, but the two contributions would rather be comparable.
As shown in Fig. 3.24 it is likely that both classes of objects contribute similarly to
the high-z galaxies, although their exact relative fractions cannot be determined from
this analysis. However, if we assume that roughly 50% of the high-z galaxies are SF
galaxies, then the fraction of SF galaxies in the matched radio sample would increase to
less than 40%. Therefore, we conclude that the population of star forming galaxies is not
the dominant population at sub-mJy levels, at least in our sample of radio sources with
104
3. VLA-COSMOS faint radio population
optical counterparts out to i = 26.
Our results are strikingly similar to the recent results by Padovani et al. (2007), who
studied the sub-mJy radio source population in the Chandra Deep Field South down to
a 5σ limit of 42 µJy. They found that SF galaxies make up only about 20% to 45% (i.e.
roughly 1/3) of the sub-mJy radio sources.
Figure 3.24 B − z vs. z − K color-color diagram for the entire matched radio sample. The symbols
are analogous to Fig. 3.23: Gray dots represent sources identified using our classification method (i.e. SF
and AGN galaxies out to z = 1.3, and QSOs), the black dots show the high-z sources, and open squares
show XMM detected point sources in the high-z sample. The dashed lines separate regions of passively
evolving (top right; outlined by the diagonal and horizontal dashed lines) and star-forming galaxies (left
of the diagonal dashed line) at z > 1.4, adopted from Daddi et al. (2004). The curved green and blue lines
correspond to the color-color tracks, obtained from SEDs of the starburst galaxy M 82, and a Seyfert 2
composite, respectively, in the redshift range from 0.1 to 2.5. Note, that Seyfert type of galaxies at z > 1.4
are also present in the region of ’star-forming galaxies’, as initially postulated by Daddi et al. (2004).
The contribution to the sub-mJy radio population from radio sources with no,
or with flagged, optical counterparts
Most of the previous studies (as well as our study up to this point) of the faint radio population have relied on sub-samples of the observed radio sources, that have been identified
with optical counterparts out to i = 26, as representative of the entire sub-mJy radio population. For example, Benn et al. (1993) used a sample of only 87 out of 523 (i.e. less than
20%) 1.4 GHz radio sources above 0.1 mJy, for which they obtained optical spectroscopy
(B < 22), to conclude that above 1 mJy about 50% of the galaxies were SF or Seyfert
galaxies, while below 1 mJy the fraction increases to ∼ 90%. Further, Gruppioni et al.
(1999) studied optical spectroscopic properties of 34 radio sources above 0.2 mJy in the
3.7 Discussion: The composition of the faint radio population
105
Figure 3.25 Distribution of the i band magnitude (Subaru where available, otherwise CFHT) for sources
in the VLA-COSMOS matched radio sample (MRS). Also shown are the distributions for the identified
sub-samples of sources: OSOs, star-forming (SF), AGN, and high-z galaxies
Marano Field down to B = 24. This sample comprised ∼ 60% of the entire sample of faint
radio sources, and they concluded that the SF galaxies do not constitute the main galaxy
population of their radio sources, and even at sub-mJy levels the majority of their radio
sources were identified with early type galaxies, consistent with AGN. Gruppioni et al.
attributed the difference in their results compared to the results from Benn et al. (1993)
to the fainter optical magnitude limit reached for their radio sample. In this work we have
an order of magnitude larger sample size (1558 sources with optical counterparts), and a
significantly deeper optical limit (i = 26) than previous studies. However, still our matched
radio source sample consists of only ∼ 65% of the VLA-COSMOS 1.4 GHz sources. Therefore, it is important to investigate the contribution of the remaining ∼ 35% of the radio
sources, with no identified or flagged optical counterparts, to the sub-mJy population. It
may indeed be possible that a ’missing’ population of objects, that significantly contributes
to the sub-mJy population, is ’hidden’ in this sample. We show below that this is not the
case.
If the above hypothesis is true, then the properties (such as e.g. MIR colors) of these
106
3. VLA-COSMOS faint radio population
remaining objects are expected to be distinct from the properties of objects in the matched
radio sample. In order to shed light on this, in Fig. 3.26 (top panel) we show the distribution
of the total flux for the 1558 sources in the matched radio sample, and for the remaining
830 sources that were a) not identified with optical counterparts with i ≤ 26 or b) have
optical counterparts with i ≤ 26 but in photometrically masked-out region (see Sec. 3.3.1).
The bottom panel shows the fractional contribution of these two samples compared to
the entire VLA-COSMOS 1.4 GHz population. The fraction of matched radio sources is
statistically consistent to be constant at ∼ 65% at all faint flux levels, although formally it
decreased from ∼ 75% at ∼ 0.7 mJy to ∼ 60% at the limit of the VLA-COSMOS survey.
On the basis of these high percentages, it is unlikely that any further population, that is
not present in our matched radio sample, could account for a significant fraction of the
sub-mJy population. This is further strengthened by the distribution of the remaining
Figure 3.26 Top: The distribution of the 1.4 GHz total flux for sources in the matched radio sample
(single hatched histogram), and for other VLA-COSMOS sources that have either no identified, or a
photometrically flagged, optical counterpart brighter than i = 26 (cross-hatched histogram). Bottom:
The fractional distribution of the two samples compared to the entire sample of 1.4 GHz radio sources.
Indicated error bars are derived from Poisson statistics.
sources in the MIR color-dolor diagram, shown in Fig. 3.27, which is consistent with the
expected distribution for a mixed sample of star forming, AGN galaxies, and QSOs at all
3.7 Discussion: The composition of the faint radio population
107
redshifts in the range from the local to the highest observable redshifts. An additional
affirmation of the mix of population arises from the 31 sources that have been detected
as X-ray point sources (see Fig. 3.27). It is worth noting however, that ∼ 60% of these
sources are consistent with higher redshift objects (z & 1.3), while this is true only for
∼ 50% of the galaxies in the matched radio sample (see Fig. 3.23). We therefore conclude
that the radio sources without identified, or with flagged, optical counterparts brighter
than i = 26 are most likely comprised of a mixture of different source populations (SF,
AGN, QSO), similar to, although on average at higher redshift then the radio sources in
our matched radio sample. Further, the relatively small total percentage of these sources
cannot significantly alter the results about the ’population mix’ in the 1.4 GHz VLACOSMOS radio sample, inferred in the previous section.
Figure 3.27
Analogous to Fig. 3.23, but for 813 VLA-COSMOS 1.4 GHz sources (grey dots) that
have either no identified, or a photometrically flagged, optical counterpart brighter than i = 26, but that
have IRAC detections (i.e. ∼ 75%; see Sec. 3.3.4). Grey squares indicate the sources that have an optical
counterpart with i ≤ 26, but that is within a masked-out area. Black open sqares represent the sources
that have XMM point source detections. The clumping of objects in the upper left quadrant is consistent
with both star forming, Seyfert and passive galaxies, in the redshift range from about 1.5 to 3, suggesting
that over ∼ 60% of the VLA-COSMOS ’remaining’ sources are high redshift galaxies or QSOs.
108
3.7.3
3. VLA-COSMOS faint radio population
Concluding remarks on the composition of the faint radio
population
The faint – submillijansky – radio population comprises the radio population responsible
for the upturn of the differential radio source counts below 1 mJy (see e.g. Bondi et al.
2007), and it has been the subject of many studies, and a matter of great debate in
the past three decades (Condon 1984a; Windhorst et al. 1985a; Gruppioni et al. 1999;
Seymour et al. 2004; Simpson et al. 2006). This radio population has been interpreted
as a ’new’ rising population of objects, that do not significantly contribute at higher radio
flux levels. However, results from studies that tried to reveal the exact composition of the
sub-mJy population have been highly discrepant. It was suggested that the majority of this
faint radio population consists of faint blue galaxies, and it was assumed that these galaxies are undergoing significant star formation (Windhorst et al. 1985a). The spectroscopic
study by Benn et al. (1993), although analyzing only less than 20% of their radio sample,
supported this result indicating that the fraction of SF and Seyfert galaxies rises from
about 50% to 90% in the range from super- to sub-mJy fluxes. However, Gruppioni et al.
(1999), who performed a deeper optical spectroscopic analysis of a larger fraction (∼ 60%)
of their radio sources, disagreed with this result identifying the majority of their sub-mJy
radio sources with early type galaxies, in which the radio emission is produced by AGN.
Recently, using a combination of optical and radio morphology as an identifier for AGN
and SF galaxies on ∼ 90% of their radio sources in the SSA13 field, Fomalont et al. (2006)
suggested that less than ∼ 40% of the radio sources below 1 mJy are AGN. On the other
hand, Padovani et al. (2007) based their SF/AGN classification on a combination of optical
morphologies, X-ray luminosities and radio–to–optical flux ratios of their radio sources in
the CDFS (Chandra Deep Field South), and indicated that only about 20 − 40% of the
faint radio sources are made-up of star forming galaxies.
As already mentioned in the Sec. 3.1 there are two main reasons for such controversial
results in the past literature: a) The identification fraction of radio sources with optical
counterparts spans a wide range (20% to 90%) in samples from radio deep fields, and b)
the methods for the separation of SF from AGN galaxies have been highly heterogeneous.
Further, the first and second point seem to exclude each other. Namely, the most efficient
separation methods (e.g. intermediate- to high- resolution optical spectroscopy) generally
led to a small identification fraction, whereas large radio samples with high optical identification fractions usually lacked robust SF/AGN indicators. In this paper we have tried to
make a strong conjunction of these two points. We introduced a new method to separate SF
from AGN galaxies, which relies strongly on rest-frame color properties, and thus allowed
us to perform a robust classification of ∼ 65% (= 1558) of the 1.4 GHz VLA-COSMOS
radio sources down to ∼ 50 µJy with identified optical counterparts out to i = 26 without
the need for e.g. optical spectroscopy. Thus, we managed to reach both a high identification fraction and a robust source classification. However, in order to avoid any possible
biases, we also utilized the full panchromatic COSMOS data set to put constraints on
the properties of the remaining ∼ 35% of the radio sources that were not identified with
optical counterparts with i ≤ 26, or that have optical counterparts with i ≤ 26 but with
3.8 Summary
109
uncertain photometry due to blending and saturation in the optical images. In short, we
have obtained a complete view of the ’population mix’ of the sub-mJy radio sources, using
to date the largest sample of 2388 faint (5σ ≈ 50 µJy) radio sources at 1.4 GHz. Our
combined methods allow us to reveal the true nature of the sub-mJy radio sources in the
VLA-COSMOS survey on a sound statistical basis.
Jarvis & Rawlings (2004) first suggested that the observed flattening of the differential
radio source counts below 1 mJy may be caused by ’radio-quiet’ AGN, i.e. radio-quiet
QSOs and type-2 AGN (consistent with our ’AGN’ class), rather than star forming galaxies. Making use of the observed correlation between X-ray and radio luminosity and the
empirically derived X-ray luminosity function, they computed the 1.4 GHz luminosity function for radio-quiet AGN out to high redshifts, and used it as the key ingredient to model
the radio source counts at faint flux levels (see Jarvis & Rawlings 2004 and references
therein). However, they did not take into account SF galaxies as an important population to explain the shape of the counts. Such an interpretation managed to represent the
differential radio source counts well down to ∼ 250 µJy, but failed at fainter fluxes. Our
results, derived in the previous sections, show that indeed star forming galaxies are not
the dominant population at sub-mJy levels in the range of 50 µJy to 0.7 Jy, however they
still contribute about 30% to 40% at these fluxes. Based on our large sample of 2388
VLA-COSMOS radio sources we find that our classified AGN, which comprise mostly of
low-luminosity and type-2 AGN, make up 50 − 60% of the faint radio population with a
decreasing trend towards fainter fluxes, while the identified QSOs, which are mostly type-1
AGN, form a minor contribution of ∼ 10% of the sub-mJy radio population in the range
of 50 µJy to 0.7 Jy. Thus, our observational results show that the ’population mix’ in the
faint radio population contains a fair contribution of both SF galaxies and (low-luminosity
and obscured) AGN, at least down to 50 µJy. Thus, separate luminosity functions for
both populations have to be taken into account in order to fully explain the flattening of
the radio source counts below 1 mJy. The 1.4 GHz luminosity function for star forming
and AGN galaxies at intermediate redshift, as well as the modeling of the 1.4 GHz radio
source counts using these new observational constrains and further analyses of the ’population mix’ in the VLA-COSMOS survey, is going to be fully addressed and presented in a
number of up-coming publications.
3.8
Summary
We have explored the properties of the sub-mJy radio population, detected at 20 cm
(1.4 GHz) in the VLA-COSMOS survey, by introducing a new method to separate star
forming from AGN galaxies. The main feature of our classification method is an effective
identification of star forming galaxies and low-luminosity AGN (i.e. LINERs and Seyferts),
based only on optical rest-frame color properties. Based on a large number of local (z < 0.3)
galaxies with available high resolution spectroscopy we have shown that, at least in a radio selected sample, such a purely photometric separation is possible due to the tight
correlation between rest-frame colors of emission-line galaxies and their position in the
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3. VLA-COSMOS faint radio population
emission-line based BPT diagram, which is commonly used for spectroscopically separating these two types of galaxies. Making use of the full COSMOS photometric data set,
in conjunction with the rest-frame color based classification method, we have performed
an in-depth analysis of 2388 VLA-COSMOS sources detected at 1.4 GHz with a signal to
noise ≥ 5. We classified the radio sources with optical counterparts brighter than i = 26
into five classes of objects: a) star candidates, b) quasi stellar objects, c) AGN, d) star
forming galaxies, and e) galaxies at redshifts greater than 1.3. For the latter class, as well
as for the radio sources without, or with photometrically flagged, optical counterparts with
i ≤ 26, we have used observed optical to MIR color properties to study their nature. Our
classification method, tests, and results are summarized as follows.
We have positionally matched the 2388 VLA-COSMOS detected at 1.4 GHz with S/N ≥ 5
with sources detected at other frequencies (optical/NIR, MIR, X-ray). About 65% of the
radio sources have optical counterparts with i ≤ 26 which are not located in blended or
saturated regions. This sample, which we defined as the matched radio source sample,
essentially contains all true matches with a false association rate (obtained from Poisson
statistics) of only ∼ 4%. Further, ∼ 90% of the sources in the matched radio source sample
have IRAC counterparts, and ∼ 10% have X-ray point source counterparts. Out of the
remaining ∼ 35% of the radio sources without identified, or with identified but photometrically uncertain, optical counterparts brighter than i = 26, ∼ 75% were associated with
sources detected in the MIR wavelength regime with Spitzer – IRAC. The lower fraction
of IRAC counterparts in this sample is mostly due to blending effects, and possibly to a
small number of spurious radio sources.
The heart of our classification method, which allows us to disentangle SF from lowluminosity (i.e. LINERs, Seyferts) and absorption-line AGN is the use of only photometric
– optical rest-frame color – properties. This method was thoroughly tested using a large
number of local galaxies drawn from the SDSS “main” spectroscopic sample, positionally
matched to the NVSS and IRAS sky surveys. For the SF/AGN separation we use the
P 1 rest-frame color, which is a linear superposition of Strömgren colors in the wavelength
range of 3500 – 5800 Å. We have adopted a P 1 color cut of 0.15 as the boundary between
SF and AGN galaxies. Based on the local sample, which is a fair representation of the
VLA-COSMOS sources at higher redshift, we have shown that such a color selection yields
a ’photometrically selected sample of SF galaxies’ that is contaminated by AGN and composite objects at the 20% and 10% levels, respectively, and it contains about 70% of ’real’
(i.e. spectroscopically identified) SF galaxies, and those make-up ∼ 85% of the complete
sample of all ’real’ SF galaxies. In addition, we have analyzed the properties of the missed
’real’ SF galaxies due to the single rest-frame color criterion applied, and we have shown
that, although the rest-frame classification method is based on optical properties, it is not
biased against dusty starburst galaxies (i.e. LIRGs and ULIRGs). Further, the ’photometrically selected sample of AGN galaxies’ is made-up of about 5% SF and 15% composite
galaxies, and contains ∼ 80% of ’real’ AGN galaxies, which compose ∼ 90% of all ’real’
AGN galaxies.
3.8 Summary
111
For the galaxies in the VLA-COSMOS matched radio source sample, we have synthesized
the rest-frame color P 1 using the SED fitting code GOSSIP with a realization of 100, 000
model spectra built using the Bruzual & Charlot (2003) stellar synthesis evolutionary models. We have extensively tested the achieved accuracy of the synthetic magnitudes and
colors, and showed that the P 1 color, derived via SED fitting, is accurate to ∼ 0.1 mag.
Using the full COSMOS multi-wavelength data set we have identified the radio sources in
the matched radio sample as a) star candidates, b) QSOs, c) AGN, d) SF, and e) high
redshift (high-z) galaxies. Comparing our classification with separation methods used in
the literature which are based on both MIR color and optical spectroscopic properties of
galaxies, we have shown that our selection technique agrees remarkably well with other,
completely independent, methods, thus confirming the robustness and effectiveness of our
classification method, also at intermediate redshifts. Further, we have used optical to MIR
color properties to get an insight into the nature of the high-z galaxies, as well as the radio
sources without (or with flagged) optical counterparts brighter than i = 26. The main
characteristics of these sub-types are outlined as follows.
Star candidates: We have identified 2 star candidates (∼ 0.1% of the 1558 radio –
optical sources). The 1.4 GHz total fluxes for these 2 objects are 126 and 152 µJy, and no
X-ray emission was detected. Nonetheless, the final verification of the true stellar nature
has to await for the completion of the COSMOS spectroscopic programs.
QSOs: We have identified 139 (∼ 9%) QSOs in the matched radio source sample based
on their compact optical morphology. We have verified our selection using a sub-sample of
the selected QSOs with available spectroscopy, and we have shown that the selected sample
is ∼ 80% complete, and essentially not contaminated by other kinds of sources. The optical
and MIR color properties, as well as X-ray to optical flux ratios, of the selected objects are
consistent with properties of QSOs, further affirming the efficiency of our selection.
SF/AGN galaxies: We have identified 601 (∼ 60%) AGN and 340 (∼ 40%) SF
galaxies out to z = 1.3 using our rest-frame color based classification method, i.e. applying
a color cut of P 1 > 0.15 for AGN, and P 1 ≤ 0.15 for SF galaxies.
high-z galaxies: The galaxies in the matched radio sample above a redshift of 1.3 were
not classified using the rest-frame color based classification method due to higher uncertainties at these redshifts. Nonetheless, their optical to MIR colors, and X-ray properties
strongly suggest that they are a mixture of both SF and AGN galaxies predominantly at
z > 1.3 and Fig. 3.24).
Remaining radio sources: The MIR color properties of the radio sources without
or with flagged optical counterparts brighter than i = 26 suggest that their composition
is similar to the composition of the sources in the matched radio sample, i.e. they also
consist of a mixture of SF and AGN galaxies. However, the fraction of these sources that
is located at redshifts beyond ∼ 1.3 is higher than the one in the matched radio sample,
indicating that these sources have a higher median redshift than the sources with identified
optical counterparts brighter than i = 26 and outside blended and saturated regions in the
optical images.
112
3. VLA-COSMOS faint radio population
We studied the properties of the SF and AGN galaxies out to z = 1.3 in the matched radio
sample, and found that the median redshift of both types of galaxies is z ∼ 0.7. Further,
their median 1.4 GHz luminosities, drawn from our flux limited sample, are very similar, i.e.
they differ only by a factor of 2 (AGN: 3.2×1023 W Hz−1 ; SF: 1.6×1023 W Hz−1 ). However,
only a minor fraction of the identified SF galaxies have luminosities above 2 ×1024 W Hz−1 ,
while AGN show a strong tail towards higher 1.4 GHz luminosities. Such a behavior is
consistent with the expectations based on results from previous – local universe – studies,
combined with the rising level of star formation activity in the universe in the redshift
range from 0 to 1.
∼ 80% of our selected star forming galaxies have MIPS 24 µm detections at or above a
signal to noise of 3, while this is true for only ∼ 40% of our selected AGN. We have derived
the classical q parameter [log(F24 µm /F1.4 GHz )], which measures the slope of the 24 µm–
radio correlation. The q parameter is significantly different for SF (0.82 ± 0.05) and AGN
(0.51 ± 0.02) galaxies, when all radio detected sources are taken into account (given the
upper limits from the 24 µm detection sensitivity). When only the 24 µm detected sources
are taken into account the q parameter for SF and AGN galaxies is different by only ∼ 10%,
consistent with findings in the local universe. Our results imply that the 24 µm– radio
correlation holds out to at least z = 1.3.
We have explored to full detail the composition of the sub-mJy radio population making
use of the entire sample of 2388 VLA-COSMOS radio sources detected above 5σ (≈ 50µJy),
in conjunction with the panchromatic (X-ray to radio) COSMOS data set. We find that
SF galaxies are not the dominant population at submillijansky flux levels, as believed previously, but that they rather make up an approximately constant fraction of 30 − 40% in
the flux range of ∼ 50 µJy to 0.7 mJy. The radio population at these fluxes is a mixture of
roughly 30 − 40% of SF and 50 − 60% of AGN galaxies, with a minor contribution (∼ 10%)
of QSOs.
In summary, our newly developed method, in conjunction with the VLA-COSMOS
1.4 GHz observations, enabled us to make, for the first time, a thorough distinction between sources where the 1.4 GHz radio emission is predominantly driven by star formation
processes from those where it is driven by SMBH processes, regardless of the luminosity of
the latter, and apply it to currently the largest sample of 1558 1.4 GHz sources, with optical
counterparts brighter than i ≤ 26, and complete down to ∼ 50 µJy. Further, utilizing the
panchromatic COSMOS data we were able to put robust constraints onto the properties
of the entire 1.4 GHz VLA-COSMOS radio sample containing 2388 sources, yielding the
first clear insight into the characteristics of the submillijansky radio sources, showing that
the faint radio sources are, contrary to common previous belief, a fair mixture of both SF
and AGN galaxies.
Chapter 4
The dust un-biased cosmic star
formation history derived using
1.4 GHz data from the
VLA-COSMOS survey
In the previous Chapter the newly developed (rest-frame color based) classification method
has been used to efficiently identify AGN and star forming galaxies in the VLA-COSMOS
radio source sample. In this Chapter the cosmic star formation history since ∼ 5 Gyr
after the Big Bang is derived based on radio data utilizing this well defined sample of
VLA-COSMOS star forming galaxies. The work presented in the following is about to be
submitted to ApJ.
Abstract
We derive the cosmic star formation history (CSFH) based on 1.4 GHz radio data using
an almost one order of magnitude larger sample of star forming galaxies, than available
previously. We constrain the 1.4 GHz luminosity function for ∼ 350 well selected VLACOSMOS star forming galaxies out to z = 1.3, and find that a pure luminosity evolution
is best described with L∗ ∝ (1 + z)2.0±0.1 . Based on the large 2◦ COSMOS field for the
first time the high-luminosity end of the star forming galaxy luminosity function has been
constrained with high precision allowing the derivation of the evolution of the cosmic star
formation rate in the most intensely star forming galaxies (SFR & 100 M⊙ ; ULIRGs).
We find that the CSFH for star forming ULIRGs has declined slower since z = 1.3 than
previously predicted based on MIR data, implying that the fraction of star forming galaxies
in MIR-selected samples is likely lower than commonly assumed. Our overall derived CSFH
confirms the high dust-obscuration corrections applied to star formation rate tracers at
other wavelengths.
114
4.1
4. The dust un-biased cosmic star formation history (CSFH)
Introduction
Studies based on different galaxy star formation indicators (UV, optical, FIR, radio) agree
that the cosmic star formation history (i.e. the total star formation rate per unit co-moving
volume; CSFH hereafter) has declined by an order of magnitude since z ∼ 1 (for a compilation see e.g. Hopkins 2004). One of the major difficulties of UV/optical based tracers is the
significant model-dependent dust-obscuration correction that needs to be imposed on the
data. This ’dust-obscuration problem’ may be overcome by reaching to longer wavelengths,
such as the IR and radio regimes. However, in these cases a multi-wavelength approach
is essential as redshift information and a good star forming (SF) galaxy identification is
required (Caputi et al. 2007, Smolčić et al. 2007 (Chap. 3); S07 hereafter). In this context
the radio star formation tracer provides an important complementary view of the CSFH.
First, radio emission is a dust-insensitive tracer of recent star formation (i.e. not affected
by old stellar populations; see Condon 1992 for a review), and secondly, interferometric
radio observations with 1 − 2′′ resolution allow more reliable identifications (compared to
FIR/(sub-)mm) with objects detected at other wavelengths.
The dust-unbiased total CSFH has been constrained to a large amount using MIR
(24/8µm) selected samples obtained by deep pencil-beam surveys (CDFS, GEMS, GOODS;
Le Floc’h et al. 2005; Zheng et al. 2006; Caputi et al. 2007). Small area surveys, however, are generally affected by cosmic variance. At the high end of the star-forming
luminosity function they do not observe a large enough comoving volume in order to
fairly sample rare high-luminosity galaxies. In this Letter we focus on the derivation
of the CSFH, with emphasis on the evolution of galaxies with high star formation rates
(& 100 M⊙ yr−1 ; i.e. ULIRGs), using 1.4 GHz radio observations of the large (2◦ ) COSMOS field (Schinnerer et al. 2007; Scoville et al. 2007a).
Throughout the paper we report magnitudes in the AB system, use the standard cosmology (H0 = 70, ΩM = 0.3, ΩΛ = 0.7), and define the radio synchrotron spectrum as
Fν ∝ ν −α , assuming α = 0.7.
4.2
4.2.1
The 1.4 GHz luminosity function for star forming
galaxies in VLA-COSMOS
Star forming galaxy sample
The sample of SF galaxies used here is presented in S07, and briefly summarized below.
The VLA-COSMOS survey of the 2◦ COSMOS field contains 2,388 sources detected
at 20 cm with S/N ≥ 5. Positionally matching these sources to the COSMOS NUV-NIR
catalog (Capak et al. 2007) S07 defined a sample of 1,558 radio-optical sources (i ≤ 26)
with the most accurate photometry. The remaining 830 sources consist of radio sources
with photometrically flagged (∼ 30%), or without (∼ 70%), optical counterparts with
i ≤ 26.
Using the full COSMOS panchromatic data S07 classified the 1,558 radio – optical
4.2 The 1.4 GHz luminosity function for star forming galaxies
115
sources into star candidates, QSOs, AGN, SF and high-z galaxies. Special care was
given to the separation of SF galaxies from low-luminosity AGN (e.g. Seyfert, LINER
and absorption-line AGN) out to z = 1.3 by developing a scheme, based on a rest-frame
color which separates these two populations with high efficiency (see also Smolčić et al.
2006).
The final sample of selected SF galaxies in the VLA-COSMOS survey out to z = 1.3
contains 340 objects, 150 of which have available spectroscopic redshifts, while the remaining sources have reliable photometric redshifts (σ (∆z/(1 + z)) = 0.027; see S07 and
reference therein). Based on Monte Carlo simulations, S07 have shown that the photometric errors in the rest-frame color, used for the selection, introduce a number uncertainty of
∼ 5% in favor of SF galaxies. In this work we use their sample of star forming galaxies,
statistically corrected for this effect.
4.2.2
Derivation of the luminosity function (LF)
We derive the LF (Φ) for our SF galaxies in four redshift bins, chosen to sample approximately equal numbers of sources, using the standard 1/Vmax method (Schmidt 1968). We
limit the accessible volumes a) on the bright end by the minimum redshift out to which an
object could be observed due to the optical saturation limit of i∗ = 16 (AB mag; see also
Capak et al. 2007), and b) on the faint end by the maximum redshift out to which a galaxy
could be observed given the flux limits on both the radio and optical data. In practice,
the latter is dominated by the radio detection limits, rather than the optical, as the major
fraction of the sources used here has i band magnitudes brighter than ∼ 24 (see Fig. 25 in
S07). In the computation of Vmax we take into account the non-uniform rms noise level in
the VLA-COSMOS mosaic. We use the differential visibility area of the VLA-COSMOS
mosaic (i.e. fraction of areal coverage, Ai , vs. rms; for the cumulative representation see
Fig. 13 in Schinnerer et al. 2007), andPfor a source with 1.4 GHz luminosity Lj we compute
Ak
, Lj ).
its maximum volume as Vmax (Lj ) = nk=1 Ak · Vmax (zmax
Several corrections need to be taken into account in order to robustly derive the LF:
i) the VLA-COSMOS detection completeness, ii) the fraction of sources not included in
the radio-optical sample, and iii) the SF galaxy selection bias due to the rest-frame color
uncertainties.
The detection completeness of the VLA-COSMOS survey has been derived by Bondi et al.
(2007) via Monte Carlo – artificial source – simulations for the inner 1◦ . Comparisons
between real and simulated data showed that a power law angular size distribution with
an exponent of 0.5 reproduces the data most accurately (see their Tab. 1). Assuming that
the corrections hold for the full 2◦ we utilize them to correct our LFs for the detection
incompleteness of the VLA-COSMOS survey.
To correct for objects that are not contained within our conservatively defined radiooptical sample due to photometrically flagged regions in the optical images, we construct
a correction curve as a function of total 1.4 GHz flux density using the inverse of the ratio
of sources in the radio-optical sample and all sources that have optical counterparts with
i ≤ 26 (see Fig. 27 in S07). Hence, in the ith luminosity bin the comoving space density (Φi ),
116
4. The dust un-biased cosmic star formation history (CSFH)
and its corresponding error (σi ), are computed by weighting the contribution of each galaxy
by the two correction factors, fdet and fflag , which were obtained by linearly interpolating
the two correction curves described above, respectively, at the total flux density of the
given, jth , source:
v
!2
uN
j
j
j
j
N
uX fdet
X
×
f
fdet × fflag
flag
; σi = t
(4.1)
Φi =
j
j
V
V
max
max
j=1
j=1
We take into account the selection bias caused by the rest-frame color uncertainties
via Monte Carlo simulations. As described in S07 (Sec. 4.2.3.), we iteratively simulate
the error distribution of the synthesized rest-frame color, and in each iteration we perform
the selection of SF galaxies using the rest-frame color based classification method. This
procedure is repeated 300 times, and in each step we derive the LF as described above. In
this way we obtain 300 realizations of (Φi ,σi ) for each luminosity bin. We take the median
values as representative.
4.2.3
The luminosity function
The LFs for our SF galaxies for the 4 chosen redshift bins are shown in Fig. 4.1. In each
panel we show the two locally derived 20 cm LFs for SF galaxies given by Sadler et al.
(2002b, see also Sec. 4.2.4), and Best et al. (2005). There is a good agreement between the
local LFs, and the one derived from the VLA-COSMOS data in the lowest redshift bin (top
left panel in Fig. 4.1). Although the 2◦ COSMOS field samples a small comoving volume
at these redshifts, and a photometric identification of SF galaxies has been used, our LF
in the lowest redshift range agrees remarkably well with the local LFs that were derived
using all-sky surveys (NVSS) combined with good quality spectroscopic data (SDSS, 2dF)
to identify SF galaxies.
At higher redshift we compare our LFs with the total IR LFs derived by Le Floc’h et al.
(2005, hereafter LF05) based on a 24 µm selected sample in the CDFS (Chandra Deep
Field South; top right and both bottom panels in Fig. 4.1). The total IR luminosity was
converted to 1.4 GHz luminosity using the total IR-radio correlation (Bell 2003), which
has an intrinsic scatter of ∼ 0.26 dex (horizontal error bars in Fig. 4.1). The IR LFs were
re-scaled to our redshift ranges either by combining two narrower redshift bins given in
LF05 or by scaling a given comoving density using the evolution parameters, and their
corresponding errors, given in LF05. The final errors of the IR LFs were obtained via error
propagation. There is an excellent agreement between the 1.4 GHz and IR LFs. Note also
that the VLA-COSMOS LFs constrain well the high-luminosity end, i.e. the galaxies with
high SF rates.
4.2.4
The evolution of star forming galaxies
Sadler et al. (2002b) fitted the local 1.4 GHz radio LF by a double-exponential analytic
4.2 The 1.4 GHz luminosity function for star forming galaxies
117
Figure 4.1 1.4 GHz luminosity functions (LFs) for star forming galaxies in the VLA-COSMOS survey,
shown for four redshift ranges (filled blue squares), indicated in each panel. The number of galaxies in
each redshift bin, statistically corrected for selection uncertainties (see text for details), is also indicated in
each panel. The solid (orange) curve and open squares in each panel correspond to the local 20 cm LFs for
star forming galaxies derived by Sadler et al. (2002b) and Best et al. (2005), respectively. In the top left
panel, the local 60 µm LF is also shown (Takeuchi et al. 2003). In the top right, and bottom panels, we
show the IR LFs (LF05) for the corresponding redshift ranges (open circles; see text for details). The total
IR luminosity was converted to 1.4 GHz luminosity using the correlation given in Bell (2003). Note the
good correspondence between the VLA-COSMOS LFs and the independently derived local and IR LFs.
function:
L
Φ(L) = C
L∗
1−α
2 )
L
1
exp − 2 log (1 + )
2σ
L∗
(
(4.2)
with α = 0.84, σ = 0.94, Φ∗ = 22.9 × 10−3 Mpc−3 , and L∗ = 1.95 × 1019 W Hz−1 (scaled
to the cosmology used here; see Hopkins 2004). Comparing our derived LFs (see Fig. 4.1)
118
4. The dust un-biased cosmic star formation history (CSFH)
with the local LF implies strong evolution with look-back time, which is most commonly
parameterized with a monotonic evolution of the local LF in both luminosity and density
as a function of redshift:
L
′
αD
(4.3)
Φ (L, z) = (1 + z) × Φ
(1 + z)αL
where αD and αL are the characteristic density and luminosity evolution parameters, respectively. As the VLA-COSMOS data do not allow the derivation of the LF out to, and
fainter than, L∗ , at this point we cannot break the well known degeneracy between luminosity and density evolution (e.g. Hopkins 2004; LF05). Therefore, in the further analysis
we take only pure luminosity evolution into account (αD = 0).
In Fig. 4.2 we show the luminosity density for our 4 redshift bins. We constrain possible
evolution scenarios in two ways. First, in each redshift bin we separately fit pure luminosity
evolution to our data (dashed curves in Fig. 4.2). Second, we obtain the average pure radio
luminosity evolution of the local 1.4 GHz LF by summing the χ2 distributions obtained
for a large range of fixed αL (αD = 0) in each particular redshift bin. The uncertainty
in αL is then taken to be the 1σ error obtained from the χ2 statistics. The derived
pure radio luminosity evolution of the local 20 cm LF yielded αL = 2.0 ± 0.1 (see solid
curve in Fig. 4.2). Haarsma et al. (2000) have found that a pure luminosity evolution
with αL ≈ 2.74 is a good representation of the evolution of their SF galaxies. However,
no uncertainties were associated with this estimate. Given that their SF galaxy sample
contained 37 galaxies out to z ∼ 2.5 (which corresponds to only ∼ 10% of our sample
which reaches to z = 1.3), we consider that these two results are in rough agreement.
4.3
4.3.1
The cosmic star formation history (CSFH)
The total cosmic star formation history
We convert the 1.4 GHz radio luminosity to star formation rates (ψ) using the calibration given in Haarsma et al. (2000), based on the Condon (1992) model. After the conversion,
we compute the star formation rate density (SFRD) for a given redshift bin as
R
ψ (L) Φ (L, z)′ dL, where Φ′ is the evolved LF (eq. 4.3), and we integrate over the entire
SFRD curve. In Fig. 4.3 we show the CSFH derived using our VLA-COSMOS data fitted
for pure luminosity evolution in each redshift bin separately (thick blue symbols). The
SFRD uncertainties were derived from the 1σ errors of the best fit luminosity evolution
parameter (αL ). It is worth noting that our CSFH uncertainties are significantly smaller
compared to previous radio-based results at high redshifts (Haarsma et al. 2000, grey symbols in Fig. 4.3 at z > 0.2). The main reason for this is that the radio sample of star forming
galaxies used here is almost one order of magnitude larger than the one used previously. In
Fig. 4.3 we also show the local 1.4 GHz LF function evolved only in luminosity using the
obtained average evolution (red curve; see Sec. 4.2.4). Superimposed are also the CSFH results from previous studies based on a range of SF estimators – UV, optical, FIR, and radio.
4.3 The cosmic star formation history
119
Figure 4.2
Luminosity density for
VLA-COSMOS star forming galaxies (filled
squares) in 4 redshift bins.
The dotted
curve and open squares in the top left panel
correspond to the local 1.4 GHz data from
Sadler et al. (2002b) and Best et al. (2005),
respectively. The dashed curve in each panel
represents the best fit pure luminosity evolution to the VLA-COSMOS data in each redshift bin separately using the local LF given
by Sadler et al. (2002b). The solid curve in
each panel shows the most probable pure luminosity evolution obtained by summing the
χ2 distributions obtained in all four redshift
bins (see text for details).
A luminosity-dependent obscuration correction was imposed where necessary (see Hopkins
2004, and references therein). Overall, our derived CSFH agrees with the general trend of
a rapid decline of almost an order of magnitude in the cosmic star formation rate density
since z ∼ 1. Note that if a different radio luminosity to SFR calibration is used, e.g. Bell
(2003), the SFRD would decrease by a factor of ∼ 2, however it would still be consistent
with results from other studies (corrected for dust-obscuration). Our derived CSFH using
the evolved local luminosity function suggests a possibly slower decline of the CSFH than
obtained by the least-squares fit to other wavelength-based data at z < 1, when these are
corrected for luminosity dependent dust-obscuration (log SFRD = 3.29 log (1 + z) − 1.80,
adopted from Hopkins 2004; see dotted line in Fig. 4.3). However, the CSFH derived by
the best fit evolution in our individual redshift bins (thick blue symbols in Fig. 4.3) is
consistent with the best least-squares fit.
4.3.2
The CSFH of massively star forming galaxies
The VLA-COSMOS SF sample constrains well the high end of the LF for SF galaxies.
Given the 2◦ VLA-COSMOS field the comoving volume sampled at z = 1 is ∼ 7 ×
106 Mpc3 , corresponding roughly to half the volume observed locally by SDSS (DR1).
Thus, for the first time this allows a robust derivation of the CSFH for galaxies forming
stars at rates of & 100 M⊙ yr−1 out to z = 1.3. Such radio selected galaxies are equivalents
to ultra-luminous IR galaxies (ULIRGs, LIR > 1012 L⊙ ), and it is noteworthy that the
VLA-COSMOS survey is sensitive to a complete sample of these galaxies out to z ∼ 1 (see
120
4. The dust un-biased cosmic star formation history (CSFH)
Fig. 20 in S07).
In order to derive the evolution of the SFRD at the high-luminosity end, we integrate the
SFRD curve, obtained from the best fit pure radio luminosity evolution in each particular
redshift range (see dashed curves in Fig. 4.2), only for our SF galaxies that have L1.4 &
2 × 1023 W Hz−1 , which corresponds to LIR > 1012 L⊙ given the total IR-radio correlation
(Bell 2003). For easy comparison between our radio and IR (LF05) results the total IR –
radio correlation (Bell 2003) is used which differs by a factor of ∼ 2 from Haarsma et al.
(2000) used in Sec. 4.3.1. Hence, in this way both the radio and IR derived SFRDs, shown
in Fig. 4.4, have been put on the same relative scale. The evolution of our star forming
ULIRGs is consistent with the lower envelope predicted by LF05, although on average
slightly flatter. The larger sample of ULIRGs in the VLA-COSMOS survey (due to the
almost a factor of 3 larger field than the CDFS) yields a better constrained high end of
the LF using the VLA-COSMOS data. Further, particular care was taken to separate the
VLA-COSMOS population into SF and AGN galaxies, while no attempt has been made
to minimize the AGN contamination in the IR sample. Within the assumptions made here
our results are underpinning the findings of Caputi et al. (2007) and Daddi et al. (2007)
on the AGN contamination of MIR samples. Caputi et al. (2007) have found > 10% of
24 µm-AGN at z ∼ 1, and a factor of 2 more at z ∼ 2, implying that the AGN fraction
in MIR samples rises with redshift, while Daddi et al. (2007) have demonstrated that, at
least at z ∼ 2, the MIR AGN fraction is a function of stellar mass, and reaches ∼ 50 − 60%
for masses > 4 × 1010 M⊙ . Our findings are consistent with a lower fraction of massively
star forming galaxies in MIR selected samples than often assumed.
4.4
Summary
We have derived the cosmic star formation history out to z = 1.3 using to date the largest
sample of radio-selected star forming galaxies observed at 1.4 GHz (20 cm) in the VLACOSMOS survey. The almost one order of magnitude increase in the number of radio
selected SF galaxies out to high redshift, compared to previous studies (Haarsma et al.
2000), allowed to precisely constrain the evolution of the 1.4 GHz luminosity function for
radio-selected star forming galaxies, as well as to significantly reduce the uncertainties of
the radio-derived CSFH. We find that a pure radio luminosity evolution of VLA-COSMOS
star forming galaxies is well described with L∗ ∝ (1 + z)2.0±0.1 . Our overall CSFH agrees
well with past findings, when these are corrected for dust-obscuration where needed. This
verifies the assumptions about large dust-obscuration corrections required, especially for
short-wavelength (e.g. UV) star formation tracers. Making use of our large statistical
sample of radio-selected star forming ULIRGs complete out to z ∼ 1 we have robustly
constrained the high-end of the SF galaxy LF at different cosmic times. Using these we
have derived the CSFH of the most intensely star forming galaxies (& 100 M⊙ yr−1 ; i.e.
star forming ULIRGs) out to z = 1.3. We find an, on average, slower evolution of the
cosmic star formation rate in star forming ULIRGs than predicted by MIR results (LF05)
suggesting that the fraction of star forming galaxies in MIR samples is, likely, becoming
4.4 Summary
121
Figure 4.3 Star formation rate density as a function of redshift derived from 1.4 GHz VLA-COSMOS
data (thick blue symbols). We also show the cosmic star formation history (CSFH) obtained by evolving
the local star forming LF (Sadler et al. 2002b) using our derived average pure luminosity evolution (αL =
2.0 ± 0.1; red curve). For comparison, the compilation of CSFHs, based on UV, Hα, FIR, and X-ray
measurements, presented in Hopkins (2004), is also shown (light-grey squares). All have been corrected
for dust-obscuration using luminosity-dependent corrections (see Hopkins 2004 for details). The dotted
line is the best least-squares fit to the CSFH points at z < 1 obtained by Hopkins (2004); log SFRD =
3.29 log (1 + z) − 1.80. Dark-grey symbols denote other radio estimates (Machalski & Godlowski 2000;
Condon et al. 2002; Sadler et al. 2002b; Serjeant et al. 2002 for z < 0.2; Haarsma et al. 2000 for z > 0.2).
For all radio data the 1.4 GHz luminosity to star formation rate calibration given in Haarsma et al. (2000)
is used, based on the model developed in Condon (1992). Note that such radio-derived CSFHs may be a
factor of ∼ 2 lower given the calibration uncertainties (see text for details; see also Appendix B.2).
122
4. The dust un-biased cosmic star formation history (CSFH)
lower with redshift.
Figure 4.4 Cosmic star formation history derived from VLA-COSMOS data for the overall population
(dark-blue symbols, red curve as in Fig. 4.3), and only for the star forming ULIRGs (light-blue symbols).
We also indicate the evolved star formation rate density for the entire (light-grey shaded curve), and
the ULIRG population (dark-grey shaded curve), derived from the evolved total IR luminosity function
(LF05). Note that the IR- and radio-based star formation rates (SFRs) are put here to the same relative
scale using the IR luminosity to SFR, and the 1.4 GHz luminosity to SFR, calibrations given in Bell (2003);
see text for details.
Chapter 5
A wide angle tail radio galaxy in the
COSMOS field: evidence for cluster
formation
In this Chapter an in-depth panchromatic study of a peculiar radio galaxy – the wide angle
tail radio galaxy CWAT-01, and its complex, merging cluster environment is presented.
This work has been published in the COSMOS ApJS special issue as:
V. Smolčić, E. Schinnerer, A. Finoguenov, I. Sakelliou, C. L. Carilli, et al., 2007, ApJS,
172, 295, A Wide-Angle Tail Radio Galaxy in the COSMOS Field: Evidence for Cluster
Formation
and has been awarded with the Ernst Patzer Award 2006 for best refereed publications by
young MPIA scientists.
Abstract
We have identified a complex galaxy cluster system in the COSMOS field via a wide angle
tail (WAT) radio galaxy consistent with the idea that WAT galaxies can be used as tracers
of clusters. The WAT galaxy, CWAT-01, is coincident with an elliptical galaxy resolved in
the HST-ACS image. Using the COSMOS multiwavelength data set, we derive the radio
properties of CWAT-01 and use the optical and X-ray data to investigate its host environment. The cluster hosting CWAT-01 is part of a larger assembly consisting of a minimum
of four X-ray luminous clusters within ∼ 2 Mpc distance. We apply hydrodynamical models that combine ram pressure and buoyancy forces on CWAT-01. These models explain
the shape of the radio jets only if the galaxy’s velocity relative to the intra-cluster medium
(ICM) is in the range of about 300 − 550 km s−1 which is higher than expected for brightest cluster galaxies (BCGs) in relaxed systems. This indicates that the CWAT-01 host
cluster is not relaxed, but is possibly dynamically young. We argue that such a velocity
could have been induced through subcluster merger within the CWAT-01 parent cluster
and/or cluster-cluster interactions. Our results strongly indicate that we are witnessing
the formation of a large cluster from an assembly of multiple clusters, consistent with the
124
5. Wide angle tail galaxy in the COSMOS field
hierarchical scenario of structure formation. We estimate the total mass of the final cluster
to be approximately 20% of the mass of the Coma cluster.
5.1
Introduction
Wide-angle tail (WAT) galaxies form a class of radio galaxies, usually found in clusters,
whose radio jets have been bent into a wide C shape. The general morphology of WATs suggests that the sources interact significantly with their external environment. The most natural interpretation of the jet bending is that the jets are being swept back by ram pressure
resulting from the high velocity motion of the associated active elliptical galaxy through its
surrounding intra-cluster medium (ICM), first developed by Begelman, Rees, & Blandford
(1979) and applied by a number of investigators (e.g. Burns 1981; Pinkney et al. 1994). In
addition to ram-pressure, buoyancy forces were introduced to explain the bending of the
jets (e.g. Gull & Northover 1973; Sakelliou et al. 1996). If the jet density is lower than the
density of the surrounding medium, buoyancy forces will drag the jets towards regions of
the ICM where the densities are equal.
A point first noticed by Burns (1981) was that WATs are usually associated with brightest cluster galaxies (BCG; D or cD galaxies), which are expected to reside at rest at the
bottom of the clusters’ gravitational potential well (Quintana & Lawrie 1982; Merritt 1984;
Bird 1994). Thus the large velocities of the WAT host galaxies relative to the ICM needed
for the ram pressure models to shape the jets seemed to be inconsistent with velocities typical for BCGs.1 Therefore, it was necessary to evoke alternative scenarios to explain the
bent shape of WAT galaxies (e.g. Eilek 1979). However, the most prominent explanation is
that the jets are bent by ram pressure. It has been suggested in numerous studies that the
necessary ram pressure may be provided during cluster mergers (e.g. Pinkney et al. 1994;
Loken et al. 1995; Gomez et al. 1997; Sakelliou et al. 1996; Sakelliou & Merrifield 2000).
This merger scenario is consistent with cosmological models, such as the cold dark matter
model (CDM), which propose that the structure in the Universe is formed hierarchically
with large features forming from mergers of smaller groups. The cluster potential well
deepens then through accretion of poor clusters, dark matter and gas into more massive
systems. The material is accreted from supercluster filaments which connect clusters into
the large-scale structure of the Universe (Evrard 1990; Jing et al. 1995; Frenk et al. 1996;
Burns et al. 2002).
Based on ROSAT PSPC X-ray observations of a sample of 9 Abell clusters containing
WAT galaxies, Gomez et al. (1997) find evidence for statistically significant X-ray substructure in 90% (8 out of 9) of the clusters hosting WATs, as well as a strong correlation of the
orientation of the jets and the direction of X-ray elongation within the core of the cluster.
Combined with numerical hydro/N-body simulations their results are consistent with WAT
1
Malumuth et al. (1992) have shown that the velocity dispersion of the cD population is 0.3 of the
dispersion of the cluster population; Beers et al. (1995) have found a velocity difference between the
peculiar velocity of the central galaxy and the mean of the rest of the cluster galaxies . 150 km s−1 ;
recently Oegerle & Hill (2001) , analyzing 25 Abell clusters, showed that peculiar velocities of cD galaxies
differ only by ∼ 160 km s−1 from the mean cluster velocities.
5.2 Observations and data reduction
125
clusters undergoing mergers with groups or subclusters of galaxies. Sakelliou & Merrifield
(2000) show that WATs are not generally located at the centers of their host clusters as
defined by their X-ray emission. They also find that the orientation of the bent jets is
found to be preferentially pointed directly towards or away from the cluster center. Thus,
if the morphology is due to ram pressure, WATs are then primarily on radial orbits through
the cluster. These results are explained as a natural consequence of cluster mergers creating WAT galaxies (for details see Sakelliou & Merrifield 2000 and references therein).
Blanton et al. (2001) present optical imaging and spectroscopic observations of environments surrounding 10 bent radio sources. They find that the clusters display a range of
line-of-sight velocity dispersions, σ|| , from about 300 − 1100 km s−1 . The upper limit of
σ|| suggests that the host clusters are either massive clusters and/or merging systems with
significant substructure.
Since WATs are usually found in cluster environments, they can be used as an efficient
tool for cluster search, especially for high-redshift clusters where we are biased by the
dimming of galaxies in the optical and the ICM in X-ray emission. This approach has
been successfully tested by Blanton et al. (2000, 2001, 2003) using the VLA FIRST survey
(Faint Images of the Radio Sky at Twenty centimeters survey; Becker, White, & Helfand
1995) to search for galaxy clusters via WAT galaxies. The highest redshift cluster they
have identified to date is at z = 0.96 Blanton et al. (2003).
In this paper we discuss the properties of a WAT radio galaxy (hereafter CWAT-01)
found in the VLA-COSMOS 2◦ survey, previously detected, but not resolved by the NVSS
(NRAO VLA Sky Survey; Condon et al. 1998) survey and not detected in the VLA FIRST
survey (Becker, White, & Helfand 1995). The multiwavelength data set of the COSMOS
survey (Scoville et al. 2007a) enables us to use the radio data to derive the properties of
this radio galaxy and the optical/X-ray data to investigate its host environment. In Sec. 5.2
we present the data utilized here. Sec. 5.3 describes the radio and optical properties of
CWAT-01. In Sec. 5.4 we introduce the cluster and independently analyze its properties
in the X-ray and the optical. We discuss the results in Sec. 5.5 and summarize them in
Sec. 5.6.
For calculations in this paper, we assume H0 = 70, ΩM = 0.3, ΩΛ = 0.7. We define the
synchrotron spectrum as Fν ∝ ν −α , with a positive spectral index, α > 0, throughout the
paper.
5.2
5.2.1
Observations and data reduction
Radio data
The 2 ◦ COSMOS field was observed at 1.4 GHz with the NRAO Very Large Array
(VLA) in A- and C- configuration for a total time of 275 hours (VLA-COSMOS survey;
Schinnerer et al. 2007, Chap. 2). The final 2 ◦ map has a typical rms of 10.5 (15) µJy/beam
in the inner 1 (2) ◦ with a resolution of 1.5′′ × 1.4′′ , thus making it the largest contiguous
area covered in the radio-wavelengths regime with such a high sensitivity. The VLA-
126
5. Wide angle tail galaxy in the COSMOS field
COSMOS large project catalog (presented in Schinnerer et al. 2007, Chap. 2) contains
∼ 3600 radio sources, ∼ 90 of which are clearly extended (most of them are double-lobed
radio galaxies). The sensitivity of the survey combined with the high resolution and the
large area coverage makes the VLA-COSMOS project extremely valuable for e.g. studies of the sub-mJy radio population (i.e. the faint end of the radio luminosity function),
dust-obscured star-formation history of the universe, evolution of radio-loud active-galactic
nuclei (AGN). The survey utilized the standard VLA L-band continuum frequencies and
the multi-channel continuum mode. The complete data reduction was performed using the
standard VLA software AIPS. The A- and C-array data were combined in the uv plane and
then imaged using the task IMAGR. Cleaning boxes around bright sources were defined
manually, the two intermediate frequencies (IFs) and the left and right polarization were
imaged separately and then combined into the final map. For more information about the
survey and its scientific objectives see Schinnerer et al. (2007, Chap. 2). The local rms
noise in the mosaic around CWAT-01 is ∼ 10.5 µJy/beam.
Subsequent observations of CWAT-01 were obtained in June 2005 with the VLA in
CnB configuration at 4.8 GHz. The standard C-band continuum frequencies were used
and the observations were performed in the standard continuum mode. The CWAT-01
field was observed for 40 minutes on-source. After flux and phase calibration the data set
was imaged using the AIPS task IMAGR. The IFs and the left and right polarization were
imaged together. Clean boxes were defined manually, the number of CLEAN iterations in
IMAGR was set to 3000 and the flux cut-off to 60 µJy. The uv data points were weighted
using the natural weighting function (UV W T F N was set to ’N’). Due to the asymmetric
uv coverage the resolution of the 4.8 GHz map was tapered down to obtain a rounder beam
(the UVTAPER option in IMAGR was set to 35 kλ). The resolution and rms noise in the
tapered map are 7.27′′ × 5.53′′ and ∼ 40 µJy/beam, respectively.
5.2.2
X-ray data
The COSMOS 2◦ field is being observed by the XMM-Newton satellite (Jansen et al.
2001) for an awarded time of 1.4 Msec (Hasinger et al. 2007). The data collected to date
amount to 0.8 Msec over the 2◦ area with an effective depth of ∼ 40 ksec, taking vignetting
into account. In this study we utilize some of the results of Finoguenov et al. (2007)
who identify clusters in the COSMOS field via diffuse X-ray emission. The flux limit
for the cluster identification in the 0.5 − 2 keV energy band is 2 × 10−15 erg cm−2 s−1 .
Finoguenov et al. (2007) report four diffuse structures within 4′ of CWAT-01, one of them
containing CWAT-01.We perform the spectral analysis of the extended emission associated
with the four identified clusters in the following way. The EPIC pn observations of the
COSMOS field have been merged together and a uniform cleaning criterion for background
flares has been subsequently applied. Observations that do not satisfy this criterion are
removed. In this way we achieve a uniform background level for the clean dataset. The
resulting file consists of a total of 365 ksec homogeneously cleaned data (for the whole
2◦ field). However, the region around CWAT-01 is still covered at an effective depth of
43 ksec as the removed observations are all located at the edge of the COSMOS field.
5.2 Observations and data reduction
127
Since the instrumental background is not uniform over the detector, in order to estimate
the background, we produce a background file from the same merged event file by excluding
the area containing a detected X-ray source. This removes ∼ 20% of the area, which we
account for in correcting for the background. The background is further assumed to be the
same in detector coordinates. In calculating the background spectrum, the sky position of
the cluster is mapped to the detector, taking into account multiple pointings, which map
the same region of the sky on different detector areas. The background spectrum is collected
weighting accordingly the contribution in each detector pixel. For the clusters in this study
the ratio of the background to signal accumulation times is 5, which is sufficient to reduce
the statistical uncertainty associated with the background subtraction. The calculation
of auxiliary response files is performed by the SAS-based task clarf of Finoguenov et al.
(2004), which takes the mosaicing into account. The pn calibrations involved in the data
reduction correspond to SAS (Science Analysis Software; Watson et al. 2001) version 6.5.
5.2.3
Optical data
The optical imaging data of the 2◦ COSMOS field, we use in this paper, was obtained in
Spring 2004 and 2005. Within the COSMOS HST Treasury project the 2◦ field was imaged in 590 orbits during Cycles 12 and 13 using the Advanced Camera for Surveys (ACS;
Scoville et al. 2007c). The F814W band imaging has a 0.07′′ resolution and a 10σ sensitivity of IAB = 27.2. Each of the 590 fields consists of 4 exposures, which are calibrated
and combined into a single image for each field using the MultiDrizzle software (for details
see Koekemoer et al. 2007) The whole COSMOS field was imaged with the Suprime-Cam
camera (SUBARU telescope) in 6 broad band filters, BJ , VJ , g + , r + , i+ , z + with 5σ sensitivity in AB magnitudes of 27.3, 26.6, 27.0, 26.8, 26.2, 25.2, respectively (Taniguchi et al.
2007; Capak et al. 2007). u∗ and i∗ band images of the whole 22 COSMOS field were
obtained with the CFHT (Canada France Hawaii Telescope). The 5σ sensitivity in AB
magnitudes is 26.4 and 24.0 for u∗ and i∗ , respectively.
The COSMOS photometric catalog was constructed using the SUBARU i+ band image.
The details on constructing the photometric redshift catalog are described in Mobasher et al.
(2007). The catalog produces photometric redshifts accurate to dz/(1 + z) = 0.034. We
utilize these redshifts to study the optical (sub)structure of the clusters in latter sections.
5.2.4
Redshift
Trump et al. (2007) present spectroscopic redshifts of the first ∼500 X-ray and radio selected targets in the 2◦ COSMOS field. The spectra were obtained using the Magellan
IMACS instrument. They also perform a robust classification of the observed objects (for
details see Trump et al. 2007 and references therein). In the cluster area around CWAT01 (see Sec. 5.4.2) there are two galaxies that have IMACS spectra. Their properties,
classification and redshifts are reported in Tab. 5.1.
Searching for high resolution (R = 1800) Sloan Digital Sky Survey (SDSS, York et al.
2000; Abazajian et al. 2003, 2004, 2005; Adelman-McCarthy et al. 2006) spectra, we find
128
5. Wide angle tail galaxy in the COSMOS field
Table 5.1 IMACS spectra in cluster assembly region.
name
COSMOS J100021.81+022328.5∗
COSMOS J100025.30+022522.5
RA
10 00 21.816
10 00 25.298
DEC
+02 23 28.523
+02 25 22.476
type
elliptical
elliptical
zspec ± ∆zspec
0.22067 ± 0.00007
0.22090 ± 0.00013
∗
Corresponds to SDSS J100021.81+022328.46 (see Tab. 5.2)
Specifications of galaxies within the cluster area that have IMACS spectra (for details see Trump et al.
2007). The last column specifies the spectroscopic redshift and error.
Table 5.2 SDSS spectra in cluster assembly region.
name
SDSS J10004.35+022550.71
SDSS J10006.65+022225.98
SDSS J100021.81+022328.46∗
plate
501
501
501
fiber
348
353
388
RA
10 00 4.354
10 00 6.654
10 00 21.815
DEC
+02 25 50.711
+02 22 25.982
+02 23 28.463
zspec ± ∆zspec
0.2201 ± 0.0001
0.2221 ± 0.0002
0.2206 ± 0.0001
∗
Corresponds to COSMOS J100021.81+022328.5 (see Tab. 5.1)
SDSS specifications of galaxies within the cluster area that have SDSS spectra. The last column specifies
the spectroscopic redshift and error.
that 3 galaxies in our cluster area around CWAT-01 have SDSS spectra. One of them was
observed with IMACS. The specifications of these galaxies are listed in Table 5.2. It is
worth noting that the galaxies with spectroscopic redshifts are the most prominent galaxies
within each cluster (see Sec. 5.4).
The mean spectroscopic redshift of the galaxies presented above is 0.2209 with an
accuracy of 2.8 × 10−4 . Given the dispersion of the photometric redshift values they are
compatible with a redshift of z = 0.22. Therefore, for the scope of this paper we adopt a
mean cluster redshift of z = 0.22, based on four measured spectroscopic redshifts.
5.3
The wide angle tail galaxy: CWAT-01
The radio galaxy first resolved in the VLA-COSMOS survey, CWAT-01, has a morphology
typical for wide angle tail (WAT) galaxies. Its radio jets are bent into a wide C shape
(see Fig. 5.1 for example). In Sec. 5.3.1 we describe the structure of CWAT-01 and derive
its radio properties in order to investigate the correlation to its host environment in latter
sections. In Sec. 5.3.2 we describe the optical properties of the CWAT-01 host galaxy.
5.3.1
Radio properties of CWAT-01
The structure of CWAT-01
The radio galaxy discussed here was first detected in the NVSS survey (NVSS J100027+022104;
Condon et al. 1998), but not resolved due to the low resolution of the NVSS (45”). It was
first resolved in the 1.4 GHz mosaic of the central 1◦ field from the VLA-COSMOS pilot
project (Schinnerer et al. 2004). CWAT-01 was not detected in the FIRST survey as this
survey over-resolves radio sources larger than ∼ 10′′ (and thus underestimates their fluxes;
see Becker et al. 1995 for details). Hence, a galaxy like CWAT-01, which extends over more
5.3 The wide angle tail galaxy: CWAT-01
129
than 1′ on the plane of the sky and has a total flux density of ∼ 13 mJy (see below), would
be strongly resolved out in the FIRST survey, i.e. the galaxy would consist of multiple
components and the flux from the extended regions (& 10′′ ) of each component would be
missed. The individual parts of CWAT-01 that could have been detected by FIRST have
flux densities below the detection limit of the FIRST survey (1 mJy). Thus, CWAT-01 or
any fraction of the galaxy stays undetected in the FIRST survey.
45’’
30’’
Declination
15’’
21’ 00’’
45’’
30’’
02o 20’ 15’’
10h 00m 30s
28s
26s
Right Ascension
24s
Figure 5.1 1.4 GHz radio map of the wide angle tail galaxy CWAT-01 in the COSMOS field in grey scale
with contours overlaid. The contour levels are in steps of 2σ starting at the 2σ level (1σ = 10.5 µJy/beam).
The clean beam is shown in the lower left corner (the resolution is 1.5′′ × 1.4′′ ). C0, E1 − 2 and W 1 − 3
label features of the jets discussed in the text. The colorbar units are in Jy/beam.
Our new VLA-COSMOS observations, as part of the VLA-COSMOS large project
(Schinnerer et al. 2007, Chap. 2), at 1.4 GHz provided significantly better data of this
extended radio galaxy. The new 1.4 GHz VLA-COSMOS map of CWAT-01 is shown in
Fig. 5.1 (the resolution is 1.5′′ × 1.4′′ ). The radio jets of the galaxy are curved in a C shape
typical for WAT galaxies. The central radio peak of CWAT-01 is at α = 10 00 27.35 and
δ = +02 21 24.15 (J2000) and its host galaxy is an elliptical galaxy with a photometric redshift of z = 0.2±0.03. The photometric redshift was taken from the COSMOS photometric
130
5. Wide angle tail galaxy in the COSMOS field
redshift catalog described in detail in Mobasher et al. (2007). The quoted error is the 1σ
error obtained from the 95% confidence interval. The optical counterpart is discussed in
more detail in Sec. 5.3.2. The jets are barely resolved in width (i.e. perpendicular to the
jet axis) out to points E1 and W1 (see Fig. 5.1). The Eastern radio jet can be traced out to
a projected distance of ∼ 1′ (∼ 210 kpc) from the central galaxy. At point E1 it bends to
the west (in the projected plane), and broadens. The end of the jet is marked with E2 (see
Fig. 5.1). The structure of the Western jet is more complex: It extends to a distance of
∼ 45′′ (∼ 160 kpc) in the plane of the sky. From the core till W1 it is narrow, but indicates
curvature in the northern part. After W1 the jet broadens and bends slightly to the west
in the projected plane (W2). The faint feature labeled as W3 in Fig. 5.1 seems also to be
part of CWAT-01. The integrated flux density of CWAT-01 at 1.4 GHz is 12.69 mJy. This
is within the errors of the reported NVSS flux density of 13.5 ± 1.9 mJy. For consistency
we will use the flux density derived from the VLA-COSMOS for calculations throughout
the paper.
The bending angle of the jets, which we define as the angle between lines parallel to the
part of the jet closest to the core (see Fig. 5.11, bottom panel), is ∼ 100◦ . The asymmetry
of the jets may be due to projection effects which would indicate that the whole structure
is not moving only in the plane of the sky. However, with the data in hand we cannot
reach any firm conclusions about projection effects nor rule them out. More radio bands
and higher resolution radio data where relativistic core beaming effects could be explored
might resolve this issue; i.e. on ∼ 10 kpc scales from the radio core of the galaxy (where
the bulk motion of the particles is relativistic) relativistic effects yield that the ratio of the
radio brightness of the two jets can be correlated with the orientation angle.
Spectral index
The 4.8 GHz map at 7.27′′ × 5.53′′ resolution is shown in Fig. 5.2. The main features seen
in the 1.4 GHz map (with a resolution of 1.5′′ × 1.4′′ ; Fig. 5.1) are still apparent although
the much lower resolution reduces the amount of details. The total integrated flux at 4.8
GHz is 4.1 mJy. To obtain the spectral index map (shown in Fig. 5.3), the 1.4 GHz image
was convolved to the resolution of the 4.8 GHz map and the two images were regridded to
the same pixel scale. Pixels with values below 3σ in each map were blanked. The middle
of the central feature is a flat spectrum region (0.1 . α . 0.3), with the spectral index
steepening to α ∼ 1 to the north-west edge in this feature. The outer region (corresponding
to E1) with a flat spectrum (0.2 . α . 0.6) suggests possible re-acceleration regions. The
spectral index steepens to α ∼ 1 at E2. In W2 the spectrum emission is on average steeper
than in the E1 feature, with an average spectral index corresponding to α ∼ 0.7. The mean
spectral index in the total source is α = 0.6 which we will use for calculations throughout
this paper, unless mentioned otherwise.
5.3 The wide angle tail galaxy: CWAT-01
131
45’’
30’’
Declination
15’’
21’ 00’’
45’’
30’’
02o 20’ 15’’
10h 00m 30s
28s
26s
Right Ascension
24s
Figure 5.2 4.8 GHz radio map of CWAT-01 with contours overlaid. The contour levels are in steps of
2σ starting at the 3σ level (1σ = 40 µJy/beam). The clean beam is shown in the lower left corner (the
resolution is 7.27′′ × 5.53′′ ). The colorbar units are in Jy/beam.
Radio power and luminosity
We compute the radio power of CWAT-01 at 1.4 GHz using a spectral index of α = 0.6
(see Sec. 5.3.1) and the luminosity distance (1.1 Gpc) at z = 0.22. The radio power of
CWAT-01 is then P1.4 = 2.0 × 1024 W Hz−1 which places the radio galaxy between FRIs
and FRIIs where WATs are normally found (Hardcastle & Sakelliou 2004).
We also calculate the total radio luminosity, given by (e.g. O’Dea & Owen 1987):
2
Ltot = 1.2 × 1027 DL,[Mpc]
F0 ν0α (1 + z)−(1−α) ×
(1 − α)−1 (ν21−α − ν11−α ) [erg s−1 ]
(5.1)
where DL,[Mpc] is the luminosity distance expressed in Mpc and F0 the flux density, at
a fiducial frequency ν0 , expressed in Jy. We take the lower and upper frequencies to be
ν1 = 10 MHz and ν2 = 100 GHz, respectively. The observed frequency is ν0 = 1.4 GHz.
132
5. Wide angle tail galaxy in the COSMOS field
Figure 5.3 Spectral index map of CWAT-01 for pixels with values > 3σ in both maps (1.4 GHz and
4.8 GHz). C0, E1-2, W1-3 are labeled as in Fig. 5.1 and are presented here for clarity. We define the
spectral index as Fν ∝ ν −α throughout the paper. The contour levels are 0.1, 0.2, 0.3 etc.
The total luminosity of CWAT-01 is then Ltot = 3.2×1041 erg s−1 , typical for peculiar radio
galaxies (Pacholczyk 1970).
Magnetic field and minimum pressure
Assuming that the total energy in a radio galaxy is the sum of the energy of electrons, Ee ,
heavy particles, Ep , and the magnetic field, EB , Etot = Ee + Ep + EB , we can estimate the
minimum energy density, ume , and the corresponding magnetic field, Bme , using the minimum energy condition which corresponds almost to equipartition between the relativistic
particles and the magnetic field.2 We adopt the expression from Miley (1980, for details
2
Equipartition requires that the magnetic energy is equal to the total particle energy, i.e. EB = Ee +Ep ,
while the minimum energy condition holds for EB = 34 (Ee + Ep ). Hence, the computed total energy,
Etot = Ee + Ep + EB , agrees within ∼ 10% for the first and latter.
5.3 The wide angle tail galaxy: CWAT-01
133
see Pacholczyk 1970):
2
7 Bme
[dyn cm−2 ]
3 8π
−5 1 + k
= 5.69 × 10
(1 + z)3+α ×
η
(5.2)
ume =
Bme
1/2−α
1
F0 ν
· α 2
3/2
Θx Θy l sin Φ ν0
1
2
1/2−α
− ν1
−α
#2/7
[G]
(5.3)
where k is the ratio of relativistic proton to relativistic electron energies, η is the filling
factor of the emitting region, z is the redshift, Θx and Θy correspond to the clean beam
widths, l is the pathlength through the source along the line of sight, Φ is the angle
between the uniform magnetic field and the line of sight, F0 is the flux density at a fiducial
frequency ν0 , ν1 and ν2 are the lower and upper frequency cutoffs and α is the spectral
index. The minimum energy density and the corresponding magnetic field were measured
in the middle of the diffuse portion of the Eastern radio jet in the 1.4 GHz map with the
following assumptions: a) the radio plasma fills the volume completely (η = 1), b) the
magnetic field is transverse to the line of sight (sin Φ = 1), c) the relativistic proton energy
equals the relativistic electron energy (k = 1), d) there is cylindrical symmetry, and e) the
radio spectrum spans from 10 MHz to 100 GHz. The mean spectral index derived from the
spectral index map in this part of the jet corresponds to α = 0.65. The resulting magnetic
field is Bme = 3.7 µG and the minimum energy density is ume = 1.3 × 10−12 dyn cm−2 . The
minimum internal pressure within the jets is then Pmin = ume /3 = 4.3 × 10−13 dyn cm−2 .
The particle lifetime
The synchrotron age of the electrons at frequency ν is given by van der Laan & Perola
(1969) assuming the following model: The electrons age as a result of synchrotron and
inverse Compton losses due to the interaction with the cosmic microwave background
(CMB). There is a brief “generation phase”, during which the relativistic gas is presumably
created by the active galaxy, and a long-term “remnant phase” during which the particle
supply is switched off. The model computes the lifetime of the “remnant phase” as it
assumes that the lifetime of the “generation phase” is much shorter:
t ∼ 2.6 × 104
B 1/2
(B 2 + BR2 )[(1 + z)ν]1/2
[yr]
(5.4)
where B is the magnetic field in the jet and BR is the equivalent magnetic field of the CMB
radiation, BR = 4[1 + z]4 µG. In order to constrain the electron lifetime, we substitute
into eq. [5.4] the magnetic field corresponding to the minimum energy condition, B =
Bme , calculated for the region at the end of the radio jets at 1.4 GHz (E2 and W2 in
Fig. 5.1). The magnetic field Bme is again derived making the same assumptions as in
Sec. 5.3.1. The mean spectral index at the end of the Eastern and Western jet corresponds
134
5. Wide angle tail galaxy in the COSMOS field
to 0.9 and 0.7, respectively, and the minimum-energy magnetic fields are then 3.5 µG and
3.1 µG, respectively. Both values give the lifetime of an electron radiating at 1.4 GHz of
∼ 13 Myr. Therefore, if we assume that there is no particle re-acceleration within the jets,
the relativistic electrons created in or near the core could travel the whole jet length within
their lifetime with bulk velocities in the range of about (0.04 − 0.05)c.
45’’
26’’
30’’
Declination
Declination
15’’
21’ 00’’
24’’
22’’
45’’
02o 21’ 20’’
02o 20’ 30’’
10h 00m 30s
29s
28s
27s
Right Ascension
26s
25s
10h 00m 27.70s 27.60s
27.50s
27.40s
Right Ascension
27.30s
27.20s
Figure 5.4 Top panel: HST-ACS FW814 band (grey scale) image of the CWAT-01 host galaxy with
1.4 GHz radio contours overlaid. The contour levels are in steps of 2σ starting at the 3σ level (1σ =
10.5 µJy/beam). The dashed box indicates the region shown in the bottom panel. Bottom panel: HSTACS FW814 band image of the CWAT-01 host galaxy. The galaxy has a morphology of an elliptical
galaxy.
5.3.2
The host galaxy
CWAT-01 is coincident with an elliptical galaxy, shown in Fig. 5.4, located at α = 10 00 27.43
and δ = +02 21 23.62 (J2000). The spectral energy distribution (SED) type, reported in
the COSMOS photometric redshift catalog (Mobasher et al. 2007; Capak et al. 2007) is
1.33 (ellipticals and Sa/Sb correspond to 1 and 2, respectively; see Coleman et al. 1980;
Kinney et al. 1996). The photometric redshift of the galaxy is z = 0.2 ± 0.03 (the quoted
error is the 1 σ error obtained from the 95% confidence interval). We construct the surface
brightness profile of the host galaxy (shown in Fig. 5.5) using the GIPSY ellipse fitting task
ELLFIT on the background-subtracted HST-ACS F814W band image (Koekemoer et al.
2007). The surface brightness follows the r 1/4 law fairly well, but it deviates from it
in the outer parts, indicating an excess in surface brightness, possibly an extended halo
(see dashed line in Fig. 5.5). The early type morphology, the extensive envelope and the
shallower surface brightness profile compared to the r 1/4 law suggest that the CWAT-01
5.4 The clusters
135
host galaxy might be classified as a D type galaxy (e.g. Beers & Geller 1983). To obtain
1/n
a better fit to the surface brightness, we fit the Sersic model I(r) = Ieff ebn [1−(r/reff ) ] ,
bn ∼ 2n − 0.324, (Sersic 1968) to the data with effective radius, reff , effective intensity,
Ieff , and Sersic index, n, as free parameters (solid line in Fig. 5.5). The data is very well
fit by the Sersic law with n = 5.0, reff = 8.2 kpc and an effective surface brightness of
µeff = −2.5 log Ieff − 48.6 = 22.1 mag arcsec−2 . It has already been noted by Schombert
(1987) that intrinsically bright ellipticals are flatter (i.e. have higher values of n) and
that intrinsically faint ellipticals have more curvature (i.e. lower values of n) than predicted by the r 1/4 law. Typical values of n for brightest cluster galaxies (BCGs) are n > 4
(Graham et al. 1996). The effective radius and the Sersic index of the CWAT-01 host
galaxy make it consistent with being a BCG at the low end of the n vs. reff correlation for
BCGs (for details see Graham et al. 1996). In Sec. 5.4.2 we show that the CWAT-01 host
galaxy is indeed the brightest galaxy in its parent cluster.
Figure 5.5 Surface brightness profile
of the optical counterpart of CWAT01. The surface brightness is expressed
in AB95 magnitudes and was derived
from the HST-ACS F814W band image (see text for details). The dots are
the data points with 1σ error-bars. The
de Vaucouleurs fit (dashed line) reveals
an excess in surface brightness in the
outer parts of the galaxy while the Sersic law fits the profile very well (solid
line). The effective surface brightness,
effective radius and Sersic index for the
redshift of z = 0.22 are listed in the
panel.
5.4
The clusters
WATs are normally found in cluster environments and are in general associated with the
brightest cluster galaxy (BCG). The X-ray image of the field around CWAT-01 (described
in Sec. 5.2.2) showed the presence of multiple extended X-ray sources around the WAT.
Additionally, in the previous section we have shown that the CWAT-01 host galaxy has
136
5. Wide angle tail galaxy in the COSMOS field
the characteristics of BCGs. Taking advantage of the availability of the COSMOS multiwavelength data set we investigate the nature and properties of CWAT-01’s environment
in this section.
The whole cluster structure detected via diffuse X-ray emission is part of the largescale structure component, LSS #17, reported by Scoville et al. (2007b). The diffuse Xray emission shows substructure itself. Throughout the paper we will use the following
nomenclature. We refer to the whole area identified via diffuse X-ray emission as cluster
assembly. The cluster assembly encompasses four subclumps (i.e. four individual diffuse Xray emitting regions), which we call clusters or poor clusters, and is embedded in LSS #17;
see for example Fig. 5.6 (top panel).
5.4.1
X-ray properties
The search for extended X-ray sources in the COSMOS 2 ◦ field (for details see Finoguenov
et al. 2007) reveals 4 diffuse sources within 4′ radius of CWAT-01. Fig. 5.6 (top panel)
shows a part of the X-ray image in the 0.5−2 keV band encompassing the cluster assembly.
In the middle panel of Fig. 5.6 we display the same area in the sky, but from the wavelet
reconstruction of the 0.5 − 2 keV band image which was presented in Finoguenov et al.
(2007) and utilized for their cluster search. CWAT-01 is located in the south-eastern cluster
(hereafter CWAT-01 parent cluster).
Finoguenov et al. (2007) assign redshifts to the identified diffuse X-ray sources by analyzing redshift bins of width ∆z = 0.2 using the COSMOS photometric redshift catalog
(Mobasher et al. 2007). Three of the four diffuse X-ray sources described here are associated with a large galaxy concentration in the same redshift bin with the median photometric
redshifts of the clusters of 0.22 (Finoguenov et al. 2007). A description of the cluster X-ray
catalog names, their positions and fluxes is given in Table. 5.3. In the next sections we
show that also the fourth diffuse X-ray source can be associated with an overdensity at a
redshift of about z = 0.22.
For the purpose of this paper we assume that the clusters are all located at the same
redshift and calculate their properties at z = 0.22.
The cluster assembly
For the spectral analysis we used the energy band of 0.5 − 3 keV, since the counts at
energies above 3 kev are dominated by background photons. Nonetheless, energies above
3 keV can be used to check the quality of the background subtraction and this was found
to be satisfactory. Based on the surface brightness level, 5 main zones were constructed
for the spectral analysis in such a way to avoid bright point-sources. The zones are shown
in Fig. 5.6 (bottom panel). Zones 1 (which corresponds to the CWAT-01 parent cluster),
3, and 4 are sampled with 3 subzones, labeled a, b, c. Table 5.4 summarizes the results
of the spectral analysis based on the APEC thermal emission model (Smith et al. 2001).
We report the temperature, normalization and their corresponding errors, the reduced χ2
value and the number of degrees of freedom, Nd.o.f. . Zones 3b, 3c, 4b, and 4c turned out
5.4 The clusters
137
27
26
Declination
25
24
23
22
21
02o 20’
10h 00m 40s
30s
20s
Right Ascension
10s
30s
20s
Right Ascension
10s
30s
20s
Right Ascension
10s
27
26
Declination
25
24
23
22
21
02o 20’
10h 00m 40s
27
26
Declination
25
24
23
22
21
02o 20’
10h 00m 40s
Figure 5.6 Top panel: X-ray image of the cluster area in the 0.5 − 2 keV energy band, convolved
with a Gaussian of 8′′ width. The color-scale is linear with lighter color displaying higher signal to noise.
Middle panel: Wavelet reconstruction of the X-ray 0.5 − 2 keV band image showing the cluster area
(taken from Finoguenov et al. 2007). The contour levels are: 3 × 10−4 , 10−5 , 3 × 10−5 , 10−6 , 3 × 10−6 ,
5 × 10−7 cnt/s/px. The position of the CWAT-01 host galaxy is indicated (cross). Bottom panel: 5 main
zones with the corresponding subzones constructed for the X-ray spectral analysis are shown in color (see
also Tab. 5.4). Each zone is labeled with its corresponding ID. X-ray contours (same as in top panel) are
indicated to guide the eye.
138
5. Wide angle tail galaxy in the COSMOS field
Table 5.3 X-ray properties of 4 clusters adopted from Finoguenov et al. (2007).
main
zone
1
2
3
4
catalog
ID
78
82
85
87∗
RA
10
10
10
10
00
00
00
00
28.337
25.454
21.852
13.925
DEC
+02
+02
+02
+02
21
25
23
22
21.6
19.2
42.0
48.0
Flux ± err
[10−14 erg cm−2 s−1 ]
2.63 ± 0.15
0.76 ± 0.07
2.44 ± 0.15
1.24 ± 0.10
zphoto
0.22
0.22
0.22
0.40
Description of the four X-ray luminous clusters adopted from Finoguenov et al. (2007): We list the main
zone (column 1; see Sec. 5.4.1 and Fig. 5.6 for details) associated with the X-ray cluster catalog ID
(column 2), the cluster’s position (columns 3 and 4), the corresponding flux and error (column 5) and the
photometric redshift (last column).
∗
Finoguenov et al. (2007) associate this extended X-ray emission with an optical overdensity at higher
redshift. There seem to be, however, two X-ray features in their cluster catalog, one associated with this
background cluster and the other with the overdensity we find at z ∼ 0.22. [Note, that the position of
this cluster is not exactly coincident with the location of zone 4a (see Fig. 5.6), where we find the optical
overdensity at z ∼ 0.22.] The revised cluster catalog (Finoguenov et al. in prep), produced utilizing the
complete set of XMM-Newton observations, will have a separate entry for the z ∼ 0.22 structure .
to be non-thermal in origin, likely AGN, while zone 5 corresponds to a bright foreground
star. A power-law fit with a photon index of Γ = 2.3 ± 0.1 to zone 1c gives a better χ2
value (χ2 = 1.2 compared to χ2 = 2.07 for the thermal model), which indicates that a
more plausible interpretation of this zone may be a background AGN, as suggested by
Brusa et al. (2007). They associate the optical counterpart of this X-ray peak with a
background (zphoto = 0.89) galaxy. The galaxy has a morphology of a spiral galaxy, clearly
resolved in the HST-ACS image. Nevertheless, we expect the X-ray centroid of the CWAT01 parent cluster to be located in the same area (i.e. associated with zone 1c). The overall
structure of the diffuse X-ray emission suggests that the center is approximately in zone
1c. Furthermore, the center of mass calculated using stellar masses of the “high-density”
galaxies (see Sec. 5.4.2 for details) is offset from the X-ray peak by ∼ 22′′ and is still within
zone 1c. Thus, for simplicity we take the X-ray peak as the center of the CWAT-01 parent
cluster for calculations in this paper arguing that it is not far from where we would expect
the cluster center to be. In Tab. 5.4 we also present the estimated properties of zone 1c,
assuming its thermal origin. The properties of this zone do not deviate strongly from the
expectations of cluster X-ray emission. In addition, we emphasize that the mean values of
the three-dimensional properties based on the spectral analysis results change only within
∼ 10% when taking CWAT-01 as the cluster center and are consistent within the errors
with the properties calculated taking the X-ray peak as the center.
The temperature of each of the four clusters (see Tab. 5.4) is consistent with the temperature range typical for poor clusters (1 − 3 keV; Finoguenov et al. 2001a). Following
Henry et al. (2004) and Mahdavi et al. (2005), we estimate the cluster volume corresponding to the spectral extraction zones. The derived values for gas mass, gas density, entropy
and pressure are listed in Tab. 5.5.
Using the luminosity-weighted temperatures of each of the four clusters (excluding zone
1c), we estimate the total mass within the corresponding radius of the enclosed matter
overdensity of 500 times the critical density (Mtot
500 , r500 in the Table 5.5), using the M-T
5.4 The clusters
139
Table 5.4 X-ray spectral analysis for the clusters
zone
1a
1b
1c
2
3a
3b∗
3c∗
4a
4b∗
4c∗
5+
kT [keV]
1.08 ± 0.26
2.26 ± 0.74
2.37 ± 0.45
1.40 ± 0.45
1.46 ± 0.31
1.47 ± 0.61
-
normalization [×10−5 ]
5.4 ± 2.9
3.5 ± 1.4
4.1 ± 1.1
2.0 ± 1.2
4.5 ± 2.2
2.2 ± 0.8
-
χ2r
1.06
1.12
2.07
1.38
1.18
0.98
-
Nd.o.f.
33
19
16
16
31
19
-
∗
Nonthermal in origin
Foreground star
Results from the X-ray spectral analysis for the clusters using the APEC thermal model. Column 1
displays the spectral extraction zone (see Fig. 5.6 for reference). The temperature is given in column 2,
the normalization in column 3, the χ2 value in column 4, and the number of degrees of freedom, Nd.o.f. ,
in the last column.
+
Table 5.5 X-ray properties of the clusters in the assembly
zone
1a
1b
1c
2
3a
4a
Mgas
[1011 M⊙ ]
13.0 ± 3.2
5.9 ± 1.1
2.00 ± 0.25
2.7 ± 0.7
8.5 ± 1.9
7.0 ± 1.2
ne
cm−3 ]
4.6 ± 1.1
6.7 ± 1.2
23.7 ± 3.0
8.3 ± 2.3
6.0 ± 1.3
0.36 ± 0.06
[10−4
S
[keV cm2 ]
180.8 ± 52.8
295.6 ± 103.4
133.4 ± 27.5
158.4 ± 58.7
204.2 ± 52.3
287.8 ± 121.6
p
dyn cm−2 ]
7.9 ± 2.7
24.0 ± 9.0
90.0 ± 20.0
18.8 ± 7.9
1.4 ± 0.4
8.6 ± 3.8
[10−13
rmin , rmax
[Mpc]
0.104, 0.460
0.060, 0.315
0.000, 0.115
0.000, 0.183
0.071, 0.253
0.000, 0.375
r500
[Mpc]
0.490
0.490
0.438
0.449
0.451
tot
M500
13
[10 M⊙ ]
5.8
5.8
4.1
4.4
4.4
X-ray properties of the clusters. Column 1 lists the zones used for the spectral analysis (see Fig. 5.6).
The following columns are the gas mass (2), gas density (3), entropy (4), pressure (5), the minimum and
maximum radius of the extraction zone (6), the radius corresponding to matter overdensity of 500 times
the critical density, r500 , (7) and the total mass within r500 (8).
140
5. Wide angle tail galaxy in the COSMOS field
Figure 5.7 Pressure (left panel) and entropy (right panel) as a function of cluster radius for different
spectral extraction zones (labeled in each panel, for comparison see Fig. 5.6). Thick crosses (zones 1a, 1b)
represent the CWAT-01 parent cluster. The length of the crosses indicates the 1σ errors. The dashed lines
show the expected pressure and entropy behavior based on local cluster studies and scaled to the redshift
of this system (see text for details).
relation from Finoguenov et al. (2001a). It is possible that the masses are up to 20% higher
than the quoted values, according to recent XMM and Chandra results on the M-T relation
(Arnaud et al. 2005; Vikhlinin et al. 2005). The uncertainty in the total mass estimate is
primarily driven by the uncertainty in the measured temperature and is found to be on the
level of 40% for the reported values. In Fig. 5.7 we compare the derived properties of the Xray emission of the 4 clusters with the expectations based on local sample studies which we
scale to the redshift of our cluster according to the expected evolution of shock heating (see
Finoguenov et al. 2005 for details). The derived properties agree well with the prediction,
which for shallow survey-type data, such as ours, is reassuring that the identification,
background subtraction and point source confusion issues have been properly addressed.
The CWAT-01 parent cluster
The spectral properties of the CWAT-01 parent cluster are extracted from zones 1a, 1b and
1c (Fig. 5.6, bottom panel) as described in Sec. 5.4.1. The luminosity-weighted temperature
of the CWAT-01 parent cluster (excluding zone 1c) is ∼ 1.7 keV and the total mass
∼ 5.8 × 1013 M⊙ which makes it consistent with being a poor cluster (Finoguenov et al.
2001a). The spatial distribution of the diffuse X-ray emission of the cluster seems to be
elongated and irregular. In order to obtain an estimate of its spatial characteristics (i.e.
the core radius, rc , and the β index), we obtain a 1-dimensional surface brightness profile
using the 0.5 − 2.0 keV background-subtracted image corrected for exposure time. We fit
the radial profile with a two-component model: a) a Gaussian to describe the emission of
the inner ∼ 20′′ , and b) a traditional β-model (Cavaliere & Fusco-Femiano 1976) for the
underlying cluster. The models are centered on the main peak of the X-ray emission. A
5.4 The clusters
141
+30.9
′′
β-model with β = 0.57 ± 0.06 and rc = 48.0+8.7
−18.0 = 170.5−63.9 kpc is a good representation
of the cluster X-ray emission. The extended cluster component yields a count rate in the
(0.5-2.0) keV energy range out to r500 of 4.5×10−3 cnt/s. We find that the luminosity of the
cluster that hosts CWAT-01 is 3.6 × 1042 erg/s, consistent with L-T relation of Markevitch
(1998); Mulchaey (2000); Osmond & Ponman (2004).
Using the above derived values of β and rc and a temperature of kT ∼ 2.26 keV (zone
1b) we compute the central number density (n0 ) as described in Sakelliou et al. (1996). The
−3
central number density corresponds to n0 = 1.085+1.12
cm−3 which is in agreement
−0.08 × 10
with the result of the spectral analysis (Tab. 5.5). In Sec. 5.5.2 we use the derived quantities
(i.e. rc and β) for hydrodynamical models explaining the bending of the jets of CWAT-01
and constraining the velocity of the host galaxy relative to the ICM.
5.4.2
Optical properties
Cluster identification using overdensities: Voronoi tessellations
To map the galaxy overdensity in the area of the X-ray cluster assembly we use the Voronoi
tessellation-based approach (hereafter VTA; e.g. Ramella et al. 2001; Kim et al. 2002, Botzler et al. in prep). The VTA has several advantages over other overdensity estimators
which make it favorable for the scope of this paper: First, no a-priori assumptions about
cluster properties (e.g. density profile) are necessary making the technique sensitive to
elongated, i.e. non-symmetrical structures (Botzler et al.in prep). Secondly, we are mainly
interested in substructure which can efficiently be revealed with the VTA.
A Voronoi tessellation on a two-dimensional distribution of points (called nuclei) is a
subdivision of the plane into polygonal regions (nuclei lying on the edge may have open
polygons), where each of them contains one and only one nucleus. Each region consists
of the set of points in the plane that are closer to that nucleus than to any other. The
algorithm used here for the construction of the Voronoi tessellation and calculation of the
local densities is the “varea” code written by Botzler et al.(in prep) which encompasses
the “triangle” code by Shewchuk (1996). Our goal is to quantify the clustering in the
area where CWAT-01 is located, thus the input for the Voronoi tessellation (nuclei) are
galaxy positions drawn from the COSMOS photometric redshift catalog (Capak et al. 2007;
Mobasher et al. 2007). The VTA then defines the effective area, A, that a galaxy occupies
in the two dimensional space. Taking the inverse of the effective area gives the local density
of the galaxy, ρlocal = 1/A.
The selection criteria we apply to the COSMOS photometric redshift catalog are the
following: we select objects classified as galaxies (ST AR < 1) in the redshift bin of width
∆z = 0.2 centered at the CWAT-01 host galaxy’s redshift reported in the catalog. The
mean 2σ error of the selected galaxies is 0.11 ± 0.06 in photometric redshifts. Thus, 67%
of our galaxies have 2σ errors in photometric redshifts better than 0.17 and 95% better
than 0.23.
To robustly estimate the background density, we apply the VTA to a region ∼ 10 times
larger than the region of interest which is 10′ × 8′ corresponding to ∼ (2.1 × 1.7) Mpc2
142
5. Wide angle tail galaxy in the COSMOS field
(z = 0.22). In addition we run Monte Carlo simulations by randomly redistributing the
total number of galaxies in the analyzed field. Then we apply the VTA to each generated
field and calculate the mean density, resulting in a distribution of background densities
with the corresponding standard deviation, ρbkg ± σbkg . We define overdense regions as
regions that have local density values of ρlocal > ρbkg + 10σbkg .
Figure 5.8 g + −r+ vs. r+ color magnitude dia-
gram (CMD) using the COSMOS SUBARU g +
and r+ bands. The galaxies shown in the CMD
are galaxies (g + < 26.5 and Bj < 26.5) within
the cluster area of interest, corresponding to
∼ (2.1 × 1.7) Mpc2 (z = 0.22), which satisfy the
overdensity criterion imposed in the VTA analysis: ρlocal > ρbkg + 10σbkg (see Sec. 5.4.2 for details). Galaxies in masked-out regions (around
saturated objects) are excluded. Filled symbols
represent early type galaxies (SED type < 2.5)
while open symbols stand for late type galaxies
(SED type > 2.5).
Utilizing the COSMOS SUBARU g + and r + bands, we show in Fig. 5.8 the g + −r + vs. r +
color-magnitude diagram of galaxies with local density values ρlocal > ρbkg + 10σbkg and
within the ∼ (2.1×1.7) Mpc2 area encompassing the cluster assembly. Galaxies in maskedout regions (around saturated objects) are excluded to reduce the number of artifacts (note
that excluding the masked-out galaxies from the input sample for the VTA would only
slightly lower the mean background density value). We also impose a magnitude cut of
Bj < 26.5 and g + < 26.5 to exclude noise artifacts which are presumably due to the g +
and Bj detection limits for extended sources. We therefore define the final sample of “highdensity” galaxies as galaxies that satisfy the following criteria: a) ρlocal > ρbkg + 10σbkg ,
b) Bj < 26.5 and g + < 26.5, and c) the galaxies are outside masked-out regions around
saturated objects.
Cluster assembly structure
The Voronoi tessellation for the ∼ (2.1 × 1.7) Mpc2 area surrounding the cluster assembly
with indicated “high-density” galaxies is shown in Fig. 5.9. The large-scale overdensity is
elongated in NW-SE direction with several obvious subclumps. The spatial distribution
of the galaxies seems not to be spherically-symmetric, but irregular, both, on large and
small scales. In the areas around saturated stars (i.e. masked-out regions) we loose all
information about clustering.
5.4 The clusters
143
2.44
2.42
2.4
2.38
2.36
2.34
2.32
150.15
150.12
150.09
150.06
150.03
RA
Figure 5.9 Voronoi tessellation analysis in the area of the cluster assembly (solid blue lines). The
shown field is ∼ (2.1 × 1.7) Mpc2 (at z = 0.22) in size and ≈ 10 times smaller than the total area
analyzed. Masked-out regions (around saturated objects) in the photometric redshift catalog are marked
with dotted green lines (see text for details). The points represent “high-density” galaxies that a) satisfy
our overdensity criterion of ρlocal > ρbkg + 10σbkg , b) satisfy the magnitude criterion of g + < 26.5 and
Bj < 26.5 and c) are not located in masked-out regions. CWAT-01 is marked with the open solid circle.
In Fig. 5.10 the “high-density” galaxies are overlaid on the SUBARU i+ band image.
For comparison, diffuse X-ray emission contours are also shown. It is evident that the
“high-density” galaxies display a complex and irregular spatial distribution, consistent
with the irregular and elongated distribution of the diffuse X-ray emission. Each X-ray
identified poor cluster has a counterpart in optical overdensities approximately following
the distribution of the X-ray emission. Note that the X-ray cluster corresponding to zone
4a is associated with an optical overdensity with a mean redshift of z ≈ 0.22 like the
other clusters. This is additionally confirmed by the SDSS J10006.65+022225.98 galaxy
spectrum (see Tab. 5.2 for details).
The optical overdensities reveal, in addition, a clustering region north-west from the
diffuse X-ray emission (LG-N in Fig. 5.10) not detected in the 0.5 − 2.0 keV X-ray band.
144
5. Wide angle tail galaxy in the COSMOS field
27
26
25
Declination
24
23
22
21
02o 20’
10h 00m 40s
30s
20s
Right Ascension
10s
Figure 5.10 Grey scale SUBARU i+ band image of the cluster area overlaid with X-ray contours (blue).
The contour levels are the same as in Fig. 5.6. The shown area is ∼ (2.1 × 1.7) Mpc2 (at z = 0.22) with
thin circles (black) denoting the “high-density” galaxies (same galaxies as in Fig. 5.9). Masked-out regions
around saturated objects (drawn from the COSMOS photometric catalog) are indicated with dashed green
lines. CWAT-01 is marked with the thick red circle. Orange circles indicate galaxies which have spectra
(see Sec. 5.2.4 and Tabs. 5.1 and 5.2 for details). LG-N labels the overdensity evident from the Voronoi
tessellation-based aproach (VTA) but not detected in X-rays (see text for details). The 0.5 Mpc projected
distance is indicated for reference.
The SDSS J10004.35+022550.71 galaxy spectrum confirms that this structure is at the
same redshift as the whole cluster system. We assume that LG-N is a loose group bound
to the cluster assembly.
Substructure in the CWAT-01 parent cluster
The spatial distribution of the “high-density” galaxies (Fig. 5.11, top panel) in the CWAT01 parent cluster is irregular and elongated with two dominant subclumps: a) a Western
overdensity (including the CWAT-01 host galaxy) extended in NW-SE direction (SC1 in
Fig. 5.11) and b) an Eastern overdensity elongated in NE-SW direction (SC2). There
are three bright foreground stars contaminating the CWAT-01 parent cluster area. Nev-
5.4 The clusters
145
30’’
Declination
22’ 00’’
30’’
21’ 00’’
30’’
02o 20’ 00’’
10h 00m 35s
30s
Right Ascension
25s
10h 00m 35s
30s
Right Ascension
25s
30’’
Declination
22’ 00’’
30’’
21’ 00’’
30’’
02o 20’ 00’’
Figure 5.11 Top panel: SUBARU i+ band image (grey scale) of the CWAT-01 parent cluster. Overlaid
are X-ray contours with contour levels as in Fig. 5.6. Indicated are “high-density” galaxies. Red solid
circles denote early type galaxies (SED type < 2.5) while blue solid circles indicate late type galaxies (SED
type > 2.5). The brightest galaxies (MV < −20.5) in this area are marked with thick red circles. Dashed
green lines mark masked-out objects. SC1 and SC2 label the two subclumps evident in the cluster (see
text for details). Bottom panel: SUBARU i+ band image (grey scale) and X-ray contours as in top panel
overlaid with 1.4 GHz radio contours. The radio contour levels start at the 3σ level and increase in steps
of 1σ. The bending angle of CWAT-01 is indicated by thin lines while the arrow indicates the velocity
direction of the galaxy. The open box marks the position of the center of mass of the cluster computed
taking into account the stellar masses of the “high-density” galaxies in the CWAT-01 parent cluster (the
stellar masses were drawn from the COSMOS photometric redshift catalog; Mobasher et al. 2007.
146
5. Wide angle tail galaxy in the COSMOS field
ertheless, those masked-out regions should not affect our results substantially since they
are located at the outer parts of the parent cluster. Contrary to the expectation in relaxed systems where one would expect early type galaxies to be centrally concentrated
around the bottom of the cluster potential well, the distribution of the early type galaxies
(SED type < 2.5) in the CWAT-01 parent cluster is spatially elongated and coincident
with subclumps SC1 and SC2 (see Fig. 5.11). Late type galaxies are preferentially at the
outskirts of the cluster. The brightest galaxy in the cluster is the CWAT-01 host galaxy
(r + = 18.899 ± 0.004, MV = −22.9 ± 0.1). This is not surprising due to previous studies which have shown that WATs are generally associated with BCGs (e.g. Burns 1981).
In addition, the brightest galaxies (MV < −20.5) in the cluster are strongly clustered
in the region around CWAT-01, while only one (i.e. the second brightest in absolute V
magnitude) is located at the outskirts of the cluster (see Fig. 5.11, top panel).
Using the stellar masses reported in the COSMOS photometric redshift catalog Mobasher
et al. (2007) of the “high-density” galaxies we compute the position of the center of mass
(indicated in Fig. 5.11, bottom panel). The offset of the center of mass from the main peak
in the diffuse X-ray emission is only ∼ 22′′ . Note that because of the bias introduced by
the masked-out regions in the cluster, the center of mass may be closer to the main X-ray
peak than given above.
5.5
Discussion
The unified theory for the mechanism responsible for bending the jets of WAT radio galaxies
is the dynamic pressure exerted on the jets by the ICM due to the relative motion between
the galaxy and the ICM. In Sec. 5.5.1 we compare the minimum pressure present in the
radio jets to the thermal ICM pressure in order to investigate the confinement of the jets.
We have shown in previous sections that CWAT-01 is associated with the BCG in its
parent cluster. Therefore it is expected to be at rest in the minimum of the gravitational
potential (e.g. Bird 1994). In order to constrain the relative velocity between CWAT-01’s
host galaxy and the ICM we apply several hydrodynamical models explaining the bending
of the jets of WATs (Sec. 5.5.2). In Sec. 5.5.3 we suggest possible merger and encounter
scenarios responsible for the bending of the jets (Sec. 5.5.3). The environment of CWAT-01
on larger scales (i.e. the cluster assembly) is discussed in Sec. 5.5.4.
5.5.1
Pressure balance
It is often assumed that the radio jets are confined by the ICM (e.g. Miley 1980 and
references therein) thus it is interesting to compare the minimum internal pressure in
the radio jets with the thermal ICM pressure. The minimum internal pressure in the
∼
radio jets was calculated in the middle part of the Eastern tail (Sec. 5.3.1), pjet
min = 4.3 ×
−13
−2
10 dyn cm , and it is lower than the ICM pressure in zone 1b, which contains CWAT-01,
1b
−13
pzone
dyn cm−2 (see Sec. 5.4.1 for details). Such a pressure imbalance is
ICM = (24 ± 9) × 10
not unusual for WATs (e.g. Hardcastle & Sakelliou 2004). It implies either some departure
5.5 Discussion
147
from the minimum energy condition (which is almost equal to equipartition between the
relativistic particles and the magnetic fields in the jets) or a contribution to the pressure
from particles which do not participate in equipartition such as thermal protons. However,
one should be careful in comparing the two pressure values due to the low resolution
of the X-ray data and the numerous assumptions in the minimum pressure calculation.
Nevertheless, the ram pressure models we apply to CWAT-01 in order to constrain the
relative velocity between the galaxy and the ICM (next section) should not be affected by
this pressure imbalance. The inherent assumption in these models is that the dynamic ram
pressure of the ICM is comparable with the pressure difference across the jet.
5.5.2
Bending of the radio jets of CWAT-01:
constraints on the galaxy velocity
In this section we apply several hydrodynamical models explaining the bending of the jets
of CWAT-01 in order to constrain the velocity of CWAT-01’s host galaxy relative to the
ICM. The classical Euler equation describes the jets if the bulk plasma velocity in the
jets is characterized by non-relativistic motions (e.g. Jaffe & Perola 1973; Begelman et al.
1979; Christiansen et al. 1981; Sakelliou et al. 1996). Sakelliou et al. (1996) developed a
simple hydrodynamical model to describe the bending of the jets of 3C34.16. They assume
that the forces acting on the jets are ram pressure and buoyancy and they model the jets in
the plane of the sky assuming a steady plasma flow (Sakelliou et al. 1996, their equations
[8]-[11]). The strength of this model is that it provides a constraint on the galaxy velocity
relative to the ICM solely dependent on the jet density at the point where ram pressure
and buoyancy balance. At the turn-over point the bending of the jet changes its direction.
At this point the forces of ram pressure and buoyancy in the direction normal to the jet
balance and the only unknowns are the galaxy velocity and the density of the jet at this
point:
rto
ρj
r̂ · n̂
3βkTICM h
rc
2
1−
(5.5)
vgal =
2
µmp rc
v̂gal · n̂
ρICM
rto
1 + rc
Here rto is the radial distance from the cluster center to the turn-over point (projected on
the plane of the sky), rc and β are the core radius and the standard hydrostatic-isothermal
β model parameter, respectively (see Sec. 5.4.1), n̂ is the normal vector to the jet in the
plane of the sky, vgal is the component of the galaxy velocity in the plane of the sky, h is
the scale height of the jet, ρj and ρICM are the jet and ICM densities, respectively, kTICM
is the ICM temperature in keV (∼ 2.26 keV), µ is the mean molecular weight and mp the
proton mass.
The 1.4 GHz radio map (Fig. 5.1) clearly shows that the radio jets bend twice. Near
the optical counterpart the jets turn to the south. The second bend of the jets is towards
the south-west direction (points E1 and W1 in Fig. 5.1). The first bend can be attributed
to ram pressure as a result of the relative motion of the galaxy through the ICM. At
larger radii buoyancy takes over and the jets are pushed towards lower density regions in
148
5. Wide angle tail galaxy in the COSMOS field
the ICM. Fig. 5.12 shows the galaxy velocity as a function of the ratio of the jet density
to the ICM density, ρj /ρICM , calculated at the point W1 (thick solid line). The allowed
range in velocities is indicated (broad hatched region). The limiting velocity of the galaxy
−1
relative to the ICM (in the limit ρj /ρICM → 0) is vgal ∼ 400+150
−110 km s . Note that vgal
measures the projected velocity on the plane of the sky, thus any inclination of CWAT-01
to the line-of-sight would result in an additional line-of-sight component of velocity, thus
increasing the total speed of the system. The upper and lower limits of the galaxy velocity
were computed taking into account the errors of rc , β, and kT . Additional uncertainties
are introduced through the estimate of n̂ at the turn-over point and r̂ · n̂ which depends
on the position of the center of the total mass of the cluster (discussed in Sec. 5.4.1). Thus,
the derived velocity is a rough estimate of CWAT-01’s velocity relative to the ICM as a
function of ρj /ρICM .
800
600
400
200
0
0.0001
0.001
0.01
0.1
1
Figure 5.12 Mean galaxy velocity, vgal , (thick lines) as a function of the ratio of the jet to ICM
density, ρj /ρICM , with the allowed ranges (hatched regions) corresponding to our error estimates. The
broad hatched region shows the galaxy velocity calculated from eq. [5.5] using the model developed in
Sakelliou et al. (1996, S96) which takes into account ram pressure and buoyancy as forces responsible for
bending the jets (see text for details). The narrow hatched region shows the galaxy velocity calculated from
eq. [5.6] (developed by Begelman, Rees, & Blandford 1979, B79) taking only ram pressure into account.
If we require that the conditions of both models are satisfied, then the allowed ranges for vgal and ρj are
within the intersecting area.
Another hydrodynamical model, that we apply to CWAT-01 to estimate the galaxy
velocity, was first proposed by Begelman, Rees, & Blandford (1979) to explain the jets in
NGC1265. The curvature of the jets is again assumed to be produced by ram pressure
exerted on the galaxy as it moves through the ICM. The ram pressure is balanced by the
centrifugal force exerted by the jet as it curves:
5.5 Discussion
149
2
ρICM vgal
ρj vj2
=
h
R
(5.6)
where vj is the bulk jet velocity, h is the scale hight, R is the radius of curvature, vgal
is the galaxy velocity and ρICM and ρj are the ICM and jet densities, respectively. Placing
the mean jet velocity of the velocity range derived in Sec. 5.3.1, vj ∼ 0.045c, into eq. [5.6],
using an estimate of the scale height (at point C0 in Fig. 5.1) of h ∼ 1.7′′ (∼ 6 kpc) and
the radius of curvature of R ∼ 10′′ (∼ 35 kpc), we show the galaxy velocity as a function
of the ρj /ρICM ratio in Fig. 5.12 (dashed lines). Indicated is the galaxy velocity range
corresponding to the jet velocity range, 0.04c . vj . 0.05c (narrow hatched region).
Requiring that the conditions of both equations, eq. [5.5] and [5.6], are satisfied (as
illustrated in Fig. 5.12), we obtain an estimate of both, the galaxy velocity, vgal , and the
−1
jet density, ρj . vgal is in the range of about 400+150
and ρj is 0.005+0.01
−100 km s
−0.003 ρICM ,
respectively. The ICM density is known from the spectral analysis, ρICM = (6.94 ± 1.27) ×
10−4 cm−3 , thus the estimated jet density with the corresponding errors is about 0.03+0.09
−0.02 ×
10−4 cm−3 .
So far we have neglected possible in situ particle acceleration within the jets. If particle
re-acceleration occurs then the bulk lifetime of the synchrotron electrons in the jets would
be higher than the 13 Myr estimated in Sec. 5.3.1. A lower limit of the galaxy velocity can
then be estimated by assuming efficient conversion of kinetic energy into internal energy in
the plasma jet flow (e.g. Eilek 1979). If the observed luminosity, L, of the jets is supplied
by conversion of the bulk kinetic energy with an efficiency, ǫ, then L ∼ π2 ρj h2 vj3 ǫ (e.g. Burns
1981; O’Donoghue et al. 1993). Substituting vj in terms of the luminosity into eq. [5.6],
one gets:
vg &
2L
π
1/3
1/6 −1/2
ρj ρICM h−1/6 R−1/2 ǫ−1/3
(5.7)
which is only weakly dependent on the jet density, ρj . For an efficiency of ∼ 10%, assuming the above derived jet density, the galaxy velocity is roughly vg & 350 km s−1 which
is consistent with the results from the previously applied models.
The models we applied to CWAT-01 in the above discussion thus suggest that the
galaxy velocity relative to the ICM is in the range of about 300 − 550 km s−1 .
5.5.3
Subcluster merging in the CWAT-01 parent cluster?
Beers et al. (1995) report a median velocity dispersion of 336 ± 40 km s−1 (which is in
agreement with e.g. Ramella et al. 1994; Ledlow et al. 1996) for a sample of MKW/AWM
poor clusters 3 . They find a velocity offset between the velocity of the central galaxy
and the mean velocity of the rest of the galaxies of . 150 km s−1 for clusters with no
3
A sample of 23 poor clusters of galaxies originally identified by Morgan, Kayser, & White (1975) and
Albert, White, & Morgan (1977).
150
5. Wide angle tail galaxy in the COSMOS field
evidence for subclustering. The velocity range of 300 − 550 km s−1 we found for CWAT01’s host galaxy, which is the dominant galaxy (BCG) in its parent cluster, is significantly
higher than this limit which indicates recent merger events between less massive systems
of galaxies (Bird 1994). High peculiar velocities are strongly correlated with the presence
of substructure in the system (Bird 1994). Indeed, the VTA results indicate subclustering
in the CWAT-01 parent cluster (SC1 and SC2 in Fig. 5.11). Furthermore, the brightest
galaxies in the cluster are strongly concentrated around CWAT-01. We speculate that
SC1 and SC2 interact and, furthermore, that SC2 may be infalling into the gravitational
potential of SC1. This interaction may cause a dynamical state of the cluster violent enough
to produce the inferred relative velocity of the CWAT-01 host galaxy to the ICM needed to
bend the jets in the observed way. Moreover, the irregular assembly of early type galaxies
in the CWAT-01 parent cluster (see Fig. 5.11) suggests that it is not a relaxed system.
The elongated and irregular diffuse X-ray emission of this cluster indicates independently
possible merger or accretion events in the cluster. Finoguenov et al. (2005) have shown
that X-ray elongations are often seen at the outskirts of clusters and that their spectral
characteristics correspond to a colder, dense gas in pressure equilibrium with the cluster.
This gas is associated with accretion zones in clusters where dense parts of the filaments
survive the accretion shock and penetrate the cluster outskirts.
Our results suggest that merger events within the CWAT-01 parent cluster caused such
a dynamical state in the cluster which is needed for bending the radio jets of CWAT-01
in the observed way. Such a merging scenario is consistent with conclusions of previous
studies which have suggested that the bent shape of WAT galaxies is caused by mergers
(e.g. Gomez et al. 1997; Sakelliou et al. 1996; Sakelliou & Merrifield 2000).
Based on a sample of ∼ 20 WAT galaxies which are located in Abell clusters and have
ROSAT X-ray observations Sakelliou & Merrifield (2000) have shown that WATs travel
predominantly radially towards or away from the center (as defined by the X-ray centroid)
of their host cluster. CWAT-01 does not seem to be on a radial motion in the projected
plane of the sky, as is evident in Fig. 5.11 (bottom panel). This may be a bias caused
by projection effects. However, the gravitational influence of the neighboring clusters may
have played a significant role in causing the inferred velocity modulus and direction of
CWAT-01’s host galaxy relative to the ICM.
Based on the above arguments we suggest that the radio jets of CWAT-01 were bent as a
consequence of the motion of CWAT-01 relative to the ICM induced through interactions
between subclusters (SC1 and SC2) and/or interactions between the CWAT-01 parent
cluster and the other identified clusters.
5.5.4
Galaxy cluster assembly
Since CWAT-01 seems to be part of a very complex large-scale structure, the possible
gravitational influence of the other clusters on the galaxy and its immediate environment
cannot be neglected. Fig. 5.13 shows the distribution of emission from different parts
of the electromagnetic spectrum within the cluster area. The diffuse X-ray emission has
revealed that the CWAT-01 parent cluster is only one of the poor clusters encompassed in a
5.5 Discussion
151
Figure 5.13 Color composite image of the cluster area (top) and the CWAT-01 parent cluster (bottom).
The SUBARU B (blue), V (green), and i+ (red) bands are displayed in the background. The diffuse
X-ray emission is presented by rainbow colors and the 1.4 GHz map is shown in white. The top panel
encompasses an area of ∼ (2.1 × 1.7) Mpc2 (analogous to Fig. 5.10). The size of the bottom panel is as in
Fig. 5.11.
152
5. Wide angle tail galaxy in the COSMOS field
larger cluster structure. The cluster assembly contains a minimum of four X-ray luminous
clusters within ∼ 2 Mpc distance. In addition, the VTA indicates that there is at least
one more loose group (LG-N; Fig. 5.10) on the outskirts of the X-ray cluster assembly but
not detected in the X-rays. Furthermore, the whole cluster assembly is part of a largescale structure component (LSS #17, Scoville et al. 2007b) extended over ∼ 4 Mpc in NS
direction.
The direction of the jets of CWAT-01 is almost perpendicular to the X-ray elongation of
the CWAT-01 parent cluster as well as to the large-scale elongation of the X-ray emission.
Although LSS #17 spatially extends ∼ 4 Mpc in NS direction, the direction of its long axis
(as defined in e.g. Novikov et al. 1999) is in the NW-SE direction (see Fig. 3 in Scoville et al.
2007b). Hence, the direction of the jets of CWAT-01 is almost perpendicular also to the
long axis of LSS #17. This is in disagreement with the correlation of the alignment between
the direction of the jets and a) the central X-ray emission elongation (Burns et al. 1994;
Gomez et al. 1997) and b) the supercluster axis as defined by the distribution of the nearby
Abell clusters (Novikov et al. 1999). Nevertheless, the misalignment seen in CWAT-01’s
orientation compared to the parent cluster and to LSS #17 elongations may not be
unexpected if we assume an early stage of cluster formation. Rich clusters form at the
intersection of large-scale filaments. Compared to numerical simulations (e.g. Burns et al.
2002; Springel et al. 2005) of cluster evolution at z = 0.2 the final cluster has not yet
formed or relaxed at the intersection of filaments where matter is accreted from numerous
filaments. Thus, in such an environment it would not be unexpected to observe a WAT
galaxy with its jets not aligned with the cluster or large-scale structure elongations.
The X-ray and optical analysis indicate that each individual poor cluster within the
cluster assembly is not spherically-symmetric, both in the diffuse X-ray emission and in
the spatial distribution of galaxies in the optical (VTA). This strongly indicates that the
poor clusters are not in a dynamically relaxed state. In addition, the overall, large-scale
distribution of the cluster assembly is very complex and irregular. It is likely that the
loose group LG-N is bound to the system. The lack of X-ray emission from this group
suggests that the system is not very massive. The flux limit for the cluster search (see
Sec. 5.2.2) allows a detection of an object at z = 0.22 with a limiting luminosity of
Llimit
= 3×1041 erg s−1 , which corresponds to a low-mass group (e.g. Mulchaey et al. 2003).
x
Following the luminosity-temperature (L-T) relation for groups (Osmond & Ponman 2004)
the limiting temperature then equals Txlimit ∼ 0.4 keV. Using the mass-temperature (M-T)
relation from Finoguenov et al. (2001b) the minimum mass of a system to be detected in
tot,limit
the diffuse 0.5 − 2 keV band at z = 0.22 then corresponds to M500
∼ 1.5 × 1013 M⊙ .
Thus, the total mass of LG-N has to be less than this mass.
Although spectroscopic verification is needed for the physical connection of the clusters,
we believe that we are witnessing a protocluster in the process of being built up of multiple
galaxy clusters and groups. Adding up the estimated masses for the four clusters identified
via diffuse X-ray emission (see Tab. 5.5) and the limiting mass inferred for LG-N the
resulting combined mass is M ∼ 2.0 × 1014 M⊙ . This is a rough estimate of the total mass
of the cluster system once it is formed since it does not include the material between the
clusters nor other loose groups that may be bound to the cluster assembly (the cluster
5.6 Summary and conclusions
153
assembly is only part of LSS #17). Thus, the estimated mass that the final cluster may
have after the individual clusters merge would correspond to ∼ 20% of the Coma cluster’s
total mass. This is the first time such a complex dynamically young cluster system in the
process of formation is identified via a WAT radio galaxy.
5.6
Summary and conclusions
We have analyzed a wide angle tail (WAT) radio galaxy, CWAT-01, first resolved in the
VLA-COSMOS survey. The multiwavelength data set of the COSMOS survey have enabled
us to identify and analyze the environment of CWAT-01 in several independent ways. The
cluster structure revealed via CWAT-01 seems to be more complex than any structure
hosting WAT galaxies reported in the past. We summarize the basic findings of this
analysis as follows:
• The lengths of the Eastern and Western radio jets of CWAT-01 are ∼ 210 kpc and
∼ 160 kpc, respectively, and the bending angle is ∼ 100◦ in the projected plane
of the sky. It seems to be asymmetric and at this point we cannot rule out that
the asymmetry is caused by projection effects. The 1.4 GHz radio power of P1.4 =
2.0 × 1024 W Hz−1 puts CWAT-01 on the lower end of the FRI-II break region where
WATs are usually found.
• The host galaxy of CWAT-01 is an elliptical galaxy with a shallower surface brightness
profile than predicted by the de Vaucouleurs law. It is the brightest cluster galaxy
(BCG) in the CWAT-01 parent cluster. The surface brightness profile is very well
fit by the Sersic r 1/n law with n = 5, reff = 8.2 kpc and µeff = 22.11 mag arcsec−2
consistent with values typical for brightest cluster galaxies (cD/D).
• Applying several hydrodynamical models, taking ram pressure and buoyancy forces
into account, to explain the observed bending of the radio jets of CWAT-01, the
allowed range of the galaxy velocity relative to the ICM is approximately 300 −
550 km s−1 . Both, the upper and lower velocity are higher than is expected for
dominant galaxies (i.e. BCGs) in relaxed systems.
• The cluster hosting CWAT-01 (CWAT-01 parent cluster) was detected in diffuse
X-ray emission. The luminosity weighted temperature of the cluster is ∼ 1.7 keV
consistent with poor cluster temperatures. The total mass within the r500 radius is
∼ 5.8 × 1013 M⊙ . The cluster shows evidence for subclustering, both in diffuse X-ray
emission and in the spatial distribution of galaxies found from the optical analysis
applying the Voronoi tessellation-based approach (VTA). The distribution of early
type galaxies is not centrally concentrated, it is irregular and partitioned into two
apparently distinct subclumps.
• The CWAT-01 parent cluster itself is part of a larger cluster assembly consisting
of a minimum of 4 clusters within ∼ 2 Mpc distance identified via diffuse X-ray
154
5. Wide angle tail galaxy in the COSMOS field
emission. The ICM temperatures of the three clusters surrounding the CWAT-01
parent cluster are in the 1.4 − 1.5 keV range consistent with temperatures of poor
clusters. The total masses of the clusters within the r500 radius are in the range of
about (4.1 − 4.4) × 1013 M⊙ .
• The Voronoi tessellation-based approach (VTA) results indicate that there is at least
one more loose group that is likely bound to the system. From the X-ray detection
limit for diffuse sources we infer that the total mass of this group must be less than
1.5 × 1013 M⊙ .
The whole cluster structure described in this paper is encompassed in a large-scale
structure component, LSS #17, reported in Scoville et al. (2007b). LSS #17 is elongated
in NS direction and extends ∼ 4 Mpc along the major axis. Our results strongly indicate
that we are witnessing the formation of a large cluster from an assembly of multiple clusters,
consistent with the scenario of hierarchical structure formation. If this is the case, then the
estimated minimum total mass of the final single cluster after the poor clusters merge would
correspond to M ∼ 2.0 × 1014 M⊙ or ∼ 20% of the Coma cluster mass. In this scenario,
the large velocity of the CWAT-01 host galaxy relative to the ICM can easily be explained.
The CWAT-01 parent cluster seems not to be relaxed, thus a plausible explanation of the
motion of the galaxy relative to the ICM is interaction of the two identified subclumps
(SC1 and SC2) within the cluster. On the other hand, we cannot rule out the gravitational
influence of the other poor clusters as a cause for inducing such a velocity.
Resolving the detailed physics causing the bending of the radio jets of CWAT-01, the
dynamical interplay between and within particular clusters as well as the spectroscopic
confirmation of the physical connection of the clusters has to await the completion of the
zCOSMOS program (Lilly et al. 2007). Nevertheless, our results support the idea that
WAT galaxies are tracers of galaxy clusters, in particular dynamically young ones.
Chapter 6
Summary and outlook
The main aim of this thesis has been to expand our understanding of properties of radioluminous sources, and place them into the general context of global properties of galaxies
and their evolution. In this chapter a summary of the obtained results as well as future
prospects are presented.
6.1
The faint radio population and its
cosmic evolution
In the following section the major results of this thesis related to the faint radio population
are summarized, and ongoing and future projects are discussed.
6.1.1
The submillijansky radio population
VLA-COSMOS Large Project
The VLA-COSMOS Large Project is state-of-the-art in current radio astronomy as it encompasses the widest area (2◦ ) survey at 1.4 GHz (20 cm) with a resolution of only 1.5′′
and unprecedented sensitivity of 10.5 (15) µJy beam−1 over the full 1 (2)◦ . These observations have provided the basic data-set for the science presented in this thesis. Obtaining
the 1.4 GHz mosaic has required a major effort of combining large amounts of telescope
time (250 h with the VLA in A-array, and 25 h in C-array), computational power (it took
more than two weeks of pure computation time only to image the 23 pointings), and human resources (about 2 years were required to observe, image, catalog, and thoroughly test
the data). The pointing layout of the survey has been designed as a hexagonal pattern
that consists of 23 individual overlapping pointings allowing to maximize a uniform noise
coverage over the 2◦ . Exactly this uniform and low rms over the full large field makes the
VLA-COSMOS mosaic exceptional compared to other deep radio surveys which usually
observe smaller sky-areas with very non-uniform noise that substantially rises towards the
edges of the field (e.g. Fomalont et al. 2006; see also Tab. 2.1).
156
6. Summary and outlook
The VLA-COSMOS source catalog has been carefully constructed using well tested automatic source-finding algorithms. Generally, in any survey sources that have intrinsically
large angular sizes may be observed as multiple discrete components which is caused by
the sources’ surface brightness combined with the observational flux limit. It is extremely
difficult to identify such objects and extract their flux densities in an automated way. This
is in particular true in a radio survey as e.g. radio galaxy morphologies can be very complex. Therefore, special care was paid to identify such objects (mostly radio galaxies with
(double-)jet radio morphologies). It is noteworthy that most other publically available
catalogs at 20 cm (e.g. FIRST, NVSS) have not undertaken such an effort, and provide
only component catalogs, implying that, when working with these tables, large extended
sources first have to be identified (which is not trivial), and then combined either from the
multiple entries in the catalog or from the images themselves.
The generated VLA-COSMOS source catalog contains an impressive number of ∼ 2, 400
radio sources with a signal to noise equal to, or greater than, 5 (∼ 50 µJy), providing to
date the largest and most uniform statistical sample of radio sources down to such faint
flux densities. About 90% of these sources have submillijansky flux densities, making the
VLA-COSMOS 1.4 GHz radio sample the optimal data set to study the properties of
the faint radio population that consists of two main populations: AGN and star forming
galaxies. However, the relative contribution of AGN and star forming galaxies to the
submillijansky radio population is not well understood, and has been hotly debated in the
past (e.g. Condon 1984a; Windhorst et al. 1985a; Benn et al. 1993; Gruppioni et al. 1999;
Seymour et al. 2004; Simpson et al. 2006; Fomalont et al. 2006). One of the major aims
of this thesis has been to resolve this controversy about the composition of the faint radio
population. This has been approached by an in-depth panchromatic statistical study of the
VLA-COSMOS radio sources, in particular by developing a new method for disentangling
AGN (both radio-quiet and radio-loud) from star forming galaxies.
New method for separating AGN from star forming galaxies
A new method has been developed to discriminate between AGN and star forming galaxies
based purely on photometric – rest-frame color – data, thus eliminating the need for timeconsuming spectroscopy. One major advantage of this method is that it separates well
low luminosity AGN, such as Seyfert, LINER galaxies and absorption line AGN from star
forming galaxies using a minimal number of parameters (the NUV – NIR SED). This
makes it favorable for general purposes as it carries the potential to be efficiently applied
to other data-sets where similarly to the VLA-COSMOS radio sources (see below) the two
dominating sub-populations are star forming and AGN galaxies.
The separation is based on a rest-frame color – P1 – which is a linear superposition of
colors in the Strömgren photometric system encompassing the wavelength range of 3500 –
5800 Å (Smolčić et al. 2006). The method has been well calibrated and thoroughly tested
using a large local sample of galaxies representative of the VLA-COSMOS population at
higher redshifts (∼ 7, 000 sources in the SDSS “main” spectroscopic galaxy sample, NVSS
and IRAS surveys). These local galaxies have been used to infer the statistical properties
6.1 The faint radio population and its cosmic evolution
157
of the photometrically selected samples of star forming and AGN galaxies, yielding a high
completeness of the selected star forming (∼ 85%) and AGN (∼ 95%) galaxy samples,
with a fairly modest contamination of ∼ 20% of AGN in the first, and ∼ 5% of star
forming galaxies in the latter sample. Using the local optical - radio -IR galaxy sample
(SDSS - NVSS - IRAS) it has been demonstrated that the rest-frame color based separation
method is not biased against selecting dusty star forming galaxies. This is an especially
important aspect for a radio-selected sample as radio emission is a valuable dust-unbiased
star formation tracer.
The high angular resolution (1.5′′ ), and the excellent astrometric accuracy (130 mas;
see Chap. 2) of the VLA-COSMOS survey have allowed me to essentially unambiguously
identify counterparts of VLA-COSMOS sources at other wavelengths (optical, MIR, Xray; COSMOS Project; see Chap. 3). Galaxies in the VLA-COSMOS radio population
(i.e. point sources – stars and QSOs – excluded) that have been securely associated with
counterparts out to faint optical limits (i ≤ 26) and z ≤ 1.3 have been classified into
star forming and AGN galaxies using the newly developed rest-frame color based method.
The rest-frame color P1 has been computed by χ2 fitting of the observed NUV – NIR
SED of the VLA-COSMOS galaxies with a realization of 100,000 model spectra generated
with the Bruzual & Charlot (2003) stellar population synthesis models, and its accuracy
has been extensively tested (and shown to be ∼ 0.1 mag). Further, in order to infer the
effectiveness of the star forming/AGN galaxy discrimination of the VLA-COSMOS sources,
the panchromatic properties of the identified star forming and AGN galaxies have been
thoroughly studied, and compared with other independent diagnostic schemes that rely on
mid-infrared colors and optical spectroscopy, demonstrating a remarkable agreement and
confirming the validity of our classification method.
In summary, a new method that separates star forming from (low-luminosity) AGN
galaxies, applicable out to high redshifts (z ∼ 1.3), has been developed, robustly tested,
and applied to the VLA-COSMOS 1.4 GHz radio – optical (i ≤ 26) sources out to z = 1.3.
Hence, well defined statistical radio – optical samples of star forming (340) and AGN (601)
galaxies with submillijansky radio fluxes, located out to z = 1.3, have been constructed.
This new method based on UV to NIR SEDs has the potential to become a standard tool
also for other samples drawn from other wavelengths where the main populations are star
forming and AGN galaxies.
The composition of the submillijansky radio population
In addition to the above described star formation/AGN galaxy discriminator, that has
been applied to the VLA-COSMOS sources with secure optical counterparts (i ≤ 26; z ≤
1.3), the panchromatic – optical/NIR, MIR and X-ray – properties of the complete VLACOSMOS radio source sample (∼ 2, 000 sources) have been used in order to put, for the
first time, robust limits on the composition of the submillijansky radio sources (’population
mix’ hereafter). Inferring the ’population mix’ has been subject of many studies for the
past two decades that yielded controversial results estimating the fraction of star forming
galaxies to range from about 20% to 80% (e.g. Fomalont et al. 2006; Padovani et al. 2007;
158
6. Summary and outlook
see Chap. 3 for more details). One of the major reasons for this discrepancy has been a
conspiracy of good star formation/AGN classifiers usually ruling out statistically complete
radio samples, and vise versa. The analyses presented in this thesis have overcome this
problem by using various panchromatic diagnostic methods (see Chap. 3 for details) on a
large statistical sample of (∼ 2, 000) submillijansky radio sources, allowing a deep insight
into the statistical composition of the faint radio population, not affected by any other
(e.g. optical flux) limits. Our main finding is that star forming galaxies form only about
30 − 40% of the submillijansky population, while the remainder is composed of AGN and
QSOs.
6.1.2
The evolution of radio sources
The cosmic star formation history based on 1.4 GHz radio data
In Chap. 4 the luminosity evolution of star forming galaxies at 1.4 GHz has been constrained
out to z = 1.3 by using the well constructed sample of VLA-COSMOS star forming galaxies (see Sec. 6.1.1). Given the large size (2◦ ) of the VLA-COSMOS field, that samples a
comoving volume at z = 1 comparable to the one observed locally with the SDSS (DR1),
for the first time the evolution of the cosmic star formation rate in the rare, most intensely
star forming galaxies (& 100 M⊙ yr−1 ) has been derived with high precision. Our results
imply a slightly slower evolution for these massively star forming galaxies than previously
predicted based on MIR data. Overall, we find that the radio-derived cosmic star formation
history agrees well with other wavelength-based (Hα, [OII], UV, FIR) findings, when these
are corrected for dust-obscuration where necessary. This verifies the necessary assumptions about the large (luminosity-dependent) corrections required at shorter wavelengths,
especially in the UV regime.
Future prospects: The co-evolution of star formation and supermassive black
hole accretion
The AGN luminosity function, and its evolution, constrain the cosmic formation history of supermassive black holes in centers of galaxies. As to date radio surveys allowed in-detail studies only of the evolution of radio-loud AGN (Dunlop & Peacock 1990;
Willott et al. 2001; Snellen & Best 2001; Clewley & Jarvis 2004), which are genuinely rare
– about 10 times less abundant than their radio-quiet counterparts (Goldschmidt et al.
1999; Ivezić et al. 2002) – optical and X-ray surveys have taken the lead in statistical AGN
studies (e.g. Richards et al. 2002; Ueda et al. 2003). However, these short-wavelength windows are heavily biased against highly dust-obscured sources. Therefore, the radio – dustunbiased – regime provides an essential, complementary view on the accretion history of
the universe. Furthermore, the accretion history of supermassive black holes may be closely
related to the star formation in their host galaxies. It has been demonstrated that the evolution of the luminosity density of star forming and AGN galaxies, where the first traces
the cosmic star formation history and the latter the cosmic black hole accretion history,
6.2 Radio galaxies in galaxy clusters
159
exhibit a similar behavior out to z ∼ 2 (e.g. Boyle & Terlevich 1998; Franceschini et al.
1999; Silverman et al. 2007).
The luminosity function for radio selected star forming galaxies, its evolution, and the
radio-based cosmic star formation history have been presented in Chap. 4, and are summarized above. However, to date the 1.4 GHz luminosity function for (radio-quiet) AGN is
known only in the local universe (Sadler et al. 1999; Best 2004), and its evolution is not well
understood. In simulations the evolution of radio-quiet AGN is usually approximated using
very indirect means, such as e.g. the evolved hard X-ray (2−10 keV) AGN luminosity function (Ueda et al. 2003), converted to monochromatic 1 keV luminosity (known to correlate
with 1.4 GHz luminosity; Brinkmann et al. 2000), and furthermore essentially ’guessing’
the additional contribution of obscured (Compton thick) AGN (e.g. Jarvis & Rawlings
2004). Our large statistical samples of (∼ 350) star forming and (∼ 600) AGN from VLACOSMOS will be used to address the co-evolution of the cosmic star formation rate and
black hole accretion out to z = 1.3, from a dust un-biased perspective. For the first time,
the pure radio evolution of the 1.4 GHz luminosity function for radio-quiet AGN will be
robustly constrained. Our first results on the luminosity functions, derived separately for
star forming and AGN galaxies out to z = 1.3, are shown in Fig. 6.1, implying a stronger
luminosity evolution for star forming galaxies than for AGN.
It is noteworthy that constraining the luminosity function evolution for star forming and
AGN galaxies at 1.4 GHz is of immediate relevance for the preparation of next generation
radio interferometers (e.g. the Square Kilometre Array – SKA; commissioning scheduled
for ∼ 2020, and the Expanded VLA – EVLA; commissioning scheduled for 2009) that will
reach orders of magnitudes deeper than current radio instruments. In particular, the star
forming and AGN galaxy luminosity functions are planned to be constrained to fainter
limits than currently by using the already existing deeper observations at 1.4 GHz of the
inner 1◦ of the COSMOS field (VLA-COSMOS Deep Project). The higher sensitivity of
the VLA-COSMOS Deep, compared to the Large, Project allows one to probe a factor of
∼ 2 fainter in luminosity, and these data will be combined with radio image stacking in
order to probe even fainter levels of the luminosity functions. Reaching fainter luminosities will expand the parameter space allowing for more complex parameterizations of the
luminosity function evolution, such as simultaneous density and luminosity evolution, or
even luminosity-dependent density evolution.
6.2
Radio galaxies in galaxy clusters
Radio galaxies are important for many cosmological aspects. For example they can efficiently be used as tracers of dense environments (e.g. Zanichelli et al. 2001), such as
high-redshift (e.g. Blanton et al. 2003; Miley et al. 2006) and/or dynamically young galaxy
clusters (Chap. 5; Smolčić et al. 2007a). Furthermore, recently semi-analytical models have
proposed that radio galaxies may be the main driver for cosmological processes related to
galaxy formation (Croton et al. 2006; Bower et al. 2006). In the following the results presented in this thesis on the newly identified merging galaxy cluster assembly hosting the
160
6. Summary and outlook
Figure 6.1
First results on
the 1.4 GHz luminosity functions (LFs) for star forming
(blue filled squares) and AGN
(red filled squares) galaxies out
to z = 1.3 derived using the
well defined VLA-COSMOS SF
and AGN samples (see Chap. 3
for details). The LFs were computed as described in Chap. 4,
and the redshift range is indicated in each panel. For comparison the locally derived LFs
(Best et al. 2005) for star forming (open squares) and AGN
(open circles) galaxies are also
shown in each panel. These results will for the first time allow to constrain the cosmic evolution of AGN at 1.4 GHz, shedding light on the co-evolution of
the cosmic star formation rate
(presented in Chap. 4) and black
hole accretion from a dust unbiased perspective since ∼ 5 Gyr
after the Big Bang (see text for
details).
peculiar radio galaxy CWAT-01 are summarized, and future scientific projects related to
galaxy cluster evolution are discussed.
6.2.1
CWAT-01 galaxy cluster assembly
An in-depth case-study of a radio-luminous AGN, a wide angle tail radio galaxy named
CWAT-01, found in the VLA-COSMOS survey has been presented in Chap. 5. CWAT-01
has been associated with a highly complex galaxy cluster assembly, consistent with the idea
that such radio galaxies are efficient tracers of galaxy clusters. The assembly consists of
at least one optically detected and four X-ray luminous clusters within ∼ 2 Mpc distance
at the same redshift (z ∼
= 0.2), and is most probably in the process of forming a large
cluster, that will eventually contain a total mass of at least ∼ 20% of the Coma cluster
mass. Hence, a dynamically young, merging cluster environment in the process of forming
a large cluster has been identified, consistent with the standard ΛCDM cosmological model
that suggests a hierarchical, bottom-up, structure formation scenario. The complexity of
this cluster assembly implies that it may become a proto-typical example for merger of
6.2 Radio galaxies in galaxy clusters
161
multiple cluster constituents at intermediate redshift. Therefore, detailed studies of this
cluster assembly, especially accurately mapping its internal kinematics and structure, will
shed light on processes deemed important for cosmological structure growth.
It is noteworthy that the 5 identified clusters have recently been spectroscopically verified to be exactly at z = 0.22 via 31 galaxy spectra (see Fig. 6.2) obtained by the zCOSMOS
survey (Lilly et al. 2007) which samples spectroscopy for bright objects (i < 22.4) in the
COSMOS field at low spectral resolution (R = 300 − 500). In order to map the precise internal kinematics, structure, and properties of the clusters, higher resolution spectroscopy
(R ∼ 1000) out to faint levels (r = 24) for ∼ 250 potential cluster members using the
ESO/VLT (FORS2 instrument) has already been proposed.
26
Declination
25
24
23
22
21
02o 20’
27
26
25
24
Declination
27
23
22
21
02o 20’
10h 00m 40s
30s
Right Ascension
10h 00m 40s
30s
20s
Right Ascension
20s
10s
10s
Figure 6.2
Left panel: Grey scale SUBARU i+ band image of the CWAT-01 galaxy cluster assembly
overlaid with X-ray contours (thin dark-blue contours on large scales). The shown area is ∼ (2.1×1.7) Mpc2
(at z = 0.22) with thin black circles denoting the photometrically selected potential cluster member
galaxies. CWAT-01 is part of the cluster at the lower-left in the panel, and shown in red contours. Regions
outlined with green lines are flagged areas in the photometric catalog. Bold blue squares indicate the
galaxies with available spectroscopy that puts them exactly at z = 0.22 (see right panel). Right panel:
The distribution of the spectroscopic redshifts for galaxies with i < 22.4 obtained by the zCOSMOS survey
in the area 10′ × 7′ around the cluster assembly, shown in the left panel. The overdense structure at
z = 0.22 is clearly identified (blue squares in the left panel).
6.2.2
Future prospects
Galaxy clusters consist of two major components: i) individual member galaxies emitting in the UV-optical through IR regime and ii) a hot plasma encompassing them (i.e.
the intra-cluster medium – ICM), most prominent in the X-rays. Clusters containing
162
6. Summary and outlook
moderate-power radio galaxies provide even more information with cosmological implications (e.g. Fabian et al. 2003; Forman et al. 2005): radio galaxies are now thought to be the
main drivers of the so-called ’AGN energy feedback’ process, which was recently invoked
in cosmological models (Croton et al. 2006; Bower et al. 2006). In order to explain the observed galaxy properties, in particular the truncation at the upper end of the galaxy mass
function, cosmological models have introduced energy feedback from AGN as the most
relevant mechanism for the suppression of mass growth (Croton et al. 2006; Bower et al.
2006). It is believed that the ’radio mode’, i.e. the energy outflows from radio galaxies (as
these are energetically most favorable), heats the surrounding gas, and thereby truncates
star formation in the host galaxy disabling it to grow too high in mass. However, it is
debated both theoretically and observationally, exactly how (and if) the AGN feedback
works. Observationally, particular cases have been found where AGN energy outflows have
been demonstrated to directly impact the AGN’s environment. Namely, in galaxy clusters
which contain central cooling flows, AGN feedback may account for the observed lack of
cooler gas in the cluster core. However, direct evidence of such a radio – thermal gas
interplay has been seen in only a few local examples (e.g. Fabian et al. 2003; Forman et al.
2005), and it is unclear whether this is common behavior.
The above theoretical suggestions and observational results invoke interesting cosmological aspects that require further investigations. With about 50 X-ray identified galaxy
clusters hosting a radio galaxy, detected out to redshifts of ∼ 1.2, the COSMOS survey is
optimally suited for in-depth studies of clusters that contain a radio galaxy at both intermediate (0.2 ≤ z ≤ 0.6) and high redshifts (z > 0.6). Both redshift ranges are similarly
important, as previous detailed studies were limited to small and heterogeneous samples
at these cosmic times (e.g. Zhang et al. 2005; Popesso et al. 2007, and AGN feedback processes were studied in detail only in a few local examples (e.g. McNamara et al. 2000;
Blanton et al. 2001; Dunn & Fabian 2006). A project exploiting the COSMOS data, and
performing spectroscopic follow-up to advance the topic of galaxy cluster evolution with a
particular focus on the impact of AGN feedback on galaxy formation is underway.
6.3
Further radio observations of the COSMOS field
Three further directions need to, and will, be taken for increasing our knowledge of galaxy
properties and their evolution from a radio perspective. These are observations of the
COSMOS field at i) higher angular resolution, ii) higher sensitivity and iii) additional
radio frequencies.
The e-Merlin radio interferometer provides the largest potential to improve on the
first point. Using this instrument, observations at 1.4 GHz over the entire 2◦ field can
(and are being planned to) be performed at comparable sensitivity to the Large Project,
but with an angular resolution higher by one order of magnitude. This sub-arcsecond
(∼ 0.15′′) angular resolution would enable radio morphology studies i) in star forming
galaxies, discriminating from the radio perspective alone between radiation arising from
the nuclear (most probably AGN) vs. disk (star formation) galaxy component, and ii) in
6.3 Further radio observations of the COSMOS field
163
radio galaxies, allowing better constraints on the structure and physics of radio jets and
lobes with the immediate application in galaxy cluster studies (see previous section). Such
high angular resolution would enable reaching closer to understanding an important aspect
of galaxy properties, namely the properties, fraction, and evolution of composite objects,
where star formation and black hole accretion co-exist and -evolve, possibly also inducing
each other (e.g. Boyle & Terlevich 1998; Franceschini et al. 1999; Silverman et al. 2007).
Higher sensitivity observations have already been performed in the inner 1◦ of the
COSMOS field (VLA-COSMOS Deep Project) reaching an rms of ∼ 7 µJy beam−1 . These
observations have been taken in 2006; the data reduction, imaging, and mosaicing have
been performed by VS and ES as described in Chap. 2. The final catalog contains 3,744
sources down to a 4σ limit at 2.5′′ resolution, and was generated by A. Datta and CC. As
described in Sec. 6.1.2 these data will be important to better constrain the faint end of our
1.4 GHz radio luminosity functions for star forming and AGN galaxies out to z = 1.3.
Additional observations of the entire COSMOS field at other radio frequencies are essential for statistical studies of the radio synchrotron spectrum emitted from different radio
populations posing the question: Is the hardness of the synchrotron spectrum different for
star forming and AGN galaxies? Further, constraining the synchrotron spectrum for radio galaxies allows detailed studies of the physics, and energy losses, of the synchrotron
radiation in radio galaxies, providing important implications on AGN feedback studies. In
particular, the EVLA (Expanded VLA) is planned on being used for observations of the
COSMOS field at both lower and higher frequencies than 1.4 GHz (320 MHz, 4.8 GHz).
164
6. Summary and outlook
Appendix A
Summary of all-sky surveys
A.1
The Sloan Digital Sky Survey
The Sloan Digital Sky Survey (SDSS; York et al. 2000; Stoughton et al. 2002; Abazajian et al.
2003, 2004, 2005; Adelman-McCarthy et al. 2006, 2007) is a spectroscopic and photometric
survey that aims at mapping one-quarter of the entire sky (∼ 10, 000◦) and perform a
comprehensive redshift survey of galaxies, quasars and stars. To date the SDSS has made
publically available photometry for ∼ 215 million objects observed over 8,000◦ (DR5)
in five NUV – NIR broad photometric bands (u, g, r, i, z) with effective wavelengths of
(3551, 4686, 6165, 7481, 8931) Å, and a median angular resolution of 1.4′′ . Spectroscopy
at a resolution of R = λ/dλ = 1800 is systematically obtained in the wavelength range of
3800Å to 9200Å with targeting magnitude limits of rPet < 17.7 for galaxies and i < 19.1
for quasars. In total, to date ∼ 1 million spectra, observed over 5740◦ , have been made
publically available. The spectroscopy of extragalactic sources is divided into three main
samples: the ’main’ galaxy sample (Strauss et al. 2002), the luminous red galaxy sample
(Eisenstein et al. 2001), and quasars (Richards et al. 2002). Details about the survey, and
its extension (SDSS-II; DR6) can be found at www.sdss.org.
A.2
The NRAO VLA Sky Survey (NVSS)
The National Radio Astronomy Observatory VLA Sky Survey (NVSS; Condon et al. 1998)
has observed the entire sky north of −40◦ declination at 1.4 GHz (20 cm) continuum with
the Very Large Array (VLA). The major product is a catalog of ∼ 1.8 million discrete
sources. Note that extended sources, that consist of several discrete components, have not
been matched in the catalog, but are reported separately. The angular resolution of the
survey is 45′′ , and the positional uncertainties vary from less than 1′′ for relatively strong
(> 15 mJy) point sources to 7′′ for the faintest (∼ 2.3 mJy) sources. More details can be
found at www.cv.nrao.edu/nvss
166
A.3
A. All-sky surveys
The Faint Images of the Radio Sky at 20 cm
The Faint Images of the Radio Sky at Twenty-centimetres (FIRST; Becker et al. 1995) has
mapped over 10, 000◦ of the North Galactic Cap at 1.4 GHz (20 cm) continuum with the
Very Large Array. The angular resolution is 5′′ , and the positional accuracy varies from
less than 0.5” at the 3 mJy to 1” at the survey detection threshold of 1 mJy. A discrete
source catalog with ∼ 811, 000 sources has been made publically available. Components of
extended sources are reported separately in the catalog, and it needs to be cautioned that
sources with angular sizes larger than 10′′ are out-resolved by the survey, and therefore
their integrated fluxes are underestimated. More details can be found at sundog.stsci.edu.
A.4
The Infrared Astronomical Satellite (IRAS) Allsky Survey
IRAS is the abbreviation for the Infrared Astronomical Satellite (Neugebauer et al. 1984)
that conducted an almost all-sky survey (∼ 98% of the sky) in four broadband IR photometric channels at 12, 25, 60 and 100 µm. The angular resolution varied between ∼ 0.5′
at 12 µm to ∼ 2′ at 100 µm. The positional accuracy is about 20′′ , or better. Two main
catalogs have been generated: The IRAS Point Source Catalog (IRAS PSC) containing
∼ 250, 000 sources with angular sizes less than approximately 0.5′ , 0.5′ , 1.0′ , and 2.0′ at
12, 25, 60 and 100 µm, respectively; and the IRAS Faint Source Catalog (IRAS FSC). The
sensitivity of the FSC exceeds that of the PSC by about a factor of 2.5. The FSC contains
data for ∼ 173, 000 point sources with flux densities typically greater than 0.2 Jy at 12,
25, and 60 µm and greater than 1 Jy at 100 µm. Although the angular resolution is of
the order of one arcminute, and the faint limits reach only 1 Jy, IRAS remains a highly
valuable resource due to its important wavelength range and nearly full sky coverage. More
details can be found at lambda.gsfc.nasa.gov/product/iras
A.5
2MASS
The Two Micron All Sky Survey used two telescopes located in the northern and southern
hemispheres (Mt. Hopkins, Arizona and Cerro Tololo/CTIO Chile, respectively) to observe
the entire sky in three near IR photometric bands around 2 µm: J (1.25 µm), H (1.65 µm),
and Ks (2.17 µm). The survey’s 10σ point source sensitivity is about 1 mJy. The main
2MASS catalogs contain ∼ 471 million point sources (2MASS PSC) and ∼ 1.65 million
extended sources (2MASS XSC). Details about 2MASS photometry is given in Jarrett et al.
(2000).
A.6 2dF
A.6
167
The 2dF Galaxy Redshift Survey
The 2dF Galaxy Redshift Survey (2dFGRS), integrated with the 2dF QSO survey, is a
major spectroscopic survey performed using the 2dF (Two Degree Field) facility built
by the Anglo-Australian Observatory. About 220,000 spectra for objects brighter than
bJ = 19.45 (extinction-corrected) over ∼ 1500◦ have been obtained yielding reliable
redshifts. More details can be found here: magnum.anu.edu.au/ TDFgg.
A.7
GALEX
The Galaxy Evolution Explorer (GALEX) satellite was launched in 2003 and will eventually
map the entire sky in two photometric ultraviolet bands: NUV (1750 - 2800Å) and FUV
(1350 - 1750Å) down to AB magnitudes of 20 to 25. Further details can be found at
www.galex.caltech.edu.
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A. All-sky surveys
Appendix B
Relevant equations
B.1
The computation of radio luminosity
The rest-frame 1.4 GHz luminosity is calculated in the following way:
L1.4GHz =
4πD2L
F1.4GHz
(1 + z)1−α
(B.1)
where F1.4GHz is the total flux density observed at 1.4 GHz frequency, DL is the luminosity
distance, z is the redshift and α is the spectral index (assuming a synchrotron spectrum
∝ ν −α ).
B.2
Conversions of 1.4 GHz radio luminosity to star
formation rate
The star formation rate for an individual galaxy is directly proportional to its 1.4 GHz
radio luminosity. There are two commonly adopted calibrations. The first was initially
developed by Condon (1992) based on the Milky Way supernovae rate, and later modified
by Haarsma et al. (2000). The second was derived by Bell (2003), based on the total IR –
radio correlation. These two calibrations agree within a factor of ∼ 2.
Condon (1992) used an extended Miller-Scalo (1979) initial mass function (IMF),
ψ(M) ∝ M−2.5 , and modeled the average formation rate of stars more massive than 5 M⊙
(with an upper cut-off mass of 100 M⊙ ). This model was modified by Haarsma et al. (2000)
to account for the formation of lower mass stars (0.1 − 100 M⊙ ), as well as for a different
(Salpeter) IMF, ψ(M) ∝ M−2.35 . The correlation between 1.4 GHz luminosity and star
formation rate given by Haarsma et al. (2000) yields:
SFR [M⊙ yr−1 ] = 1.20 × 10−21 L1.4GHz
(B.2)
where L1.4GHz is the 1.4 GHz radio luminosity in units of W Hz−1 (see Appendix B.1), and
a typical synchrotron spectral index of 0.8 is assumed. Assuming a synchrotron spectral
index of 0.7 instead would yield a conversion factor of 1.16 × 10−21 .
170
B. Relevant equations
The conversion of 1.4GHz luminosity to star formation rate proposed by Bell (2003) is
given as follows:
(
5.52 × 10−22 L1.4GHz ,
L1.4GHz > Lc
(B.3)
SFR [M⊙ yr−1 ] =
5.52×10−22
L1.4GHz , L1.4GHz ≤ Lc
0.1+0.9(L
/L )0.3
1.4GHz
c
where Lc = 6.4 × 1021 W Hz−1 , and L1.4GHz is the 1.4 GHz radio luminosity in units of
W Hz−1 .
The difference in the derived star formation rates using the two calibrations is shown
in Fig. B.1 as a function of 1.4 GHz luminosity. This calibration given by Bell (2003) gives
star formation rates lower by a factor of ∼ 2 for L1.4GHz & 6 × 1021 W Hz−1 compared to
Haarsma et al. (2000). As the luminosity decreases the Bell (2003) star formation rates
approach those above, and become higher for luminosities of L1.4GHz . 2 × 1020 W Hz−1 .
Figure B.1 The ratio of star forma-
tion rates obtained using the 1.4 GHz
luminosity to star formation rate calibration from Bell (2003, eq B.3) and
Haarsma et al. (2000, eq B.2) as a function of 1.4 GHz radio luminosity. There
is a factor of ∼ 2 discrepancy changing
with luminosity.
Acknowledgments
171
“It was the best of times, it was the worst of times”
Charles Dickens, A Tale of Two Cities (1859)
There are many people that have contributed to this path, to whom I am immensely
grateful. First, I would like to thank Korado Korlević who has shown me that limits
do not exist; each individual is equally important for moving the world. I would like to
thank Krešimir Pavlovski for putting his personal effort into creating the opportunities
for my initial scientific work, and for treating me with great respect already during my
undergraduate studies. I am eternally grateful to Željko Ivezić for leading me into the
world’s league of astronomy and for always being a strong support. I thank Hans-Walter
Rix for providing, already during the Vatican Summer School 2003, the chance for me to
do my PhD in Heidelberg.
I would like to thank Chris Carilli for insightful discussions, as well as support and help
related to my entire PhD thesis work. I thank Irini Sakelliou and Alexis Finoguenov for
making X-ray emission from galaxy clusters very understandable. I would especially like
to thank Eric Bell for many insightful discussions related to the scientific projects I have
worked on, and for helping me with technical procedures and details; thanks to Eric Monte
Carlo simulations are a peace-of-cake for me. I would also like to express my gratefulness
to Gianni Zamorani for many discussions, and for his detailed and thorough understanding
of science that contributed immensely to my own scientific advance. I thank Nick Scoville
on detailed comments on my work, as well as for opening the opportunity for me to work
at Caltech. In particular, a big thanks goes to Eva Schinnerer for enabling me to work
on the cutting edge project – COSMOS, for her patience during my beginning in radio
astronomy, for exclusively excellent comments related to the manuscripts I have worked
on, and for mentoring me through this work.
My friends in Heidelberg and Zagreb – Dominik, Florian, Isabel, Marie-Helen, Anja,
Dinko, Ida, Maja-Laura, and Kristina – thanks for fulfilling and embellishing my time here
in Heidelberg. I especially thank Dominik for always patiently listening to my problems,
and always being willing to get some beer after a (very) long working day.
A great ’thank you’ goes to my whole family for their tremendous reliance and help. You
have always been, and you always will be, my greatest support in life. I would especially
like to point out my nephews – Boris, Marko, Martin, Tomislav and Patricia – who always
knew how to make me smile in less than a second. Aunt Hana and Željka, thank for your
expeditious help with the proper translation of the above citation.
Monika, I will always be grateful to you for being with me during my most difficult
moments, for comforting me and making me smile when it was necessary . . . but above all
thank you for staying.
Finally, my greatest thanks goes to my mother Vesna, and my aunt Zlata. Mom, and
aunt, thank you for your constant support and help in each possible sense. Without you I
would not be where I am. You are the reason for who I am.
172
Zahvale
”Bilo je to najbolje vrijeme, bilo je to najgore vrijeme”
Charles Dickens, Priča o dva grada (1859)
Mnogi su ljudi, kojima sam beskrajno zahvalna, doprinjeli ovom putu. Najprije bih
htjela zahvaliti Koradu Korleviću što mi je pokazao da granice ne postoje; svaki individualac je jednako važan u pokretanju svijeta. Zahvaljujem Krešimiru Pavlovskom što mi je
osobnim trudom stvorio prilike za moj pčetni znanstvani rad i što me tretirao s ogromnim
poštovanjem još za vrijeme dodiplomskog studija. Vječno sam zahvalna Željku Iveziću što
mi je omogućio ući u svjetsku ligu astronomije i cijelo vrijeme bio snažan stup oslonac.
Hvala Hans-Walter Rixu što mi je, još u Vatikanskoj ljetnoj školi 2003, stvorio mogućnost
za poslijediplomski studij u Heidelbergu.
Htjela bih zahvaliti Chris Carilliu na podršci i pomoći vezanoj uz moj pslijediplomski
rad te Irini Sakelliou i Alexis Finoguenovu što su mi učinili visoko energetsko zračenje
iz skupova galaksija vrlo jasnim. Posebno hvala ide Eric Bellu na mnogim diskusijama
vezanim uz znanstvene projekte na kojima sam radila te pomoći oko tehničkih procedura
i detalja; zahvaljujući Ericu Monte Carlo simulacije su mi danas sitnica. Takoder zahvaljujem Gianni Zamoraniu na mnogim diskusijama te detaljnom i temeljitom poimanju
znanosti koje je mnogo doprinjelo mojem znanstvenom nadogradivanju; te Nick Scovilleu
što mi je otvorio put u Caltech. Posebno zahvaljujem Evi Schinnerer što mi je omogućila
da radim na odličnom novom projektu – COSMOS – i na strpljivosti za vrijeme mojih
početaka u radio astronomiji te uvijek odličnim komentarima vezanim uz clanke koje sam
pripremala.
Mojim prijateljima u Heidelbergu i Zagrebu – Dominiku, Florianu, Isabel, MarieHelene, Anji, Dinku, Idi, Maji-Lauri i Kristini – puno hvala što su mi ispunjavali i uljepšali
vrijeme provedeno u Heidelbergu. Posebno zahvaljujem Dominiku što je uvijek strpljivo
slušao moje probleme i uvijek bio voljan otići na pivu nakon (vrlo) dugog radnog dana.
Veliko hvala mojoj cijeloj obitelji na podršci i pomoći. Uvijek ste bili, i uvijek će mi
biti, najveći oslonac u mom životu. Posebno bih istaknula klince – Borisa, Marka, Martina,
Tomislava i Patriciu – koji su me uvijek znali oraspoložiti u djeliću sekunde. Teta Hana i
Željka, hvala na ekspeditivnoj pomoći oko pravog prijevoda gornjeg citata.
Monika, uvijek ću ti biti zahvalna što si bila uz mene u najtežim trenucima, tješila me
i oraspoloživala kad je trebalo . . . a najviše od svega što si ostala uz mene. Uz tebe je sve
lakše.
Iznad svega, moje najveće zahvale idu mami Vesni i teti Zlati. Mama i teta, hvala Vam
na konstantnoj podršci i potpori u svakom pogledu. Bez Vas ne bih bila ovdje gdje jesam.
Vi ste razlog zbog kojeg sam to sto jesam.
Curriculum Vitae
Vernesa Smolčić
Max-Planck-Institut für Astronomie
Königstuhl 17
69117 Heidelberg
Germany
Phone: +49-(0)6221-528258
Fax: +49-(0)6221-528246
[email protected]
http://www.mpia.de/homes/smolcic/
Basic information
Nationality: Croatian
Date of Birth, Sex: February 23rd 1980, Female
Languages: Native Croatian, fluent English and German, good Italian,
school knowledge of Latin
Present Position: PhD student, MPIA, Heidelberg, Germany
Future Position: California Institute of Technology, Pasadena, CA, USA;
CARMA Postdoctoral Scholar (2008 – 2011)
Education
Research: 2003 – Present (2007)
− Max-Planck Institut für Astronomie, Heidelberg, Germany
− Princeton University Observatory, Peyton Hall, Princeton, USA
− Department of Physics, Faculty of Science, University of Zagreb, Croatia
PhD Studies and Thesis: 2004 – 2007 (submitted)
− University of Heidelberg
− International Max Planck Research School for Astronomy and Cosmic Physics
− Max-Planck Institut für Astronomie, Heidelberg, Germany
− PhD thesis title: The Faint Radio Population in the VLA-COSMOS Survey:
Star Forming Galaxies and Active Galactic Nuclei
− Supervisors: Dr. Eva Schinnerer, Prof. Dr. H.-W. Rix
Diploma Studies and Thesis: 1998 – 2004
− Department of Physics, Faculty of Science, University of Zagreb, Croatia,
graduated April 16th 2004
− Princeton University Observatory, Peyton Hall, Princeton, USA
− Diploma Thesis: Rest-frame Properties of 99,000 Sloan Digital Sky Survey Galaxies
− Supervisors:
Prof. Dr. Ž. Ivezić, Prof. Dr. K. Pavlovski
174
Curriculum Vitae
Research Interests
−
−
−
−
Extragalactic Star Formation
AGN, Radio Galaxies
Galaxy Clusters, Large Scale Structure
Survey Science: (VLA-)COSMOS, SDSS, multi-wavelength follow-up
I. T. Skills
Programming: IDL, SM, Fortran, AIPS, Latex, HTML, PHP
Operating systems: Linux, Windows
Data reduction: optical imaging (DoPhot), radio synthesis imaging (AIPS)
Membership in Scientific Collaborations and Societies
Cosmic Evolution Survey (COSMOS)
Sloan Digital Sky Survey (SDSS)
Sloan Extension for Galactic Understanding and Exploration (SEGUE)
American Astronomical Society (AAS)
Croatian Physical Society (HFD)
Awards and Honors
2006: Ernst Patzer Award (for best refereed publications by young MPIA scientists)
2006: IAU (International Astronomical Union) grant for the XXVI General Assembly,
Prague
2003: University of Zagreb Rector’s Award (for best research projects by undergraduate
students)
Selected Conferences, Workshops and Summer Schools
2007: Legacy of Multi-wavelength Surveys; International Workshop in Xining, China; invited talk
2006: IAU Symposium 235 : “Galaxy Evolution across the Hubble Time”
2005: Workshop on Multi-wavelength Surveys, Ringberg, Germany
2004: IRAM Summer School: “mm Observing Techniques and Applications”, Grenoble,
France
2003: Vatican Observatory Summer School in Astronomy and Astrophysics: “Observations
and Theoretical Understanding of Galaxy Evolution: From the Local Universe to the
Distant Universe”, Castel Gandolfo, Rome, Italy;
“Spectroscopically and Spatially Resolving the Components of Close Binary Stars
workshop”, Dubrovnik, Croatia, I also helped organizing this workshop
Teaching
2007: Astronomy - astrophysics Lab Course at the University of Heidleberg
2003: Primary school physics replacement teacher (7th and 8th grades) for several weeks
1995–2004: Mathematics, physics and English tutor of primary and high school students
List of publications
Refereed Publications
a) Submitted
20 Smolčić, V., et al., “A new method to separate star forming from AGN galaxies at intermediate redshift: The submillijansky radio population in the VLACOSMOS survey”, submitted to ApJ; Chap. 3 in this thesis
19 Smolčić, V., et al., “The dust un-biased cosmic star formation history derived
using 1.4 GHz data from the VLA-COSMOS survey”, about to be submitted to
ApJ; Chap. 4 in this thesis
18 Sakelliou, I., Smolčić, V., et al., submitted to MNRAS, “The Hot Gas and
Galaxies in Abell 2151”
b) Accepted
17 Younger, J. D., inter alios Smolčić, V., et al., accepted for publication in
ApJ, “Evidence for a Population of High-Redshift Submillimeter Galaxies from
Interferometric Imaging”
16 Smolčić, V., Zucker, D., et al., 2007, AJ, 134, 1901, “Improved Photometry
of Sloan Digital Sky Survey Crowded-Field Images: Structure and Dark Matter
Content in the Dwarf Spheroidal Galaxy Leo I”
15 Adelman-McCarthy, J. K., inter alios Smolčić, V., et al. 2007, ApJS, 172, 634,
“The Fifth Data Release of the Sloan Digital Sky Survey”
14 T. Murayama, Y.Taniguchi, N. Z. Scoville, inter alios Smolčić, V., et al., 2007,
ApJS, 172, 523, COSMOS special issue, “Lyman alpha Emitters at Redshift 5.7
in the COSMOS Field”
13 C. L. Carilli, M. Ajiki, R. Wang, inter alios Smolčić, V., et al., 2007, ApJS, 172,
518, COSMOS special issue, “Radio and Millimeter Properties of z ∼ 5.7 Ly
alpha Emitters in the COSMOS Field: Limits on Radio AGN, Submm Galaxies,
and Dust Obscuration”
176
List of publications
12 M. I. Takahashi, Y. Shioya, Y. Taniguchi, inter alios Smolčić, V., et al., 2007,
ApJS, 172, 456, COSMOS special issue, “The [O II] λ 3727 Luminosity Function
and Star Formation Rate at z ≈ 1.2 in the COSMOS 2 Square-Degree Field and
the Subaru Deep Field”
11 Smolčić, V., Schinnerer. E., et al., 2007, ApJS, 2007, 172, 295, COSMOS
special issue, “A Wide Angle Tail Radio Galaxy in the COSMOS field: Evidence
for Cluster Formation”; Chap. 5 in this thesis
10 A. Finoguenov, inter alios Smolčić, V.,et al., 2007, ApJS, 172, 182, COSMOS
special issue, ”The XMM-Newton Wide-Field Survey in the COSMOS Field:
Statistical Properties of Clusters of Galaxies”
9 Bertoldi, F., inter alios Smolčić, V., et al., 2007, ApJS, 172, 132, COSMOS
special issue, “COSBO: The MAMBO 1.2 mm Imaging Survey of the COSMOS
Fiels”
8 Schinnerer. E., Smolčić, V., et al. 2007, ApJS, 172, 46, COSMOS special
issue, “The VLA-COSMOS Survey: II. Source Catalog of the Large Project”;
Chap. 2 in this thesis
7 Smolčić, V., Ivezić, Ž., et al. 2006, MNRAS, 371, 121, “The Rest-frame Optical
Colours of 99,000 Sloan Digital Sky Survey Galaxies”
6 Obric, M., Ivezić, Ž., inter alios Smolčić, V., et al. 2006, MNRAS, 370, 1677,
“Panchromatic Properties of 99,000 Galaxies Detected by SDSS, and (some by)
ROSAT, GALEX, 2MASS, IRAS, GB6, FIRST, NVSS and WENSS Surveys”
5 Adelman-McCarthy, J. K., inter alios Smolčić, V., et al. 2006, ApJS, 162, 38,
“The Fourth Data Release of the Sloan Digital Sky Survey”
4 Abazajian, K., inter alios Smolčić, V., et al. 2005, AJ, 129, 1755, “The Third
Data Release of the Sloan Digital Sky Survey”
3 Abazajian, K., inter alios Smolčić, V., et al. 2004, AJ, 128, 502, “The Second
Data Release of the Sloan Digital Sky Survey”
2 Smolčić, V., Ivezić, Ž., et al. 2004, ApJ, 615, 141, “A Second Stellar Color
Locus: a Bridge from White Dwarfs to M stars”
1 Abazajian, K., inter alios Smolčić, V., et al. 2003, AJ, 126, 2081, “The First
Data Release of the Sloan Digital Sky Survey”
Non-refereed Publications
a) Proceedings
11 Smolčić, V., et al., 2007, IAUS, 235, 428, “Obtaining the (dust-obscured) star
formation history using the VLA-COSMOS survey”
10 Ivezić, Ž., Lupton, R., Schlegel, D., Smolčić, V., et al., 2004, ASPC, 327, 104,
“Halo Structure Traced by SDSS RR Lyrae”
List of publications
177
9 Smolčić, V., Ivezić, Ž. et al., 2004, ASPC, 318, 396, “Second Stellar Color
Locus: a Bridge from White Dwarfs to M stars”
8 Ivezić, Ž., inter alios Smolčić, V., et al., 2004, ASPC, 317, 179, “Reaching to
the Edge of the Milky Way Halo with SDSS”
7 Obrić, M., inter alios Smolčić, V., et al., 2004, IAUS, 222, 533, “Multiwavelength view of SDSS galaxies”
6 Ivezić, Ž., inter alios Smolčić, V., et al., 2004, mas, conf, 53, “The Distribution
of Quasars and Galaxies in Radio Color-Color and Morphology Diagrams”
b) Abstracts
5 Smolčić, V., et al. 2006, AAS, 209, 8005, “The VLA-COSMOS 1.4 GHz Survey:
The Properties of the Faint Radio Population and Star Formation Rates”
4 Paglione, T., Smolčić, V., et al. 2006, AAS, 209, 8001, “The Luminosity Function of COSMOS Radio Sources”
3 Carilli, C. L., Bertoldi, F., Schinnerer, E., Voss, H., Smolčić, V., et al., 2005,
AAS, 207, 8305, “MAMBO Observations of the COSMOS Field: Probing High
Redshift, Dusty Starburst Galaxies”
2 Schinnerer, E., Smolčić, V., Carilli, C. L., et al., AAS, 2005, 270, 8303, “Radio
Sources in the COSMOS Field”,
1 Smolčić, V., Schinnerer et al., 2005, AAS, 207, 621, “A Wide Angle Tail
Galaxy in the COSMOS Field: Evidence for Cluster Formation”
PhD Thesis
• “The Faint Radio Population in the VLA-COSMOS Survey: Star Forming Galaxies and Active Galactic Nuclei”, 2007, submitted to the University of Heidelberg,
Germany; this manuscript
Diploma Thesis
• “Fotometrijska svojstva galaksija iz SDSS DR1 baze podataka”, 2004, Department
of Physics, Faculty of Science, University of Zagreb, Croatia
178
List of publications
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