Host galaxies and environment of active galactic nuclei

Host galaxies and environment of active galactic nuclei
Host galaxies and environment of active
galactic nuclei
A study of the XMM Large Scale Structure survey
Cover designed by Gilles Tasse.
In the sky: Canada-France Hawaii Telescope
Legacy Survey optical data + Very Large Array
radio data at 325 MHz (contours). At the center of the front page: the radio galaxy J0226.30400.
Host galaxies and environment of active
galactic nuclei
A study of the XMM Large Scale Structure survey
Proefschrift
ter verkrijging van
de graad van Doctor aan de Universiteit Leiden,
op gezag van Rector Magnificus prof.mr. P.F. van der Heijden,
volgens besluit van het College voor Promoties
te verdedigen op donderdag 31 januari 2008
klokke 13.45 uur
door
Cyril TASSE
geboren te Saint-Brieuc (Frankrijk) in 1979
Promotiecommissie
Promotor:
Prof. dr. G. Miley
Co-promotor:
Dr. H. Röttgering
Referent:
Prof. dr. R. Windhorst
(Arizona State University)
Overige leden:
Dr. P. Best
Prof. dr. M. Franx
Dr. P. Katgert
Prof. dr. K. Kuijken
(Royal Observatory Edinburgh)
“There is no absolute up or down, as Aristotle thought; no
absolute position in space; but the position of a body is relative
to that of other bodies. Everywhere there is incessant relative
change in position throughout the Universe, and the observer is
always at the center of things.”
Giordano Bruno,
Cause, Principle, and Unity (1584)
Contents
1
2
Introduction
1.1 Active galactic nuclei: old and newer paradigm
1.2 Triggering processes of the AGN activity . . .
1.3 Galaxy formation: brief sketch . . . . . . . . .
1.4 AGN and galaxy formation . . . . . . . . . . .
1.5 This Thesis . . . . . . . . . . . . . . . . . . .
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Low-frequency observations of the XMM Large Scale Structure field.
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 Observational strategy . . . . . . . . . . . . . . . . . . . .
2.2.2 Data Reduction . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Source list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1 Detection . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.2 Noise dependent errors . . . . . . . . . . . . . . . . . . . .
2.3.3 Calibration errors . . . . . . . . . . . . . . . . . . . . . . .
2.3.4 Completeness: . . . . . . . . . . . . . . . . . . . . . . . .
2.3.5 Extended flux density estimation . . . . . . . . . . . . . . .
2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.1 325MHz Results . . . . . . . . . . . . . . . . . . . . . . .
2.4.2 A Radio halo candidate at 325 MHz . . . . . . . . . . . . .
2.4.3 74MHz Results . . . . . . . . . . . . . . . . . . . . . . . .
2.4.4 Source Identification from Literature: . . . . . . . . . . . .
2.5 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . .
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1
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11
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25
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28
29
Appendices
33
A
Radio images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3
GMRT observations of the XMM large scale structure survey field
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Observations and data reduction . . . . . . . . . . . . . . . . .
3.2.1 Radio Continuum at 610 MHz . . . . . . . . . . . . . .
3.2.2 Radio Continuum at 240 MHz . . . . . . . . . . . . . .
3.3 Source extraction . . . . . . . . . . . . . . . . . . . . . . . . .
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37
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42
Host galaxies and environment of active galactic nuclei
viii
3.4
3.5
3.6
3.7
Determination of source parameters and associated errors
3.4.1 Astrometry . . . . . . . . . . . . . . . . . . . .
3.4.2 Flux densities . . . . . . . . . . . . . . . . . . .
Results . . . . . . . . . . . . . . . . . . . . . . . . . . .
Radio spectra analysis . . . . . . . . . . . . . . . . . . .
3.6.1 The multi frequency radio sample . . . . . . . .
3.6.2 Comparison with VLA data . . . . . . . . . . .
3.6.3 Spectral fits . . . . . . . . . . . . . . . . . . . .
3.6.4 Subsample definition . . . . . . . . . . . . . . .
Conclusion and Future Work . . . . . . . . . . . . . . .
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43
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54
Appendices
57
A
Radio images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Surveys of the XMM-LSS field . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.1 VLA Radio data at 74 and 325 MHz . . . . . . . . . . . . . . . . . . . . .
4.2.2 GMRT Radio data at 230 and 610 MHz . . . . . . . . . . . . . . . . . . .
4.2.3 CFHTLS-W1 optical data . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.4 SWIRE survey data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.5 Field selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Optical and infrared identification of radio sources . . . . . . . . . . . . . . . . .
4.3.1 Visual inspection and classification . . . . . . . . . . . . . . . . . . . . .
4.3.2 Optical identification: the likelihood ratio method . . . . . . . . . . . . . .
4.3.3 Contamination correction . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.4 Completeness and reliability . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.5 Infrared association . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Spectral Energy Distribution fitting . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.1 Theoretical approach: ZPEG . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.2 Semi empirical approach . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5 Subsample selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.1 Selection of the basic sample . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.2 Type-1 AGN contamination . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.3 Starburst selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.6 Output parameters accuracies . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.6.1 ZPEG standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.6.2 The influence of emission lines . . . . . . . . . . . . . . . . . . . . . . . .
4.7 Radio sources’ hosts properties . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.7.1 Basic observed properties . . . . . . . . . . . . . . . . . . . . . . . . . .
4.7.2 ZPEG outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Contents
ix
Appendices
A
Centroid uncertainties for Class 2 sources
B
Comments on individual sources . . . . .
B1
Class 3 sources . . . . . . . . . .
B2
Class 4 sources . . . . . . . . . .
C
Tables . . . . . . . . . . . . . . . . . . .
D
overlays . . . . . . . . . . . . . . . . . .
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91
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98
Radio-loud AGN in the XMM-LSS field: a dichotomy on environment and accretion
mode?
105
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.2 A sample of radio selected AGN in the XMM-LSS field . . . . . . . . . . . . . . . 107
5.3 Intrinsic properties of the host galaxies of radio sources . . . . . . . . . . . . . . . 110
5.3.1 Stellar mass functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.3.2 V/Vmax statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.3.3 Infrared properties of radio sources’ hosts . . . . . . . . . . . . . . . . . . 113
5.4 The environment of the host galaxies of radio sources . . . . . . . . . . . . . . . . 114
5.4.1 The overdensity parameter . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.4.2 The environment of radio sources . . . . . . . . . . . . . . . . . . . . . . 117
5.4.3 Comparison with X-ray selected galaxy clusters . . . . . . . . . . . . . . . 119
5.5 Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Appendices
A
Number density estimator . . .
B
Overdensity estimator . . . . .
B1
Probability functions .
B2
Overdensity parameter
6
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Internal and environmental properties of X-ray selected AGN.
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Multiwavelength dataset . . . . . . . . . . . . . . . . . . . . .
6.2.1 XMM-LSS X-ray survey . . . . . . . . . . . . . . . . .
6.2.2 Optical and infrared surveys . . . . . . . . . . . . . . .
6.3 A sample of X-ray selected Type-2 AGN . . . . . . . . . . . . .
6.3.1 Optical identification . . . . . . . . . . . . . . . . . . .
6.3.2 Spectral energy distribution fitting and sample selection
6.3.3 Extinction correction . . . . . . . . . . . . . . . . . . .
6.4 Properties of X-ray selected AGN . . . . . . . . . . . . . . . .
6.4.1 Basic properties of X-ray selected AGN . . . . . . . . .
6.4.2 Luminosity function . . . . . . . . . . . . . . . . . . .
6.4.3 Stellar mass function . . . . . . . . . . . . . . . . . . .
6.4.4 Infrared properties . . . . . . . . . . . . . . . . . . . .
6.4.5 Environment . . . . . . . . . . . . . . . . . . . . . . .
6.5 Summary and discussion . . . . . . . . . . . . . . . . . . . . .
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Host galaxies and environment of active galactic nuclei
x
Appendices
145
A
Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
7
Summary and discussion
151
7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
7.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
Nederlandse Samenvatting
155
Résumé en français
161
Curriculum Vitae
167
Acknowledgement
169
CHAPTER 1
Introduction
1.1
A  :    
The study of active galactic nuclei (AGN) have lead to some of the most important discoveries in
the last century. Most radio sources detected with the first radio telescopes in the 1950’s, were
identified at optical wavelength with either point-like sources or faint optical galaxies, located
outside the Milky Way. These observations indicated that their radio luminosities were larger than
those of normal galaxy by a few orders of magnitude. Some radio sources displayed a significant
variability on short time scales, implying that the energy was produced within a small 1 − 10 pc
region. Lynden-Bell (1969) proposed that accretion of matter onto super-massive black holes could
produce vast amounts of energy on such small scales. However, in the late sixties, the existence of
black hole was hypothetical, and the processes responsible for such enormous energy production
remained speculative for decades. Nowadays, there is quite substantial evidence that black holes
do indeed exist in the Universe: strong relativistic effects are seen in high excitation iron lines (eg.
Nandra 1997), while at the center of the Milky Way, stars are seen to be orbiting around a mass of
a few million times the mass of the Sun (Genzel et al. 1997).
The zoology of AGN is rich and AGN classification is complicated. Optical quasars are characterised by high ∼ 1013 L⊙ bolometric luminosity associated with a strong UV (the big blue bump)
and X-ray luminosities. They produce broad (∼ 5000 − 10000 km.s−1 ) and narrow (. 1000 km.s−1 )
emission line. Seyfert galaxies can be thought to be the low luminosity (∼ 1 − 5 × 1012 L⊙ ), low
redshift counterparts of optical quasars. Seyferts are classified into two Type 1/2 subclasses, with
the Type 1 showing broad and narrow emission lines while the Type 2 produce narrow emission
lines only. Radio galaxies are radio-loud AGN in general associated with massive, gas-poor elliptical galaxies. Most powerful radio galaxies (P1.4 & 1026 W.Hz−1 ) are known to produce emission
lines, whose luminosity correlates with the radio power (McCarthy 1993). The radio emission is
powered by relativistic jets through synchrotron radiation. Radio galaxies are further classified
into two subclasses (Fanaroff & Riley 1974): the FRI’s have low radio luminosities and are edge
darkened, while FRII’s are the more powerful edge brightened ones. The transition between the
25
−1
two regimes sources occur at Pcut
1.4GHz ∼ 10 W.Hz .
The properties of many of the observationally defined classes of AGN outlined above can be
described in a simple manner by the so called “unified scheme” of AGN. Within that framework,
the energy is produced by a hot accretion disk of baryonic matter infalling onto a super-massive
2
Host galaxies and environment of active galactic nuclei
Figure 1.1: Recent results from large surveys indicate that the probability of a given galaxy to be an AGN
is strongly dependent on its stellar mass. The left panel shows that relationship for AGN selected based on
emission line criteria (Best et al. 2005). The right panel shows the fraction of normal galaxies that are radioloud AGN with P > 1023 W.Hz−1 and the fraction of emission line AGN that are radio-loud with P > 1023
W.Hz−1 (Best et al. 2005). It appears that the probability that a galaxy is classified as radio-loud does not
depend on either it is classified as an emission line AGN, suggesting these phenomenon are statistically
independent at these low radio power.
∼ 106−9 M⊙ black hole. This accretion produces photo-ionising UV radiation and gives rise to
X-ray emission via Compton scattering. An obscuring dusty torus surrounds the accretion disk.
The high velocity dispersion of the gas clouds that are situated within the ∼ 1 pc of the obscuring
torus gives rise to the broad emission lines observed in optical quasars and Seyfert-1 galaxies,
while clouds situated outwards at 10 − 100 pc have lower velocity dispersion and produce narrow
emission lines. Within this framework, depending on viewing angle, the observer either sees the
accretion disk and the broad emission lines, or due to obscuration by the dusty torus, only narrow
emission lines are seen. Variability may be another important ingredient in the understanding of
AGN properties: radio sources extended on cluster scales have lifetimes of & 108 years, while
optically selected AGN may have been active for a few hundred years only.
With the availability of large surveys it has become possible to explore the relationship between
galaxies and the various classes of AGN in great detail (see Heckman & Kauffmann 2006, for
a review), and test the AGN unified scheme. Recent studies indicate that in the local z . 0.3
Universe, AGN which are selected using optical emission line criteria, are preferentially situated
in massive galaxies (Kauffmann et al. 2003), and their structural and environmental properties are
similar to those of the massive early type galaxies, except at high emission-line luminosities, where
signs of recent star formation are found.
However, the radio-selected AGN of low radio power show great differences compared to the
AGN selected using their emission-line luminosity, and it has been suggested by many authors
that the unified scheme faces several problems for this class of objects. Hine & Longair (1979)
have observed that many radio galaxies do not have the luminous emission lines expected in the
framework of the unified scheme (see also Laing et al. 1994; Jackson & Rawlings 1997). These
low-excitation radio galaxies (LERGs) are very common at low radio power, and some of the pow-
Introduction
3
erful FRII radio galaxies are LERGs as well. In addition, neither the expected infrared emission
from a dusty torus is observed (Whysong & Antonucci 2004; Ogle et al. 2006), nor is the accretion
related X-ray emission (Hardcastle et al. 2006; Evans et al. 2006). Most strikingly, the optical
AGN as probed using emission-line criteria and the low radio luminosity AGN phenomenon are
statistically independent (see Fig. 1.1, Best et al. 2005), suggesting these two phenomenon are
triggered by different mechanisms. Furthermore, Best et al. (2005) have shown that the emissionline luminosity per black hole mass falls rapidly at the high black hole mass end, while the radio
luminosity per black hole mass increases. This dichotomy is hardly explainable in terms of variability, because those two types of AGN, selected using criteria based on emission-line luminosity
or radio power, (i) appear to be located in different environments (eg. Best et al. 2005) and (ii)
form statistically independent samples.
Many authors have suggested that there are indeed two distinct classes of AGN. In this picture,
the first class corresponds to a radiatively efficient accretion mode: these AGN show the features
explained by the unified scheme, they have high accretion rates, and they trace a population of
growing black-holes. The second class of AGN, for which there is no evidence that the unified
scheme applies, corresponds to a radiatively inefficient accretion mode, and traces the dormant
population of the most massive black holes (see Heckman et al. 2004; Best et al. 2005; Heckman
& Kauffmann 2006; Hardcastle et al. 2007, for a discussion). It has been suggested that these two
accretion modes are driven by the temperature of the gas reaching the super massive black hole.
Within that framework, the accretion of cold gas produces a radiatively efficient accretion disk,
while the hot gas accretion drives a rather advective accretion, having low radiative efficiency. In
the following, we refer to these two modes as the “Quasar”, or “Cold” mode, and to the “Radio” or
“Hot” mode, respectively. It has been proposed that the type of triggering process might determine
the temperature of the gas reaching the black hole, and drive the accretion type (see Hardcastle
et al. 2007, for a detailed discussion). In this thesis, we test this scheme, in which accretion modes
and triggering processes are closely connected.
1.2
T    AGN 
The question of the physical phenomenon that triggers the AGN activity remain poorly understood.
The two necessary ingredient for making an AGN is a super-massive black hole and a significant
supply of gas to fuel it. To achieve these conditions, a broad range of triggering processes have
been proposed, including major (Petrosyan 1982; Bergvall & Johansson 1995) and minor (eg.
Taniguchi 1999) galaxy mergers, large scale and nuclear bars instability (eg. Wada & Habe 1995),
and inter galactic medium hot gas cooling.
For the low luminosity AGN, the situation is quite ambiguous (Veilleux 2003). The most recent
studies of Seyfert galaxies samples suggest that bar driven gas inflow is not a dominant mechanism
(Ho et al. 1997; Mulchaey & Regan 1997), while Seyfert 2 galaxies tend to have more companion
that the normal galaxies at a 95% significance (De Robertis et al. 1998). In addition, only ∼ 10%
of Seyfert galaxies have companion galaxies (Rafanelli et al. 1995).
For the more luminous AGN, there is quite strong evidence that the galaxy mergers and interactions play an important role. The star forming ultra luminous infrared galaxies (ULIRGS) are in
general seen to be associated with galaxy mergers, while optical and infrared selected quasars tend
to lay in morphologically disturbed hosts (eg. Baker & Clements 1997). Furthermore, ULIRGs
4
Host galaxies and environment of active galactic nuclei
have high bolometric luminosity comparable to the ones of quasars (Sanders et al. 1988a), and
signs of buried quasars have often been observed in these objects (Sanders et al. 1988b). Recently,
numerical simulations (Springel et al. 2005a,b) have shown that galaxy mergers can trigger both
starburst and AGN activity.
Alternatively, it has been suggested that the inter galactic medium (IGM) gas cooling could
also trigger the AGN activity by feeding the black hole. Best et al. (2005) using a sample of
∼ 2000 low redshift z . 0.3 NVSS radio sources (Condon et al. 1998) in the SDSS, showed that
the fraction fRL of galaxies that are radio-loud is strongly dependent on the stellar mass M of the
host galaxy. This relation scales as fRL ∝ M 2.5 , with fractions of radio-loud galaxies as high as
20−30% for galaxies of ∼ 5×1011 M⊙ and radio power P1.4 > 1023 W.Hz−1 . Best et al. (2005) have
suggested that the large quantities of gas that are seen to be cooling in the atmosphere of massive
elliptical galaxies (see Mathews & Brighenti 2003, and references therein) provides a natural way
of triggering the black hole activity, as this hot gas cooling rate Ṁ has the same dependence on
stellar mass ( Ṁ ∝ M 2.5 ).
Whatever the detailed physics of AGN is, the enormous amount of energy they liberate during their short lifetime have great influence on their environment. In the last decade, AGN have
regained attention as they are though to play a major role in the galaxy formation scenarios.
1.3
G :  
The distribution of mass in the local Universe is highly inhomogeneous. The observed Universe
indeed seems to harbour a complex, scale dependent structure: the spacial distribution of stars is
structured on ∼ 10 − 100 kpc scales, and these structures are called galaxies, while the distribution
of galaxies themselves shows a ∼ 1 − 100 Mpc scale called the “large scale structure”. The goal of
galaxy formation theories is to describe and understand the state and evolution of the Universe’s
structure.
The most widely spread and successful cosmological model is the Lambda Cold Dark Matter
(ΛCDM) cosmology. ΛCDM potentially describes theoretically the manner in which the homogeneous early Universe has evolved into the highly inhomogeneous local Universe. With a minimum
of parameters, ΛCDM gives a simple well understood framework for studying galaxy formation,
the contributions to the energy density being a cosmological constant Λ (or dark energy), cold
dark matter, and baryonic matter at levels of ∼ 74%, ∼ 22% and ∼ 4% respectively. In this theory
the Universe’s structure grows hierarchically. It evolves through the gravitational instability in an
expending space: halos of cold dark matter collapse and merge together to form more massive
structures. ΛCDM is successful in accurately describing a great variety of observations such as the
cosmic microwave background (CMB, Spergel et al. 2003), large scale structure Kilbinger (LSS,
2003) and type Ia supernovae surveys (Amendola et al. 2006).
The baryonic matter that represents a lower fraction of the mass density, slides onto the gravitational potential shaped by the dark matter halos. In order to describe galaxy formation at the
smaller scales, physical mechanisms other than gravitational interaction have to be taken into account. The gas is heated by shocks in the deep gravitational potential wells that will later evolve
in galaxy clusters and groups of various masses. The heated gas cools, and the stars are being
formed. Galaxies are thought to be evolving from gas rich late type systems into massive gas poor
elliptical through galaxy mergers and interactions.
Introduction
5
Figure 1.2: This X-ray image (Fabian et al. 2003) of the inner regions of the Perseus-A galaxy cluster
reveals the dynamics of the intra cluster medium is greatly disturbed by the radio-loud AGN activity at the
center of the picture. The 1′ scale corresponds to ∼ 22 kpc. The energy input by radio-loud AGN in the inter
galactic medium may play an important role in theories of galaxy formation. Left panel shows the observed
(squares and circles) galaxy luminosity function as well as results from numerical simulations (Croton et al.
2006). In the absence of AGN feedback (dashed line) mechanism, the model overestimate the number of
luminous galaxies by order of magnitude. Including the energy input from radio-loud AGN produces a
satisfying fit to the data (full line).
1.4
AGN   
Evidence is mounting that AGN activity plays a key role in the framework of galaxy formation:
during their short 106−8 years lifetime AGN produce an enormous amount of energy that is injected
into their surrounding environment through ionising radiation and relativistic jets. The comoving
density evolution of AGN is remarkably similar to the evolution of the total star formation rate
density and to the evolution of the space density of starbursting galaxies. All three rise by ∼ 2
orders of magnitude between z = 0 and z = 2 − 3 (Sanders & Mirabel 1996; Dickinson 1998;
Boyle & Terlevich 1998), suggesting that AGN activity and galaxy formation processes are tightly
connected.
Furthermore, the striking discovery that essentially all nearby galaxies possess a super-massive
black hole at their center, and that the black hole mass is correlated with the bulge mass and
velocity dispersion (Ferrarese & Merritt 2000; Gebhardt et al. 2000) also suggest a strong link
between galaxy formation and black hole growth (ie AGN activity). An interpretation is that the
black hole and the bulge grow together until the AGN is luminous enough so that the radiative
pressure drives winds that expels the cold gas in the intergalactic medium thereby stopping the star
formation (eg. Springel et al. 2005a,b). This AGN feedback in the form of radiative pressure, is
refereed in the literature as the “Quasar mode”.
Attempts to model galaxy formation (Kauffmann et al. 1999; Cole et al. 2000) have used semianalytical models taking into account important physical processes such as galaxy mergers, star
6
Host galaxies and environment of active galactic nuclei
formation, gas cooling, metal enrichment, and supernovae feedback. These models could reproduce the observed shape of the galaxy stellar mass function, except for high stellar masses
(M& 1011 M⊙ ), where it was needed to artificially switch-off the gas cooling inside the most massive dark matter halos, which suggests the existence of a source of heating that balances the inter
galactic medium (IGM) gas cooling. The energy input by relativistic jets of radio-loud AGN may
be a good candidate for solving that issue (see Fig. 1.2 Croton et al. 2005). The energy injection
by radio-loud AGN into the IGM (refereed as the “Radio mode” in the literature) has recently
been witnessed in the form of jet driven bubbles, shocks and sound waves in the X-ray emitting
intracluster medium (ICM) of closeby galaxy clusters (Fig. 1.2, Fabian et al. 2003; Blanton et al.
2004; Fabian et al. 2005). Furthermore, the radio jets and X-ray emitting ICM morphologies have
been observed to be strongly coupled (Croston et al. 2005).
1.5
T T
Where are the different classes of AGN located with respect to the distribution of mass in the
Universe? What are the respective influence of internal and environmental properties on the AGN
activity? What are the mechanisms that trigger the AGN activity? Are there connections between
triggering process and the AGN properties such as the accretion mode (“Quasar mode” versus
“Radio mode”)? How do those relations evolve with redshift? A good way to address these issues
is to study the statistical properties of large samples of AGN.
In this thesis, we select two samples of AGN in the XMM-Large Scale Structure survey (XMMLSS, see Pierre et al. 2004) based on (i) their radio luminosity (Chapter 2, 3, 4, 5) and (ii) their
X-ray luminosity (Chapter 6), our idea being that these samples may be dominated by Radio mode
and Quasar mode AGN respectively. A series of internal and environmental estimators have been
attached to each AGN in these sets including: stellar mass, redshift, and star formation rates of
the host galaxy, infrared excess and overdensity parameter. By studying the bias introduced by
the radio or X-ray selection on the observed internal and environmental properties, we might be
able to address some of the questions outlined above. Bellow is a more detailed description of the
chapter contents.
In Chapter 2 we present a low frequency radio survey of the XMM-LSS field using the Very
Large Array (VLA) at 74 and 325 MHz over 132 and 15.3 degree2 . Given the perturbing nature of
the ionosphere and the width of the field to be surveyed, we paid particular intention to a careful
reduction of the data. At 74 MHz, the resolution is 30′′ , an the obtained median 5σ sensitivity is
∼ 162 mJy/beam. At 325 MHz, we have a resolution of 6.7′′ , a sensitivity of 4 mJy/beam (5σ).
We detect ∼ 1500 radio sources in total.
To enlarge the radio sources sample size, and retrieve information on the radio spectra, in
Chapter 3 we make use of the large collecting area of the Giant Meterwave Radio Telescope
(GMRT) to map out the XMM-LSS field at 240 and 610 MHz. Covered areas are 18.0 and 12.7
degree2 with resolutions of 14.7′′ and 6.5′′ and sensitivity of ∼ 12.5 and ∼ 1.5 mJy/beam (5σ) at
230 and 610 MHz respectively. We have combined these data with the available source lists at 74,
325 (Chapter 1) and 1400 MHz (NVSS, Condon et al. (1998)), to build a multifrequency catalog
containing ∼ 1500 radio sources. By fitting a simple synchrotron radiation model to the brightest
radio sources, we found that ∼ 26% of sources in our sample show signatures of spectral ageing,
Introduction
7
while ∼ 6% show self absorption.
In Chapter 4 we identify the radio sources detected at 74, 240, 325 and 610 MHz with their
optical counterparts using high quality optical catalog and images. For doing this, we used a
modified version of the likelihood ratio method that takes into account a priori knowledge on the
radio sources host galaxy properties. It gives for each radio source a set of optical candidates
with a probability of association. We estimate that ∼ 75% of the radio sources have a detected
optical counterpart, and derive the photometric redshift for the 3 × 106 galaxies in the surveyed
field, including the radio sources hosts. We develop a method for rejecting the radio sources that
are likely to have corrupted photometric redshifts. This method uses two different photometric
redshift method, combined with an optical color-color criteria.
In Chapter 5 we study the properties of the sample of radio-loud AGN defined in Chapter
4, by investigating their internal and environmental properties. For studying the environment of
radio sources, we build a scale dependent overdensity parameter based on the photometric redshift
probability function. The scaling relation between the fraction of galaxies that are radio-loud
and their stellar mass inferred from low redshift studies (Best et al. 2005) is seen to flatten in the
redshift range 0.5 . z . 1.2 redshift. This suggests that the low masses radio-loud AGN were more
numerous in the past. We report an environmental dichotomy: compared to the normal galaxies of
the same mass, the radio-loud AGN are located in large 450 kpc scale overdensities. In contrast,
the lower mass systems prefer large 450 kpc scale underdensities. In addition they show an infrared
excess in the mid inferred, while the higher stellar mass systems do not have an infrared excess.
We argue that the analysis of the dataset presented in that chapter support the picture in which
the radiatively efficient accretion is triggered by galaxy mergers, while the radio mode accretion is
triggered by the gas cooling in the atmosphere of massive ellipticals.
In Chapter 6 we present a sample of AGN selected in the hard [2-10] keV X-ray band, and
carry out a similar analysis done for the sample of radio selected AGN (Chapter 4&5). We first
identify their optical and infrared counterpart, and select a subsample of Type-2 AGN. Based on
the ratio of hard band to the soft band flux ([0.5-2] keV), for each object we estimated the hydrogen
column density in the line of sight, and derive intrinsic, absorption corrected X-ray luminosities.
The X-ray luminosity function of these sources are in good agreement with previous studies conducted in the past. Interestingly, the mass dependency of the fraction of galaxies that are X-ray
AGN is in good agreement with the same relation for the emission line selected AGN. However,
there is a significant normalisation difference between these relations. This is explained in terms
of emission line AGN, which accretion related X-ray emission is strongly absorbed by high column density. In addition AGN in our sample show a strong infrared excess, at wavelength as short
as 3.5 µm and in the whole stellar mass range, while they are preferentially found in underdense
environment. Globally, the environment of X-ray selected AGN resembles the environment of the
low stellar mass radio-loud AGN that are in their radiatively efficient mode. We argue in this chapter that the X-ray selected sample probes a population of AGN that is similar to the population
selected based on their emission lines.
In Chapter 7 we outline the most important results of the thesis. We argue that our data is
consistent with the idea that there is a connection between triggering process and accretion mode.
8
Host galaxies and environment of active galactic nuclei
R
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10
Host galaxies and environment of active galactic nuclei
CHAPTER 2
Low-frequency observations of the XMM
Large Scale Structure field.
C. Tasse, A. S. Cohen, H. J. A. Röttgering, M. Pierre, N.E. Kassim, R. Perley, P.
Best, M. Birkinshaw, M. Bremer, H. Liang
Astronomy & Astrophysics 456, 791, 2006
 e XMM Large Scale Structure survey (XMM-LSS) is an X-ray survey
aimed at studying the large scale structure of the Universe. The XMMLSS field is currently being followed up using observations across a wide range
of wavelengths, and in this paper we present the observational results of a low
frequency radio survey of the XMM-LSS field using the Very Large Array at 74
and 325 MHz. This survey will map out the locations of the extragalactic radio
sources relative to the large scale structure as traced by the X-ray emission.
This is of particular interest because radio galaxies and radio loud AGN show
strong and complex interactions with their small and larger scale environment,
and different classes of radio galaxies are suggested to lie at different places
with respect to the large scale structure.
For the phase calibration of the radio data, we used standard selfcalibration at 325 MHz and field-base calibration at 74 MHz. Polyhedronbased imaging as well as mosaicing methods were used at both frequencies.
At 74 MHz we have a resolution of 30′′ , a median 5σ sensitivity of ∼ 162
mJy/beam and we detect 666 sources over an area of 132 square degrees. At
325 MHz, we have a resolution of 6.7′′ , a median 5σ sensitivity of 4 mJy/beam,
and we detect 847 sources over an area of 15.3 square degrees. At 325 MHz
we have detected a region of diffuse radio emission which is a cluster halo or
relic candidate.
T
12
Host galaxies and environment of active galactic nuclei
2.1
I
Extragalactic radio sources such as radio loud quasars and radio galaxies, have been extensively
studied to understand the physical processes relating active galactic nuclei (AGN), host galaxies,
and environments. For powerful radio sources emission line and radio luminosities seem to be
well correlated (McCarthy 1993), while at lower radio luminosities (L1.4GHz . 1025 W.Hz−1 ) radio
loudness becomes decoupled from the AGN activity as probed by emission line luminosity (Zirbel & Baum 1995; Best et al. 2005). It has been suggested that environment plays a major role
in making a galaxy radio loud or radio quiet. In the local universe, FRI-type (Fanaroff & Riley
1974) radio galaxies inhabit moderately rich cluster environments, while FRII-type radio sources
tend to lie in either small groups or isolated fields (Yates et al. 1989; Hill & Lilly 1991; Ledlow &
Owen 1996). Furthermore a number of recent X-ray observations have shown a strong, FRI/FRII
morphology-dependent coupling between steep spectrum radio emission and their surrounding intergalactic medium (IGM) Fabian et al. (2003); Blanton et al. (2004); Fabian et al. (2005); Croston
et al. (2005). The question therefore arises as to what extent both AGN activity and environment
properties are coupled with radio activity.
One way to statistically study the connections between various radio source populations and
their environment is to compare X-rays emitted by the hot IGM plasma tracing Large Scale Structures (LSS), to low-frequency radio observations (ν < 1GHz). The XMM-Large Scale Structure
Survey (XMM-LSS) is an X-ray survey designed to investigate the nature, properties and content
of the LSS in the Universe up to redshift z ∼ 1 (Pierre et al. 2004). The XMM-LSS field is being
observed by the XMM-Newton satellite and will cover 10 degree2 .It is predicted to detect 1,500
X-ray quasars, and 100 − 200 galaxy clusters up to z = 1, and ∼ 50 within 1 < z < 2 (Refregier
et al. 2002). At present, ∼ 5 degree2 of the XMM-LSS field has been observed.
The XMM-LSS field will be mapped in five bands using the 1 deg2 CCD camera MEGACAM
as part of the Canada France Hawaı̈ Telescope Legacy Survey (CFHTLS1 ). The XMM-LSS field is
also observed as part of the SWIRE (Lonsdale et al. 2003) survey in 9 bands between 3.6 and 24µm.
Spectroscopic follow-up (NTT, Magellan, VLT) of the ∼ 70 galaxy cluster candidates found in the
first 5 degree2 of the survey is completed (Pierre et al 2006, Pacaud et al 2006, in preparation).
Standard spectroscopic follow-up of the SWIRE and XMM sources is underway at the 2-degree
field spectrograph (2dF). Combining these data will provide an unprecedented view of the LSS of
the universe (see Pierre et al. 2004, for a general lay-out of the associated surveys).
Using the Very Large Array (VLA) radio telescope, we have begun a low frequency radio
survey at 325 and 74 MHz of the XMM-LSS field in order to address the following questions:
(1) Where are different classes of radio sources located with respect to the distribution of mass on
cosmological scales as traced by the X-ray emission? (2) Can the radio loud/quiet aspect of optical
and X-ray quasars be understood in terms of environmental effects? (3) How does the hot IGM
influence the physical properties of the radio sources such as linear size and radio power?
In this paper we describe the observations we have conducted using the VLA in July 2003 in
the A-configuration (most extended) and in June 2002 in the B-configuration. This combination
provides resolutions of 6.7′′ and 30′′ at 325 and 74 MHz, respectively. Following the observational
strategy described in Cohen et al. (2003), but using a mosaic of four pointings, at 325 MHz we
cover a ∼ 15 degree2 area with a resolution of 6.7′′ and a mean flux density limit per beam (5σ)
1
for more information on the CFHT Legacy Survey, see http://www.cfht.hawaii.edu/Science/CFHLS/
Low-frequency observations of the XMM Large Scale Structure field.
13
Figure 2.1: Sensitivity and resolution of our XMM-LSS radio surveys, in comparison with that of other
surveys. The dashed lines represent fiducial sources with a spectral index α = −0.8.
of ∼ 4 mJy/Beam. At 74 MHz, we cover a 132 degree2 area with a resolution of 30′′ and a flux
density limit per beam of ∼ 160 mJy/Beam. A summary of these results appear in Table 2.1.
Fig. 2.1 shows the sensitivity and resolution of the XMM-LSS low-frequency counterpart, at
74 and 325 MHz, compared with others radio surveys. At 325 MHz, the low-frequency survey
of the XMM-LSS field is deeper than the WENSS survey (Rengelink et al. 1997) by a factor of
∼ 3, and in resolution by a factor of ∼ 10. At 74 MHz, we exceed the VLSS (Cohen et al. 2006)
by a factor of ∼ 3 in both sensitivity and resolution. The NVSS survey (Condon et al. 1998)
covers the whole XMM-LSS field, and most of our 74 MHz sources will have a counterpart at
1.4 GHz, whereas our 325 MHz data is deep enough so that many sources will not have detected
counterparts in the NVSS. Compared with the VLA-VIRMOS deep field at 1.4 GHz (Bondi et al.
2003), reaching a brightness temperature limit at 5σ of ∼ 90 µJy/Beam, probing nearby starbursts
over ∼ 1 degree2 area, we are probing powerful AGN over a larger area.
This paper is organized as follows. In Section 2.2 we describe the observations, and the
data reduction. In Section 2.3 we describe the sources extraction and we study the reliability/completeness aspects of the source lists. Section 2.4 presents the final results and we conclude
in section 2.5 by discussing the survey, and future plans.
2.2
O
The XMM-LSS field 2 is centered at α(J2000)= 2h 24m 00.27s , δ(J2000)= −4◦ 09′ 47.6′′ (Pierre
et al. 2004). This location was chosen because of its high Galactic latitude and low extinction.
The declination near the equator also gives the advantage of being visible from many astronomical
observatories.
2
for more information on the present status of the XMM-LSS survey see http://vela.astro.ulg.ac.be/themes/spat
ial/xmm/LSS/index e.html.
14
Host galaxies and environment of active galactic nuclei
Table 2.1: Observational parameters for the VLA radio survey of the XMM-LSS field.
Array configuration
Number of pointings
Int. time per pointing
Observing frequency
A
31 Jul. 03,
5 Aug. 03,
3 Sept. 03
4
∼ 6 hrs
73.8/325 MHz
B
15, 16,
17, 20 Jun. 02,
16 Jul. 02
4
∼ 8 hrs
73.8/325 MHz
Frequency
Band Width
Nchannel
Channel Width
Band Pass Calibrator
Flux Calibrator
Resolution
Area (degree2 )
Sensitivity (at 5σ)
73.8 MHz
1.56 MHz
128
12.20 kHz
3C405
3C405
30′′
132
162 mJy/Beam
325 MHz
6.25 MHz
2 × 16
195.31 kHz
3C48
3C48
6.7′′
15.3
4.0 mJy/Beam
Obs. dates
2.2.1 Observational strategy
The first radio observations at 74 and 325 MHz of the XMM-LSS field were carried out with the
VLA (Cohen et al. 2003) in an 8 hour run. It covered 5.6 degree2 at 325 MHz with a resolution of
6.3′′ , and reached a flux density limit per beam of 4 mJy beam−1 (5σ), leading to the detection of
256 sources. At 74 MHz the primary beam covered a 110 degree2 area with a resolution of 30′′ ,
and a flux density limit per beam of 275 mJy.beam−1 (5σ), leading to the detection of 211 sources.
We carried out a 24 hour observation of the XMM-LSS field simultaneously at 74 and 325
MHz in the A-configuration. This observation was spread over July, August and September 2003.
At 325 MHz, we have added data from a ∼ 35 hour observing run in the B-configuration, observed
in June and July 2002. The observational parameters are listed in Table 2.1. The A-configuration
gives us the needed high resolution to determine morphologies of the radio sources, and the B
configuration is used for the determination of reliable flux densities and provides sensitivity to
large angular scale emission needed, for example, to detect giant radio halos. At 74 MHz the
primary beam is large enough to cover the whole XMM-LSS field in a single pointing, and at 325
MHz the pointing grid has been set so that we cover ∼ 90% of the XMM-LSS field with four
pointings.
2.2.2 Data Reduction
For the data reduction, we used the Astronomical Image Processing System (AIPS).
Low-frequency observations of the XMM Large Scale Structure field.
15
The 325 MHz data
After a first round of flagging data affected by Radio Frequency Interference (RFI), we calibrated
the relative response within each channel (i.e. bandpass), and performed gain and amplitude calibration. On the calibrated data, RFI was removed manually using the AIPS’s routines TVFLG and
SPFLG. In each of the four pointings ∼ 15% of the data have been flagged. We combined the A
and B configuration data in the u-v plane using the AIPS routine DBCON.
The phase calibrator we used (3C48) being far from the field, the initial phase calibration is very
poor. Assuming a median spectral index α1400
325 = −0.8, we generate a model of the sky at 325 MHz
from the NVSS database at 1.4 GHz (Condon et al. 1998). This model consists of a list of clean
components located at the position of the sources from the NVSS survey. Fourier transforming the
modeled image allows for a phase-only calibration of each antenna using a solution interval of one
minute, providing better results than the traditional calibrator-based phase calibration.
Because of the very large primary beam and the non-coplanar geometry of the VLA array,
imaging would normally require the use of a 3D Fourier transform. However, this is currently too
computationally expensive to be practical. The commonly used solution is to compute a pseudothree-dimensional Fourier transform (Perley 1999), in which the field of view is divided into much
smaller fields (facets). The 3D Fourier transform can then be approximated by using a two dimensional one. For the 325 MHz data, we used 286 facets, each 512 × 512 pixels, sampled at
1.5′′ .pixel−1 , with an overlap of two pixels between the facets. After the u-v data have been imaged into individual facet and deconvolved, the facets are combined into a single image. After a
few iterations of phase-only self-calibration, we combined the four pointings (each 2.5 degree in
diameter) as described by Condon et al. (1998) in a 15.3 deg2 single map.
The resulting noise is quite inhomogeneous across the field, ranging between ∼ 0.5 and ∼ 2.5
mJy beam−1 . The noise is higher close to the bright sources, where sensitivity is dynamic-range
limited. In each of the four pointings, the size of the synthesized beam was ∼ 6.7′′ × 6.3′′ and we
have set the restoring beam to be a circular Gaussian with a 6.7′′ FWHM.
The 74MHz data
For the 74 MHz data, we used Cygnus-A (3C405) as bandpass and flux density calibrator. Due to
its large angular size, the calibrator is resolved by our observation. To calibrate the data, we used
a standard model available from previous observations.
The problems of RFI and the non-coplanar geometry are solved in a similar way as with the
325 MHz data. However, at 74 MHz the ionosphere poses an additional challenge. Electrons in
the ionosphere produce distortion of the wavefront and the resulting phase shifts ∆φ increases linearly with the wavelength (Kassim et al. 1993). Moreover at higher frequencies the primary beam
is relatively smaller in size so that angle-variant phase shifts across the field-of-view can be ignored and standard self-calibration can be utilized to derive one time variable phase correction per
antenna. Below 150 MHz positional-dependent phase variations become significant, and simple
angle-invariant self-calibration breaks down. Therefore at 74 MHz we have used the technique
of ”field-based calibration” first developed for the VLSS survey in which the phase calibration is
position-dependent across the field-of-view (Cotton et al. 2004). This technique was also used and
discussed into much details in Cohen et al. (2003).
16
Host galaxies and environment of active galactic nuclei
Combining the A and B-configuration data in the UV-plane didn’t lead to much improvement,
probably because of complications of the ionospheric calibration routine. We therefore only considered the higher resolution A-configuration data. For each pointing, after the uv plane has been
imaged in the 184 facets, we used a circular Gaussian restoring beam of 30′′ FWHM. The four
pointings have then been combined into a single map. As with the 325 MHz data, the noise is
inhomogeneous across the field and as low as ∼ 20 mJy/Beam and as high as ∼ 55 mJy/Beam near
bright sources.
2.3
S 
2.3.1 Detection
As a first step in our source finding algorithm, we have normalized the image by a noise map,
produced using AIPS’s task RMSD. The noise is calculated within windows, fitting a Gaussian to
the histogram of the pixel values. The data above and below the 3σ domain are rejected, and after
30 iterations, a reliable estimation of the noise is obtained. The size of the window is critical since
if it is too small, the noise evaluation will be overestimated by the presence of a strong signal,
while if it is too big, it will not take into account the smaller scale variation in the noise pattern.
We set the window to be 80 × 80 pixels, corresponding to 10′ × 10′ and 2′ × 2′ , at 74 and 325 MHz,
respectively, and in order to save computing time, the rms is evaluated every 3×3 pixel, which is of
the order of the correlation length. This introduces pixel-to-pixel uncorrelated noise, and in order
to avoid for discontinuous variation in the noise level, we convolve this noise map with a circular
Gaussian with diameters of 100′′ and 20′′ at 74 and 325 MHz respectively which slightly smooths
the noise image. Fig. 2.2 shows the area mapped as a function of sensitivity for each frequency.
Dividing the original map by the noise map we get an image containing uniform noise, where
we can apply the AIPS’s source extraction algorithm ’Search And Destroy’ (SAD). SAD is given
an input cut of 5 in the noise-normalized map (↔ 5σ in the original map) applied on both peak and
integrated flux density, above which each pixel group is considered as a potential source (island).
SAD fits one or more Gaussian components to these islands, thereby producing an initial source
list. Assuming the noise distribution to be Gaussian, the input cut of 5σ leads to a total number of
false detection over the surveyed areas of . 5.10−2 at 325 and 74 MHz. A visual inspection of the
residual map does reveal some false detections, due to the non-Gaussian, correlated nature of the
noise in the proximity of bright sources. We have deleted these false detections from the list while
comparing the source list positions with NVSS as described below.
2.3.2 Noise dependent errors
Finally, the absolute flux densities are obtained by multiplying the measured noise-normalized flux
density by the local noise. Following Condon (1997) we calculate the true uncertainties, from the
signal-to-noise ratio of the Gaussian fit ρ, as expressed by:
θ M θm
ρ =
4θ2N
2

!2 αM 
!2 αm 2

SP
θN  
θN 
1 +
 1 +

θM
θm
σ2map
(2.1)
Low-frequency observations of the XMM Large Scale Structure field.
17
Figure 2.2: Area as a function of limiting flux density per beam at 5σ. The full line corresponds to the 74
MHz survey whereas the dashed one corresponds to the 325 MHz survey.
where θ M and θm are fitted FWHMs of the major and minor axes, θN is the FWHM of the Gaussian
correlation length of the image noise, corresponding to the FWHM of the synthesized beam, S P
is the peak flux density, and σ2map is the local noise variance. {α M , αm } have values determined
empirically using Monte-Carlo simulations (Condon 1997, see Tab. 2.2). We have then calculated
the errors of the fitted parameters as follows:
σ2 (x0 )
σ2 (y0 ) σ2 (θ M )
σ2 (S P )
=
8
ln
2
=
8
ln
2
=
θm2
S 2P
θ2M
θ2M
!2
σ2 (θm ) σ2 (φ) θ2M − θm2
2
=
=
≈
θm2
2
θ M θm
ρ2
θ2N
σ2 (S T ) σ2 (S P )
≈
+
θ M θm
S T2
S 2P
!
σ2 (θ M ) σ2 (θm )
+
θm2
θ2M
!
(2.2)
(2.3)
Here S T is the total flux density, φ is the position angle of the major axis. σ(x0 ) and σ(y0 ) are
related to the uncertainties in right ascension and declination (respectively σα, f it and σδ, f it ) by the
Table 2.2: Values of {α M , αm } used for the calculation of error bars on individual parameters (Condon 1997).
Parameter
Sp
θ M , x0
θm , y0 , φ
α M αm
3/2 3/2
5/2 1/2
1/2 5/2
18
Host galaxies and environment of active galactic nuclei
Figure 2.3: Positional differences in right ascension and declination between our source sample and NVSS
at 325 (on the left) and 74 MHz (on the right). The 74 MHz source sample corresponds to a much brighter
source population in NVSS than the 325 MHz source sample counterpart, so that the scatter in positional
differences on the right reflects the 74 MHz dataset calibration errors. At 325 MHz, the scatter is dominated
by the NVSS uncertainties.
relations given by Condon et al. (1998):
σ2α, f it = σ2 (x0 ) sin2 (φ) + σ2 (y0 ) cos2 (φ)
(2.4)
σ2δ, f it = σ2 (x0 ) cos2 (φ) + σ2 (y0 ) sin2 (φ)
(2.5)
2.3.3 Calibration errors
- Position errors: The imperfect phase calibration adds positional uncertainties. We can quantify
these by comparing our astrometry measurements to a much more accurate source positioning
catalog. Fig. 2.3 shows all the position differences between the NVSS survey and our source
samples at both 74 and 325 MHz on both right ascension and declination. At 325 MHz, the mean
value of the position differences do not show any significant offset, while at 74 MHz we measure
an average offset of 2.25′′ and −0.4′′ on respectively right ascension and declination. We have
removed these offsets in the final source list. The scatter around the NVSS positions is given by:
2
2
2
σ2α = ǫα,calib
+ ǫα,NVS
S + σα, f it
(2.6)
2
2
2
σ2δ = ǫδ,calib
+ ǫδ,NVS
S + σδ, f it
(2.7)
where ǫα,calib and ǫδ,calib are the calibration errors due to the ionosphere, ǫα,NVS S and ǫδ,NVS S are
the calibration errors of the NVSS sources, and σα, f it and σδ, f it are the Gaussian fitting errors (eq
1-5).
Low-frequency observations of the XMM Large Scale Structure field.
19
At 325 MHz, the scatter in the distribution of the positional differences between the 325 MHz
single source population and their associated NVSS counterpart contains the fitting errors of both
ours and NVSS databases, as well as calibration errors. Therefore, selecting only the sources with
higher signal-to-noise ratio lower the noise-dependent error contributions. Selecting the 325 MHz
sources with associated NVSS counterparts having error bar lower than 0.6′′ gives a subsample
of 41 sources with associated Gaussian fitting error contributions on the level of 0.05′′ . For this
high signal-to-noise ratio subsample, we find scatters of σα = 1.15′′ and σδ = 1.83′′ . Compared
with the NVSS noise-independent uncertainties of ǫα,NVS S = 0.45′′ and ǫδ,NVS S = 0.56′′ , our values
are much higher. We can explain this difference by the fact that we have used an NVSS-based
model to phase calibrate the data which included low signal-to-noise NVSS sources, where noisedependent uncertainties dominate noise-independent ones. Therefore, we consider the quadratic
differences between our measurement of the position uncertainties of respectively 1.15′′ and 1.83′′
and the NVSS calibration errors to be a good estimate of our calibration errors. This leads to
ǫα,calib = 1.06′′ and ǫδ,calib = 1.75′′
At 74 MHz, we get scatter values of σα = 6.8′′ and σδ = 3.7′′ for the whole population, which
contains uncertainties coming from both Gaussian fitting errors and calibration errors, as for the
325 MHz data. Although the NVSS resolution is lower than ours, we assume the position errors
from NVSS to be negligible, as the signal-to-noise ratio of NVSS is on average ∼ 10 times greater
(see Fig. 2.1). In order to quantify the calibration errors, we select sources with σα, f it and σδ, f it
lower than 0.5′′ , which makes a subsample of 43 sources detected at high signal-to-noise ratio.
We find standard deviations in right ascension and declination of respectively σα = 3.43′′ and
σδ = 2.14′′ , which, subtracting the NVSS calibration error contribution, leads to ǫα,calib = 3.37′′
and ǫδ,calib = 2.00′′ . We have quadratically added these errors to the Gaussian fitted ones.
- Flux density errors: For the flux density calibration, we have used 3C48, and 0137+331, at
74 and 325 MHz respectively. We have assumed the flux density of these sources to be reliable at
the level of 5% (see Cohen et al. 2003). We therefore have an uncertainty at the level of 5% on the
overall flux density scale. We have quadratically added that uncertainty value to the noise-based
Gaussian fitting error, given for the peak and integrated flux densities.
- Source size errors: As discussed in detail by Cohen et al. (2003), at 74 MHz incompletely
corrected ionospheric effects, which are similar to “seeing” effects in the optical domain, are hard
to quantify, as we would need to know the actual source sizes. In order to evaluate the effects of
the seeing we define the fitted size of resolved source of diameter θ2source to be:
2
θ2f it = θ2source + θbeam
+ θ2seeing
(2.8)
Here θbeam is the beam size, and θ seeing corresponds to the size of a point source, deconvolved
from the beam and imaged with that level of seeing. Fig. 2.4 shows the scatter of the extendedness
of the whole source population at 74 MHz as estimated by S t /S p .
Although we do not have any information on the actual source sizes of the individual sources,
assuming the bulk of the source population is unresolved, we can directly get an upper limit on the
actual value of the seeing. The median value med(S t /S p ) = 1.48 gives an upper limit on the seeing
of 20.7′′ , and in order to be conservative, we have considered its lower limit to be 0′′ .
20
Host galaxies and environment of active galactic nuclei
Figure 2.4: At 74 MHz: the ratio of the integrated to the peak flux density of each source vs. the peak flux
density. Even if we cannot disentangle the discrepancy between the effects of seeing and physical size, for
the seeing effect estimation we take the median value as a good upper limit (dashed line), assuming that the
sources with that size are actually unresolved.
2.3.4 Completeness:
In order to quantify the source detection efficiency, we have computed a Monte-Carlo simulation,
generating 1000 sources with peak flux densities between 4 and 12σ in a 2800 × 3100 pixel image,
cut from the residual map. Fig. 2.5 shows the number of undetected sources as a function of the
signal-to-noise ratio. A function of the following form, gives a fit to the Monte-Carlo outputs:
fm (S p /σl ) = 1.13(
Sp
− 3.67)−2.68
σl
(2.9)
where S p is the peak flux density and σl is the local noise value. We can see that ∼ 95% of the
sources are detected above 7σ, and this value could be a reasonable estimation of the completeness level. Though assuming the missed fraction to be known, we can correct the source counts
estimation by compensating down to the 5σ level the SAD detection inefficiency. We define an
effective area element as the integration of a surface element weighted by the detection efficiency
(1 − fm (S p /σl )). The total effective area at each flux density level S is the integration of that
quantity over the domain where the local noise σl is as σl < S /5:
Ae f f (S ) =
Z
σl <S /5
[1 − fm (S p /σl )]dA
(2.10)
Low-frequency observations of the XMM Large Scale Structure field.
21
Figure 2.5: The fraction of the missed sources as a function of SNR, from Monte-Carlo simulation, and
fitted curve (dashed line).
The source count estimator N(> S 0 ) is then corrected as follows:
Nc (> S 0 ) =
Z+∞
n(S )dS
Ae f f (S )
(2.11)
S0
We have computed the uncertainties from the source counts, assuming Poisson statistics:
X 1
σ (N > S 0 ) =
Ni
A2i
i
2
Here Ni is the number of sources per bin, and Ai is the area used in the source count calculation.
2.3.5 Extended flux density estimation
The source extraction method using Gaussian fitting algorithm can lead to an underestimation
of the integrated flux densities, when significant part of the emission are extended. In order to
quantify this effect, we derive another estimation of the integrated flux density inside a 60′′ diameter aperture, centered at the position of each Gaussian component. Then we have S int =
Σ × 4ln2/(π.FWHM), where S int is the integrated flux density, Σ is the sum of the pixels inside the aperture, and FWHM is the Full Width at Half Maximum in pixels. Fig. 2.6 shows the
cumulative probability distribution of the noise-normalized difference between the two flux density estimates for subsamples of unresolved point-like sources, and resolved, or multiple sources.
Using a Kolmogorov-Smirnoff test, we compare both distributions with a purely Gaussian distribution. We derive probability values for the distributions to be Gaussian of PKS ∼ 0.7 for point like
22
Host galaxies and environment of active galactic nuclei
sources and PKS << 10−4 for extended sources, indicating that the flux density estimates by the
two methods are in agreement for point-like sources and obviously disagree for resolved sources.
The median value of the ratio between the flux densities derived from the two methods for extended
sources gives the average bias to be ∼ 7%. Therefore, at 325 MHz, since the error bars generated
by the pixel based method are much higher, when the significance of the difference between the
two estimates is above 2σ, we derive the integrated flux density following the pixel-based method,
rather than summing over the Gaussian component individual flux densities. At 74 MHz, the resolution being much larger, most sources are unresolved and the Gaussian fitting based integrated
flux density estimation is reliable.
Figure 2.6: The cumulative probability distribution of the noise-normalized difference between the two flux
density estimates derived by the Gaussian fitting method (“int Gauss” label ) and the pixel-based method
(“int pix” label). The solid line represents a subsample of single, unresolved sources, whereas the dashdotted line represents the extended sources. The purely Gaussian distribution is over-plotted the dotted
line.
2.4
R
2.4.1 325MHz Results
At 325 MHz using the extraction method described above, we extract 877 sources from the 15.3
degree2 combined map. This sample contains a significant number of obviously false detections
and by visual inspection, we rejected 30 that were close to the brightest sources where the noise is
non-Gaussian.
We have defined as multiple sources those separated by less that 60′′ , and assuming Poissonian
statistics this makes the probability of two independent sources to be classified as multiple lower
Low-frequency observations of the XMM Large Scale Structure field.
23
Figure 2.7: The top panel shows the Euclidean normalized differential source count at 325 MHz. The
values with doted error bar are uncorrected, whereas the error bar in full line show the differential source
count corrected from the noise variations within the map and from the source finding algorithm efficiency
falling steeply bellow 10σ. The differential source count from deep 325 MHz survey (Wieringa 1991) is
over plotted in dashed line. The bottom panel shows the Euclidean normalized differential source count at
74 MHz. The dashed lines shows the Wieringa (1991) differential source count extrapolated from 325 to
74 MHz using different spectral index in the 0 to −1 range. Dots are showing the differential source count
extrapolation derived from Monte-Carlo simulation, taking in account spectral index dispersion σ(α74
)=
325
0.24 of a typical radio source population (De Breuck et al. 2000).
24
Host galaxies and environment of active galactic nuclei
than 1%. We finally arrive at a list of 847 sources in which 621 are single (“S”), and 226 are
multiple3 (“M”). We have defined as unresolved sources the ones distinguishable from the beam
size at the 2σ level, and on the 621 single component sources, 484 where unresolved. The final
source list appears in Tab. A14 . Images of the multiple component sources larger than twice the
beam size are shown in Fig. A1.
Since the Cohen et al. (2003) radio sources are all detected in our deeper and wider survey,
at the same frequency, we can directly compare their flux densities. We found that Cohen et al.
(2003) flux densities are on average higher than our flux densities by ∼ 20%. In order to address
this issue, we built radio spectra of ∼ 200 radio sources using the 74 MHz flux densities (that
are in agreement with Cohen et al. 2003, see section 2.4.3), and 1.4 GHz flux densities retrieved
from the NVSS database. Also, we considered two more measurements obtained in August 2004
by the Giant Meterwave Radio Telescope (GMRT) at 230 and 610 MHz, covering the same field
at comparable depth (Tasse et al. in prep.). Selecting a sample of single, unresolved sources at
325 MHz, most often, when the general trend of the radio spectra doesn’t indicate any spectral
aging or self absorption break, the flux density estimate by Cohen et al. (2003) is a poorer match
to the physical synchrotron power low spectra, whereas the new flux density estimates and their
associated error bars are well compatible with a power law spectra. We therefore conclude the flux
densities published in Cohen et al. (2003) to be overestimated by ∼ 20%, and the flux densities
presented here to be more reliable. Although not understood, the overestimation on the fluxes of
Cohen et al. (2003) might be due to instrumental or algorithm errors.
Fig. 2.7 shows the Euclidean normalized differential source count. We have calculated the
source density in each flux density bin as described in the previous section, compensating for
noise inhomogeneities across the image. As the error bars have been computed considering the
decreasing effective area, we claim that the derived source counts are correct down to the 5σ level,
where σ is the minimum noise value in the noise map, so that 5σ ∼ 2.5 mJy/beam. Comparing the Euclidean normalized differential source count estimation from a deep Westerbork survey
(Wieringa 1991), we find good agreement at low flux densities, where we have applied the efficiency corrections, but at flux densities higher than 30 mJy, they appear to differ by a factor of
∼ 2 − 3, which we attribute to either cosmic variance or resolution difference.
We have compared our source list to the NVSS database, looking for 1.4 GHz counterparts
within 45′′ of each source. The total integrated flux density used to derive the spectral index is
calculated as described in section 2.3.5. The resolution difference between our data (6.7′′ ) and
NVSS (45′′ ), makes the estimation of accurate spectral index less robust, as we can miss some of
the extended emission seen in NVSS. This probably leads to a small over estimate of the spectral
index for the extended sources, and we would need to match resolution to correct for this effect.
Out of the 847 detected sources at 325 MHz, we have found 566 sources to have NVSS counterparts, and for the 281 remaining sources we give an upper limit on their spectral index based
on the NVSS detection limit. Fig. 2.8 shows the spectral index distribution, and the corresponding flux density and completeness limit of NVSS. We have derived the median spectral index
α1400
325 ∼ −0.66, for the whole radio source sample, excluding the unidentified sources in NVSS,
3
The largest multiple sources J0217.0-0449*, J0227.2-0325*, J0216.3-0245*, do not satisfy the < 60′′ criteria, but
regarding at the morphology it is obvious that are actually multiple, see Fig. A1.
4
In the electronic version of this paper only
Low-frequency observations of the XMM Large Scale Structure field.
25
and α1400
325 (S 325 > 0.05 Jy) ∼ −0.72, on the brightest sources subsample. Fig. 2.9 shows we find
close agreement between the spectral index distributions of a S > 0.05 Jy subsample, and the De
Breuck et al. (2000) Gaussian fit to the spectral index distributions derived from the WENSS/NVSS
surveys flux densities (S 325 > 0.05 Jy subsample).
2.4.2 A Radio halo candidate at 325 MHz
Giant radio halo and relic radio sources are generally diffuse low surface brightness sources with
steep spectra and typical physical sizes of ∼ 0.1 − 1 Mpc. They are found in rich environment,
showing signs of cluster merger activity (for extended reviews on the subject see Feretti 1999;
Sarazin 2005).
An extended, low surface brightness object of ∼ 1.9′ along the right ascension axis, and ∼ 0.9′
along the declination axis is detected at 325 MHz at α(J2000)= 2h 19m 42s , δ(J2000)= −4◦ 00′ 30′′ .
Since that source is extended on the scale of the box being used for the local noise calculation, the
peak flux density was below the 5σ level after the local noise normalization. We have therefore
extracted the Gaussian components of that source on the original map, which appear together with
the other sources in Tab. A24 . Using AIPS’s task TVSTAT, we find its integrated flux density to
be 150.7 ± 12.5 mJy. We detect the diffuse emission counterparts at 74 MHz and 1.4 GHz (NVSS,
Condon et al. 1998) at flux density levels of 1.34 ± 0.2 Jy, and 27.7 ± 1.8 mJy respectively. This
1400
makes the spectral indexes to be α325
74 ∼ −1.48 ± 0.15 and α325 ∼ −1.16 ± 0.1. We have looked
for counterparts using the NED databases (NED Team 1992), and Fig. 2.10 shows the overlay
between an image retrieved in the Digital Sky Survey (DSS) and the radio halo contours. We have
found four objects classified as galaxies in Maddox et al. (1990).
Based on the morphology and the fairly steep spectral index, we suggest that this object is a
good candidate for either a radio halo, a radio relic or both. The difference between the spectral
1400
indexes α325
74 and α325 suggests the presence of more than one electron population. The contour
lines on the east side of the object shows a steep fall-off of the surface brightness, which indicates
the presence of a shock. A polarization observation of the halo could confirm the relic origin of
its diffuse emission. Visually, it looks like a galaxy overdensity and this diffuse emission likely
belongs to a galaxy group or cluster as discovered in recent, similarly observed objects at low
frequencies (Kassim et al. 2001). The detection of diffuse X-ray emission would confirm the
cluster identification, but unfortunately, the ∼ 5 degree2 X-ray field does not overlap the source.
2.4.3 74MHz Results
At 74 MHz, on the criteria outlined above, we detect 686 sources. Matching the source list with
NVSS 1.4 GHz objects, we find 20 sources to be false detections due to the correlated sidelobe
noise close to bright sources. As for the 325 MHz data, any sources closer than 60′′ have been
classified as multiple. Of the 666 remaining sources, 615 have been classified as single (S) and 51
as multiple (M) (see Fig. A2). Of the 615 simple sources, 465 were unresolved. Yet, as discussed
in section ??, the size measurement at 74 MHz have very high uncertainties, as the seeing effect is
poorly defined.
As for the 325 MHz dataset, we have checked for the consistency between our and Cohen et al.
(2003) observed flux densities at 74 MHz. Results do not show any significant overall offsets in
26
Host galaxies and environment of active galactic nuclei
Figure 2.8: Spectral index distribution at 325 MHz (top panel) and 74 MHz (bottom panel), derived from
comparison with flux density of each radio source counterpart at 1.4 GHz in the NVSS. The flat dashed lines
= −0.72. In the 325
on both plots represents the median value being respectively α1400
= −0.72 and α1400
74
325
MHz spectral index distribution plot, the dashed lines on the left correspond to the spectral index reachable
as a function of the 325 MHz flux density, with respect to the completeness, and flux density limit levels of
NVSS.
Low-frequency observations of the XMM Large Scale Structure field.
27
Figure 2.9: Spectral index distribution (S > 0.05 Jy) comparison with De Breuck et al. (2000) Gaussian
fits, based on the WENSS/NVSS surveys. Dashed line and dashed-dotted line show the spectral index
distribution of the steep-spectrum and the flat-spectrum radio source population respectively (De Breuck
et al. 2000).
between the two measurement, as opposed to the 325 MHz observations. In addition, we compare
our flux density estimates to the VLSS radio survey at 74 MHz (Cohen et al. 2006). Again, flux
differences are compatible with the error bar estimates.
At 74 MHz, assuming α = −0.8 the corresponding flux density limit at 1.4 GHz is about an
order of magnitude higher than the NVSS flux density limit. Consequently, all the 74 MHz sources
have a counterpart in the NVSS database within a 60′′ radius. Moreover our 30′′ resolution 74 MHz
map roughly matching the NVSS resolution of 45′′ , we expect the spectral index estimation to be
highly reliable. The spectral index distribution is shown in fig. 2.8, and we find a spectral index
median value of α1400
74 = −0.72.
Fig. 2.7 shows the Euclidean normalized differential source count with and without applied
corrections. In order to compare that result, we can analytically extrapolate the relation given by
Wieringa (1991) from 325 MHz to 74 MHz, assuming various mean spectral indices (hα325
74 i={−1.00,
−0.75, −0.50, −0.25, 0.00}). Also, to take in account the spectral index dispersion of the radio
source population at 325 MHz, we have conducted an extensive Monte-Carlo simulation, by generating a radio source population following the Wieringa (1991) source counts at 325 MHz, and
then giving each source a random spectral index following a Gaussian distribution with mean val325
ues of hα325
74 i, and a dispersion of σ(α74 ) = 0.24 (De Breuck et al. 2000). Extrapolating the flux
density of each source to 74 MHz, we can build the differential source count at 74 MHz. Fig.
2.7 shows that the analytical and Monte-Carlo extrapolated differential source counts are in close
agreement. The Euclidean normalized differential source count of our sample roughly corresponds
1400
to the extrapolation done with α325
= −0.72
74 ∼ −0.5 which contrasts with the median value α74
found between our 74 MHz sources and their NVSS counterparts at 1.4 GHz. This suggests a flat-
28
Host galaxies and environment of active galactic nuclei
Figure 2.10: Radio contours of the giant radio halo candidate detected at 325 MHz overlaid with an image
retrieved from Digitized Sky Survey. The contours corresponds to levels of 1.5 mJy/Beam×{1, 1.4, 2, 2.8...}
tening of the spectrum at ν < 325 MHz, likely due to the sources being synchrotron self absorbed.
2.4.4 Source Identification from Literature:
We have searched for published data on all objects we have detected at both frequencies, using
the NASA/IPAC Extragalactic database (NED / NED Team 1992). We set the searching radius at
6′′ for the individual object search, and at 3′ for the galaxy cluster search. On the 1460 detected
objects at 74 and 325 MHz, 34 with known redshift have been identified optically as QSO, Galaxy,
or are expected to belong to a galaxy cluster, and we have only selected the ones with their redshift
determined (See Table 2.3). All of them have been identified at 74 MHz and only four have also
been identified at 325 MHz. This appears to be a selection effect as the survey at 74 MHz probes a
much brighter source population and an area which is ∼ 7 times larger than at 325 MHz. Fig 2.11
shows the redshift distribution of our identified source sub-sample.
Low-frequency observations of the XMM Large Scale Structure field.
29
Figure 2.11: Redshift distribution of our whole radio source sample identified with NED, and with measured
redshift for the QSOs, galaxies, and galaxy cluster.
2.5
C  F W
We have mapped the XMM-LSS field over ∼ 130 and ∼ 15 squares degrees at 74 and 325 MHz
respectively, detecting ∼ 1500 sources in total. We detect one source of diffuse, steep-spectrum
emission, which is a candidate for a radio halo or relic. The Euclidean normalized differential
source counts at 74 MHz are consistent with previous studies assuming α74
325 ∼ −0.5, suggesting a
flattening of the spectrum of radio source population for ν < 325 MHz.
In the near future we will combine the VLA data at 74 and 325 MHz with observations from
the GMRT (Giant Meterwave Radio Telescope) at 230 and 610 MHz of the XMM-LSS field,
adding two additional frequencies. Cross-correlating this data with upcoming X-ray and optical
observations will allow us to probe in detail the low frequency spectrum of a large radio galaxy
sample, and to determine the influence of the small and large scale environment on the radio source
properties, such as linear size, and radio power.
A
The authors have made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the
Jet Propulsion Laboratory, Caltech, under contract with the National Aeronautics and Space administration. Basic research in radio astronomy at the Naval Research Laboratory is funded by the Office of Naval
Research. The authors thank the referee for a number of helpful suggestions.
30
Table 2.3: Objects identified in NED database with known redshifts.
Name
Decl. Radio Source
(J2000)
−02 11 51.93
−02 11 59.60
−02 56 41.32
−02 22 54.27
+00 08 59.32
−04 44 05.12
−00 15 47.02
−09 23 39.64
−00 15 07.11
−01 43 49.67
+00 27 59.65
−01 56 53.07
−08 26 09.02
−07 52 58.85
−00 35 32.50
−00 52 58.03
J0228.4+0032
J0228.6−0042
J0228.1−0115
02 28 25.01
02 28 40.90
02 28 07.50
+00 32 12.08
−00 42 55.03
−01 15 43.10
J0230.4+0108
J0231.0−0049
J0233.4+0015
02 30 26.25
02 31 00.82
02 33 25.37
+01 08 49.10
−00 49 44.42
+00 15 48.75
J0233.5−0203
J0234.3−0139
J0234.9−0736
J0235.5−0705
J0235.5−0219
J0237.9−0145
J0239.2−0118
J0239.7−0234
J0242.6−0000
02 33 30.17
02 34 21.64
02 34 58.71
02 35 30.73
02 35 32.43
02 37 57.00
02 39 13.42
02 39 45.71
02 42 40.40
−02 03 22.19
−01 39 00.25
−07 36 17.98
−07 05 01.43
−02 19 31.19
−01 45 10.77
−01 18 15.01
−02 34 39.93
−00 00 45.21
J0242.7−0157
02 42 47.57
−01 57 46.54
Type
G
G
G
QSO
GClstr
QSO
GClstr
QSO
GClstr
QSO
GClstr
G
QSO
QSO
QSO
GClstr
GClstr
GClstr
GClstr
AbLS
QSO
GClstr
GClstr
GClstr
GClstr
G
G
QSO
QSO
QSO
G
QSO
QSO
G
GClstr
QSO
R.A.
NED object
02 01 43.11
02 01 47.03
02 13 47.00
02 15 42.02
02 16 17.30
02 16 40.65
02 17 32.80
02 18 34.38
02 18 39.40
02 20 02.31
02 20 34.30
02 20 54.25
02 23 01.55
02 24 22.39
02 25 08.09
02 27 35.40
02 27 35.50
02 28 26.50
02 28 42.90
02 28 07.79
02 28 07.80
02 30 27.40
02 31 11.20
02 33 28.10
02 33 27.90
02 33 30.34
02 34 21.83
02 34 58.44
02 35 30.71
02 35 32.51
02 37 57.09
02 39 13.68
02 39 45.47
02 42 40.71
02 42 32.10
02 42 47.65
Decl.
NED object
−02 11 47.6
−02 11 55.7
−02 56 37.5
−02 22 56.8
+00 11 37.0
−04 44 04.7
−00 15 52.0
−09 23 38.5
−00 12 20.0
−01 43 52.7
+00 27 56.0
−01 56 51.8
−08 26 09.9
−07 52 58.7
−00 35 31.4
−00 54 05.0
−00 51 32.0
+00 32 20.0
−00 43 20.0
−01 15 40.5
−01 15 40.6
+01 09 04.0
−00 49 21.0
+00 17 22.0
+00 18 37.0
−02 03 22.4
−01 39 00.6
−07 36 19.8
−07 05 04.6
−02 19 32.0
−01 45 11.4
−01 18 16.4
−02 34 40.9
−00 00 47.8
+00 00 14.0
−01 57 49.6
dist from
object (′′ )
4.33
4.50
3.96
3.59
158.12
3.77
116.95
2.13
167.20
5.80
27.24
2.45
4.43
0.33
5.51
68.03
86.66
23.71
39.03
5.06
5.14
22.79
157.45
101.84
172.47
2.55
2.87
4.44
3.18
1.44
1.48
4.14
3.72
5.32
137.86
3.28
Redshift
Reference
0.19590
0.19600 ± 0.00100
0.35680 ± 0.00020
1.17800
0.21998
0.87000
0.31075
0.47000
0.31075
0.47000
0.26537
0.17500
1.52070
2.44920
0.68700
0.34479
0.33344
0.50000
0.42421
1.99760
2.03700
0.40000
0.39017
0.35613
0.34479
0.79400
0.64500 ± 0.00200
2.17260
2.05980
1.32100
0.84000
1.79400
1.11600
(1137 ± 3)km/s
0.21998
0.61700
2
3
4
5
1
6
1
6
1
6
1
7
8
8
9
1
1
10
1
11
1
10
1
1
1
9
12
8
8
9
9
9
9
13
1
9
An asterisk (*) after the source-name indicates the source has been detected at both frequencies.
REFERENCES:(1) Goto et al. (2002), (2) Crawford et al. (1999), (3) Crawford et al. (1995), (4) Owen et al. (1995), (5) Drinkwater et al. (1997), (6) Becker et al. (2001), (7) Hewitt & Burbidge (1991),
(8) Schneider et al. (2003), (9) Dunlop et al. (1989), (10) Postman et al. (1996), (11) Junkkarinen et al. (1991), (12) Stanford et al. (2000), (13) Huchra et al. (1999)
Host galaxies and environment of active galactic nuclei
J0201.7−0211
J0201.7−0211
J0213.7−0256
J0215.6−0222*
J0216.2+0008
J0216.6−0444*
J0217.4−0015
J0218.5−0923
J0218.6−0015
J0220.0−0143
J0220.5+0027
J0220.9−0156*
J0223.0−0826
J0224.3−0752
J0225.1−0035
J0227.6−0052
R.A. Radio Source
(J2000)
02 01 43.10
02 01 47.18
02 13 46.93
02 15 41.85
02 16 16.51
02 16 40.90
02 17 25.01
02 18 34.50
02 18 39.78
02 20 01.98
02 20 32.50
02 20 54.11
02 23 01.84
02 24 22.41
02 25 07.73
02 27 36.20
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33
A
A
R 
Figure A1: The multiple component sources in the 325 MHz source list larger than 13′′ , sorted in decreasing
angular size order. Contours corresponds to levels of 3σ × (−1.4, −1, 1, 1.4, 2, 2.8, 4, 5.6, 8, 11, ...) and greyscale is
scaled from −3σ to the maximum value in the image. On the top-left corner of each images appears the name of the
corresponding source in the source list, as well as its mean coordinate. The local noise level is shown on the bottom
of each image.
34
Host galaxies and environment of active galactic nuclei
Figure A1: Continued.
References
35
Figure A1: Continued.
36
Host galaxies and environment of active galactic nuclei
Figure A1: Continued.
Figure A2: The multiple component sources in the 74 MHz source list larger than 60′′ , sorted in decreasing angular
size order. Contours corresponds to levels of 3σ × (−1.4, −1, 1, 1.4, 2, 2.8, 4, 5.6, 8, 11, ...) and greyscale is scaled
from −3σ to the maximum value in the image. On the top-left corner of each images appears the name of the
corresponding source in the source list, as well as its mean coordinate. The local noise level is shown on the bottom
of each image.
CHAPTER 3
GMRT observations of the XMM large scale
structure survey field
Cyril Tasse, H. J. A. Röttgering, P. N. Best, A. S. Cohen, M. Pierre, R. Wilman
Astronomy & Astrophysics 471, 1105, 2007
 e low-frequency radio survey of the XMM-Large Scale Structure
(XMM-LSS) field aims to study the connection between the extragalactic
radio source populations and their environment as traced by X-ray and optical
emission. In this paper we present new radio observations of the XMM-LSS
field carried out using the Giant Meterwave Radio Telescope at 240 and 610
MHz. These observations complement the observations presented by Cohen at
al. (2003) and Tasse et al. (2006) at 74 and 325 MHz with the Very Large Array. At 240 and 610 MHz, we reach noise levels of ∼ 2.5 and ∼ 0.3 mJy/beam,
leading to the detection of 466 and 769 sources over 18.0 and 12.7 degree2
with resolutions of 14.7′′ and 6.5′′ respectively. Combining these data with the
available source lists at 74, 325 (Tasse et al. 2006) and 1400 MHz (NVSS),
we build a multifrequency catalogue containing 1611 radio sources. We check
for consistency of the astrometry and flux density estimates. We fit a simple
synchrotron radiation model to the flux density measurements of the 318 radio sources being detected in at least 4 bands. While ∼ 26% of them show
signature of spectral ageing, ∼ 6% show self absorption.
T
38
Host galaxies and environment of active galactic nuclei
3.1
I
The XMM-Large Scale Structure survey (XMM-LSS) is a 10 degree2 X-ray survey designed to
map out the large scale structure of the Universe through the detection of the X-ray emission of
the diffuse intracluster medium (ICM). Spectroscopic follow-up of ∼ 70 galaxy cluster candidates
found in the first ∼ 5 degree2 is completed, leading to a galaxy cluster density of ∼ 12 deg−2 in
the redshift range z . 1 (Pierre et al (2006), Pacaud et al (2006), in preparation). Also some 1000
deg−2 X-ray active galactic nuclei have been detected.
The Canada France Hawaii Telescope Legacy Survey (CFHTLS1 ) has observed the XMMLSS field in 5 bands down to iAB ∼ 25, allowing for optical identification of X-ray sources and
determination of photometric redshifts. Also, the XMM-LSS field was imaged in 7 bands from 3
to 160 µm as part of the Spitzer Wide-area Infrared Extragalactic Survey (SWIRE, Lonsdale et al.
2003). For a general overview of the XMM-LSS and associated surveys see Pierre et al. (2004).
Cohen et al. (2003) and Tasse et al. (2006) have conducted a deep low-frequency survey of
the XMM-LSS field at 74 and 325 MHz with the Very Large Array (VLA), reaching flux density
limits of ∼ 162 and ∼ 4 mJy/beam (5σ), leading to the detection of ∼ 1500 radio sources over
∼ 130 and ∼ 15 degree2 with resolutions of 30′′ and 6.7′′ respectively. The main scientific issues
that such data can address include: (1) Where are various radio-loud active galactic nuclei (AGN)
located with respect to the distribution of mass on cosmological scales as traced by the optical and
X-Ray emission? (2) Can the radio loudness of optical and X-ray AGN be understood as due to
environmental effects? (3) How does the hot IGM influence the physical properties of the radio
sources such as linear size and radio power?
In order to increase the size of the low frequency radio sources sample (Tasse et al. 2006),
we have conduced new observations of the XMM-LSS field at 240 and 610 MHz using the Giant
Meterwave Radio Telescope (GMRT). As compared to the VLA, the GMRT has a collecting area a
factor of 3 bigger. Integration time of ∼ 20 hours at each frequency, lead to the detection of ∼ 200
additional radio sources over ∼ 15 degree2 , corresponding to an increase of the sample size of
∼ 25%.
In addition, for 41% of the radio sources detected at 325 MHz, our GMRT observations provide
two additional flux density points giving the opportunity to study the links between host galaxy,
environment, and observed radio spectra for large samples of radio sources. Specifically, within
an evolutionary framework, the compact steep spectrum (CSS) and gigahertz peaked spectrum
(GPS) radio sources showing signs of synchrotron self absorption at ν < 1 GHz, are thought to be
the progenitors of larger steep spectrum FRI and/or FRII (Fanaroff & Riley 1974) radio sources
(eg Snellen et al. 2003; O’Dea 1998). When no more high energy electrons are injected into the
synchrotron emitting lobes, spectral ageing feature appears in the radio spectra. If the link between
these different classes of radio sources is effectively evolutionary, they should be found in similar
environments, while the infrared and optical properties of the host galaxy should be evolving. The
large multiwavelength dataset available on the XMM-LSS field, combined with the various radio
surveys we have undertaken may allow us to address this issue in a consistent way.
In this paper we present the GMRT radio observations, and discuss their consistency. Using
our multifrequency dataset and a simple model of synchrotron emission we build subsamples of
sources showing self absorption and/or spectral ageing features. In subsequent papers, we will
1
for more information on the CFHT Legacy Survey, see http://www.cfht.hawaii.edu/Science/CFHLS/
GMRT observations of the XMM large scale structure survey field
39
Figure 3.1: The noise maps at 240 and 610 MHz. On both panels, contours where plotted at level of
σ × {1, 1.4, 2, 2.8, 4, 5.6, 8...} with σ = 1.0 mJy and σ = 0.2 mJy at 240 and 610 MHz respectively. On both
panels, the crosses show the location of each individual pointing center, the thick red line shows the four
325 MHz VLA pointings, and the blue circles show the location of the X-ray pointing. The primary beams
at 240 MHz are counter intuitively smaller than those of the VLA at 325 MHz because the GMRT antennas
are larger.
40
Host galaxies and environment of active galactic nuclei
Table 3.1: Observational parameters for the XMM-LSS radio followup with GMRT.
Central Frequency
Obs. date
Number of pointings
Int. time per pointing
Total obs. time (hrs)
Band Width
Nchannel
UV range (kλ)
Band Pass Cal.
Phase Cal.
240 MHz
30, 31 Jul.,
3 Aug. 2004
7
∼ 2.8 hrs
∼ 20
8 MHz
128
∼ 0.3 − 18
3C48
0116-208
610 MHz
30, 31 Jul.
1, 2 Aug. 2004
36
∼ 0.5 hrs
∼ 18
2 × 16 MHz
2 × 128
∼ 0.3 − 48
3C48
0116-208
address the main scientific issues using the infrared, optical and X-ray data available on the XMMLSS field.
This paper is organised as follow: in Section 3.2 we give details of the observations, and data
reduction. In Section 3.3 we briefly describe the source extraction methods, and we present the
error analysis in Section 3.4. The final source list is presented and analysed in Section 3.5. In
Section 3.6 we build a multifrequency radio sample and we fit a simple synchrotron radiation
model to the brightest sources to build the self absorbed and spectrally aged subsamples. We
conclude in Section 3.7.
3.2
O   
3.2.1 Radio Continuum at 610 MHz
The XMM-LSS field centered at α(J2000)= 2h 24m 00s , δ(J2000)= −4◦ 09′ 47′′ was observed at 610
MHz from the 30th of July to the 2nd of August 2004 for a total of ∼ 18 hours. We have used the
full available bandwidth of 32 MHz, split into two intermediate frequencies (IFs), each IF being
sampled into 128 channels. The first and second IF were covering the intervals 594 − 610 MHz and
610 − 626 MHz, respectively (See Tab. 3.1 for an overview). At 610 MHz the GMRT full width
at half maximum (FWHM i.e. primary beam diameter) is θFWHM ∼ 0.7◦ . In order to survey the
∼ 15 degree2 of the 325 MHz counterpart of the XMM-LSS field (Tasse et al. 2006), we spread the
pointing centers over an hexagonal grid consisting of 36 pointings centers separated by 0.6◦ (Fig.
3.1). The pointing grid covers 78% of the area mapped at 325 MHz. For each pointing, we have
used a total integration time of 30 minutes. Since the GMRT array consists of three arms, in the uvplane the visibilities corresponding to each individual baseline come back to the same uv-location
every 4 hours. In order to optimize the uv-plane coverage we split each 30 minutes observation of
each individual pointing into three 10 minutes scans, separated by about 1.3 hours. For the flux
and bandpass calibration we used 3C48 as flux density and bandpass calibrator, observed for 30
minutes at the beginning and the end of the run. We used 0116 − 208 as phase and amplitude
calibrator, observed for 8 minutes every 30 minutes.
GMRT observations of the XMM large scale structure survey field
41
For the data reduction work, we made use of the Astronomical Image Processing System
(AIPS). We reduced each IF independently. After a preliminary round of Radio Frequency Interference (RFI) removal using AIPS tasks SPFLG and UVFLG, the bandpass as well as the overall
flux density scale were calibrated using 3C48, and the amplitudes of the gains of each antenna were
calibrated using 0116 − 208. The expected resolution of ∼ 6.5′′ is similar to the 6.7′′ resolution
obtained at 325 MHz with VLA. In order to calibrate the phases variation with time, we build from
the Tasse et al. (2006) 325 MHz source list a catalog containing the positions and flux densities of
all individual components brighter than 10 mJy. Most of these 325 MHz sources have positional
uncertainties on the level of 1.5′′ . By Fourier transforming this model of the sky plane, the AIPS
task CALIB generates a corresponding model of the uv-plane that is used to calibrate the phase of
each antenna every 3 minutes. This approach provides the advantage of suppressing all possible
astrometry offsets between the different pointings.
In order to image the full primary beam, we have used a pseudo three dimensional Fourier
transform technique (Perley 1999), where each pointing is divided into smaller fields (facets). We
have used ∼ 20 facets per pointing each 13′ in size. Each facet was then imaged, and combined into
a single image using AIPS task FLATN. The fitted size of the dirty beam is ∼ 6.5′′ along the major
axis and ∼ 4.5′′ along the minor axis. In order to have a homogeneous beam across the different
pointings in the combined map, the restoring beam was set to be a 6.5′′ × 6.5′′ circular Gaussian
in each pointing, angular scale being 1.5′′ .pixel−1 . After few standard phase only self-calibration
steps, the synthesized images of the two IFs of each pointing have been averaged. Using a radial
cutoff limit of 0.35◦ (corresponding to the FWHM), the 2IFs×36 pointings have been combined
into a ∼ 13 degree2 single image following the procedure described by Condon et al. (1998). In
the final map the noise is as low as ∼ 0.2 mJy.beam−1 to as high as ∼ 1.5 mJy.beam−1 (Fig 3.1).
3.2.2 Radio Continuum at 240 MHz
The data at 240 MHz were obtained using the GMRT from the 30th of July to the 3rd of August 2004
for a total time of ∼ 20 hours. As opposite to the 610 MHz setting, at 240 MHz, the 8 MHz wide
bandwidth (128 channels) is centered at 240 MHz and consists of only one IF (See Tab. 3.1 for an
overview). The primary beam diameter being 1.8◦ , in order to cover the 325 MHz counterpart of
the XMM-LSS field, we made use of an hexagonal grid, consisting of 7 pointing centers, separated
by 1.56◦ , as shown in Fig. 3.1. The pointing grid covers ∼ 93% of the area mapped at 325 MHz.
As for the 610 MHz observations, we used 3C48 at the beginning and the end of each run as flux
density and bandpass calibrator, and 0116 − 208 as phase and amplitude calibrator. Also in order
to optimize the uv-plane coverage, each pointing was observed for 10 minutes every ∼ 1.4 hour,
while the secondary calibrator was observed every 0.5 hour for 8 minutes. Each pointing has be
observed ∼ 2.8 hours in total. Table 3.1 shows a summary of the observational parameters we have
been using.
We have synthesized the 240 MHz images as we did at 610 MHz. As for the 610 MHz data, we
calibrated the phases using a model derived from the source list at 325 MHz by Tasse et al. (2006)
(See Sec. 3.2.1). The restoring beam has been set to be 14.7′′ circular Gaussian in each map, with
a pixel size of 3.5′′ .pixel−1 . After a few phase-only self calibration steps we have combined the 7
pointings into a single map using a radial cutoff of 0.9◦ . The resulting noise varies from as low as
1.2 mJy/beam and as high as 10 mJy/beam close to bright sources.
42
Host galaxies and environment of active galactic nuclei
Figure 3.2: Area as a function of limiting flux density at 5σ. The full line corresponds to the 610 MHz
survey whereas the dashed one corresponds to the
240 MHz survey.
Figure 3.3: Flux density limits (5σ) reached at
240 and 610 MHz as compared to the flux density
reached at 74 and 325 MHz with VLA. Dashed
lines show the flux density of a source having a
spectral index of α = −0.8.
At both 240 and 610 MHz, Fig. 3.2 shows the complete area as a function of the sensitivity
(5σ), and Fig. 3.3 shows the limiting flux density in the different available frequency bands.
3.3
S 
For a detailed discussion on the method we have used to extract the sources from the synthesized
maps, see Tasse et al. (2006). We briefly describe that process below.
With the local noise being highly variable across the field (see Fig. 3.1 & 3.2), special attention was paid to the source finding method. First, a local noise map is generated using AIPS
task RMSD, which derives a local noise value within a box. Given the noise distribution is nonGaussian, the size of the box is critical. We set visually the size of this box to the scale over
which the local noise varies significantly, which leads to values of 3′ and 1′ at 240 and 610 MHz
respectively.
Then we normalize the combined map by the local noise map, and we extract the sources in
that noise-normalized map using AIPS’s Gaussian fitting algorithm Search And Destroy (SAD),
giving an input cut on both the peak and the integrated flux densities of 5 (corresponding to 5σlocal
in the original map). We then correct for the peak flux bias introduced by the noise normalization,
and calculate the errors on each Gaussian fitting parameter following Condon (1997).
Following Tasse et al. (2006), we build a catalog by associating the components of the source
list closer than 60′′ . The number of Gaussian fitting components being 571 and 1024 at 240 and
610 MHz respectively, assuming a Poisson statistics, the probability of two components to be
associated by chance is less than 1% in both cases. In the catalogs appearing in Tab. B16 and
B26 , we have tagged the brightest component of multiple sources with an “M”, and the other
components with a “C”. Non associated components have been classified as Single sources (tag
“S”).
GMRT observations of the XMM large scale structure survey field
3.4
43
D      
3.4.1 Astrometry
The XMM-LSS field being fully covered at 1.4 GHz by the NVSS (Condon et al. 1998) we have
looked for 1.4 GHz NVSS sources in a 45′′ radius around each individual radio source2 . For
the sources identified in the NVSS, this provides 1.4 GHz astrometry, as well as a spectral index
estimate. To assess the positional accuracy, in our catalog we have selected a subsample of single
(“S”) sources with their NVSS counterpart being point-like and having positional uncertainties
NVS S
σα,δ
< 0.7′′ on both right ascension and declination, which has the effect of selecting the radio
sources that have the brightest 1.4 GHz counterparts. On this subsample containing 33 sources,
we measure overall astrometrical offsets of ∆(α610 ) = 1.15′′ and ∆(δ610 ) = −1.50′′ at 610 MHz
on right ascension and declination respectively, and ∆(α240 ) = 2.84′′ and ∆(δ240 ) = −1.85′′ at 240
MHz. These offsets are on the order of the pixel size at both 240 and 610 MHz. We have corrected
for these astrometry shifts in the final catalog. In order to be conservative, given the fitted Gaussian
major and minor axis and their associated error bars, we have classified as unresolved the sources
that are indistinguishable from the restoring beam size at the 2σ level 3 .
From the same high signal-to-noise subsample, we measure the standard deviation of the position differences between fitted components and their NVSS counterpart to be σα = 0.70′′ and
σδ = 1.78′′ at 610 MHz and σα = 1.31′′ and σδ = 2.37′′ at 240 MHz. These errors contain a
contribution from the positional uncertainties in our catalog and a contribution from the NVSS
positional uncertainties. In both these components, there is a contribution from the Gaussian fitting errors, which depends on the signal-to-noise ratio of the detection, and a contribution from
the individual antenna calibration errors, which does not depend on the local rms value. In order
to derive an estimate of the positional calibration errors in our GMRT data σGMRT cal on both right
ascension, and declination we write:
2
2
σ2 = σ2NVS S cal + σ2NVS S f it + σGMRT
cal + σGMRT f it
(3.1)
For the NVSS the positional errors due to the antenna calibration has been measured to be
σα,NVS S cal = 0.45′′ and σδ,NVS S cal = 0.56′′ (Condon et al. 1998). In the high signal-to-noise ratio
subsample defined above, corresponding to the brightest sources in our source list, we measure the
associated Gaussian fitting errors on position σGMRT, f it . 0.1′′ at both 240 and 610 MHz making
the Gaussian fitting error contribution to be negligible. We derive an upper limit to the residual
systematic errors in the 610 MHz astrometry of σα,GMRT cal . (σ2α − σ2α,NVS S cal )1/2 = 0.53′′ and
σδ,GMRT cal . 1.69′′ . At 240 MHz we find σα,calib . 1.23′′ and σδ,calib . 2.31′′ . We quadratically
add these values to the error obtained from the Gaussian fits.
2
Note that we can not use the deeper and higher resolution 1.4 GHz FIRST survey data (Becker et al. 1995) because
it only partly covers the XMM-LSS field.
3
If θN is the restoring beam diameter, θ M the major axis diameter, and σ(θ M ) its associated error bar, then the fitted
component is classified as unresolved if (θN − θ M )2 /σ2 (θ M ) < 2. Same for the minor axis.
44
Host galaxies and environment of active galactic nuclei
3.4.2 Flux densities
Various factors have to be taken into account in order to derive source lists having reliable flux
densities estimates.
Contrarily to the VLA, at the GMRT the injection of calibrated noise at the front-end of each
antenna to measure the system temperature has not been implemented. To calibrate the gains of
each individual antenna, the system temperature T sys is assumed to be sum of various components:
T sys = T r + T a + T sky
(3.2)
T sky = 3 + 20(408/ν)2.75
(3.3)
where T r is the receiver temperature, T a is the antenna temperature, T sky is the sky temperature
and ν is the observed frequency. At each individual frequency, T r , and T a are assumed to be
independent of the pointed position4 . However, because of the Galactic diffuse radio emission, the
sky temperature can vary greatly from one position to another, with T sky being higher in the galactic
plane. The gain by system temperature in units of K.Jy−1 used to calibrate the flux densities of the
′
data, is based on the measurement T sys at the position of the flux density calibrator. If T sys
is
′
the system temperature at the position of the field, then an overall multiplicative bias T sys /T sys is
introduced on the flux density estimates at the location of the field. The flux density calibrator
3C48 that we have used is situated at b = 133.96◦ and l = −28.71◦ (in Galactic coordinates),
whereas the field is centered around l ∼ 173◦ and l ∼ −57◦ . Using the Haslam et al. (1982) all-sky
radio maps at 408 MHz, that have a resolution of 0.85◦ , we estimate using Eq. 3.3 that the flux
densities need to be corrected by ∼ −2% at 610 MHz and ∼ −18% at 240 MHz. We have corrected
for these overall offsets in the final source list.
In the case of resolved sources, calculating the integrated flux density by summing the individual Gaussian fitted flux density components produces a bias towards lower fluxes as compared to
the real integrated flux density. Instead, following Tasse et al. (2006) we estimate the flux densities
at both 240 and 610 MHz by summing the pixel values in 60′′ diameter apertures centered at the
position of the individual Gaussian components. If S pix is the flux density thereby computed, then
S pix = Σ × 4ln2/(π.FWHM), and σ pix = N pix σloc , where Σ is the sum of the pixels inside the
aperture, N pix is the number of pixel over which the summation is done, σloc is the local noise at
the location of the source and FWHM is the Full Width at Half Maximum of the restoring beam
in pixels. Because this flux density estimator does not use any a priori knowledge on the restoring
beam shape, the error bars associated to the integrated flux density estimate are much bigger than
those associated with the Gaussian fitting method. Given the values and error bars estimates for
the two methods, we consider the pixel-based estimate of the integrated flux density only when the
difference is significant at the 2σ level5 .
Finally, because we have used the secondary calibrator 0116 − 208 to calibrate the gains of
individual antennas, we quadratically add a standard 10% overall flux density scale error to the
noise-based Gaussian fitting uncertainties. This value takes into account any systematic error in
the GMRT measurement, like small elevation dependant errors (for a more detailed discussion see
Mohan et al. 2001; Chandra et al. 2004).
4
5
see http://www.gmrt.ncra.tifr.res.in for more information
on the estimation of each of these temperature
q
With σ being computed as σ = (S pix − S gauss )/ σ2pix + σ2gauss
GMRT observations of the XMM large scale structure survey field
45
Figure 3.4: The spectral index distribution at 240 and 610 MHz, derived from flux density comparison with
NVSS. The resolution differences at both frequencies (14.7′′ and 6.5′′ respectively as compared to 45′′ for
NVSS) leads to an overestimate of the spectral indices for extended and resolved sources, as we may miss
a significant fraction of the total flux density. Simple crosses stand for the resolved sources, whereas thick
stars are the point-like sources, where no extended emission is expected to bias the spectral index estimate
toward higher values. On both panels, the vertical dash-dotted line indicates the 95% completeness level.
On both graphs, horizontal dotted lines indicate the median spectral index value derived from subsamples
int > 63 mJy and S int > 30 mJy, corresponding to a limiting integrated flux
having integrated flux density S 240
610
int
density at 325 MHz of S 325 > 50 mJy. Diagonal dashed lines indicate the bias introduced by the limiting
flux density and the 95% completeness limit (thick dashed) of NVSS respectively (Condon et al. 1998). We
have given the sources that are not detected in the NVSS, a spectral index upper limit. These points are not
plotted, but lie along the 95% completeness limit of NVSS.
46
Host galaxies and environment of active galactic nuclei
1400
Figure 3.5: The distribution of the spectral index α1400
240 and α610 derived from flux comparison between
our surveys flux density estimates at 240 and 610 MHz, and the NVSS at 1.4 GHz. The full line stands
of the radio sources in the WENSS radio survey (De Breuck et al.
for the spectral index distribution α1400
325
int > 63 mJy and
2000). To match the 5σ flux density limit of WENSS, we have applied a flux density cut S 240
int > 30 mJy at 240 MHz and 610 MHz respectively.
S 610
3.5
R
The main characteristics of the 240 and 610 MHz surveys are given in Table 3.2. The associated
source lists are given in Tab. B16 and B26 respectively. Contour plots are shown in Fig. A1 at 240
MHz and in Fig. A2 at 610 MHz for the brightest extended sources.
Table 3.2: Main results in both 240 and 610 MHz bands.
Frequency
240 MHz 610 MHz
Resolution
14.7′′
6.5′′
2
Area (degree )
18.0
12.7
Rms (mJy/Beam)
∼ 2.5
∼ 0.3
Single sources “S”
388
592
Single unresolved
96%
75%
Multiple sources “M”
79
175
Of the sources detected at 325 MHz (Tasse et al. 2006), ∼ 50% and ∼ 67% are detected at 240
and 610 MHz respectively. The fraction of the 325 MHz area surveyed at 240 and 610 MHz, is
∼ 93% and ∼ 78% respectively. Of the sources detected at 325 MHz and covered by the 240 and
610 MHz surveys, ∼ 55% and ∼ 80% are detected at 240 and 610 MHz respectively. Although, the
6
Available in the electronic version of this paper only.
GMRT observations of the XMM large scale structure survey field
47
Figure 3.6: Both panel show the Euclidean normalized differential source count at 240 MHz (top panel),
and 610 MHz (bottom panel). The curves shows the extrapolated Wieringa (1991) 325 MHz differential
source count, assuming different spectral index values. The vertical dotted line show the 95% completeness
level.
610 MHz survey is on average deeper by a factor of ∼ 1.5 than the 325 MHz survey (Fig. 3.3), the
fact that we miss ∼ 20% of the 325 MHz sources at 610 MHz is explainable because the 325 MHz
sources that are missed at 610 MHz are the faintest ones. In order to check for the consistency of
the amount of the 325 MHz sources detected at 240 and 610 MHz, we first generate a 325 MHz
catalog in which each source is given a random flux density value according to their measured
flux density and associated error bars. Then each of these source is given a random spectral index
48
Host galaxies and environment of active galactic nuclei
following a normal law with mean value hαi = −0.8 and dispersion σ(α) = 0.24 (De Breuck et al.
2000). We extrapolate the flux density of each of these 325 MHz radio source to 240 and 610 MHz,
and add up the local noise corresponding to their individual position at 240 and 610 MHz. From
these simulated 240 and 610 MHz catalog, we extract the respective source lists using a 5σlocal cut
as we did on the actual maps (Sec. 3.3). We observe that such a 325 MHz population would lead
to the detection of ∼ 55% and ∼ 76% of the of 325 MHz sources at 240 and 610 MHz respectively,
in agreement with our observations.
Fig. 3.4 shows the spectral index distribution derived from the flux density comparison with
NVSS. The sources without a 1.4 GHz counterpart have been given an upper limit on their spectral
index, using the NVSS 95% completeness limit at 5σ. At both frequencies the resolutions being
14.7′′ and 6.5′′ are smaller than the 45′′ resolution of NVSS (Condon et al. 1998). Such differences
make the estimation of spectral index to be less reliable for extended radio emission.
In order to compare the spectral index distribution of the 240 and 610 MHz sources to the
spectral index distribution of the WENSS 325 MHz radio sources (Rengelink et al. 1997), we
assume a mean spectral index of α1400
325 = −0.8 (De Breuck et al. 2000). We extrapolate the limiting
lim
integrated flux density of WENSS being S 325
= 50 mJy to 240 and 610 MHz, which makes
lim
lim
S 240 = 63 mJy and S 610 = 30 mJy. We derive median spectral index values of α1400
240 = −0.62 and
1400
α610 = −0.76. Fig. 3.5 shows the spectral index distribution of the subsamples defined above
(S 240 = 65 mJy and S 610 > 30 mJy) to the WENSS spectral index distribution at 325 MHz (De
Breuck et al. 2000). At 240 MHz, the spectral index distribution has the same width but is shifted
towards shallower spectral indices. This is likely to be due to the combination of the resolution
differences (14.7′′ at 240 MHz, against ∼ 55′′ for WENSS), and the natural tendency of radio
sources to be synchrotron self absorbed at low frequency. At 610 MHz, we find close agreement
in the distribution centroid, but we note an excess of flat spectrum sources, whose presence might
be due to the resolution difference being even bigger, and the shallower effective depth of WENSS
to flat spectrum sources.
Below the corresponding flux density limit of WENSS, we detect a few α > 0 inverted spectral
index for unresolved sources at rather small flux densities (Fig. 3.4). This is expected when
synchrotron self absorption occurs in compact radio sources such as compact steep spectrum (CSS)
and gigahertz peaked spectrum sources (GPS).
Fig. 3.6 shows the Euclidean normalized differential source counts at 240 and 610 MHz as well
as the WENSS differential source counts extrapolated from from 325 MHz (Wieringa 1991). In
principle, in order to derive reliable estimates of the differential counts over the whole flux density
range, biases and incompleteness correction have to be taken into account: (i) the incompleteness
introduced by the variation of the complete area with flux density (Fig. 3.3) (ii) the incompleteness
due to the efficiency of the source finding algorithm SAD that decreases with the signal-to-noise
ratio and (iii) the bias introduced by SAD flux density overestimation at low flux densities (see
Seymour et al. 2005, for a detailed discussion on these biases). All these biases occur at low
flux densities. We do not correct for any of these but plot the 95% completeness level estimated
from the noise maps. Above this flux density, the differential counts are reliable. At 240 MHz,
the differential counts agree with the WENSS differential counts until a flux density level of ∼
100 mJy. At lower fluxes, the factor of ∼ 2 difference with the expected values could be due
to cosmic variance: at 325 MHz in the same field the differential source count shows the same
offset with the Wieringa (1991) differential source count at & 20 mJy (Tasse et al. 2006). At 610
GMRT observations of the XMM large scale structure survey field
49
MHz, the Euclidean normalized differential source counts follow the Wieringa (1991) extrapolated
differential source counts, consistently with a mean spectral index value of α ∼ −1.
3.6
R  
3.6.1 The multi frequency radio sample
For each individual source, when comparing the flux density estimates obtained at the different
frequencies, several issues have to be considered.
First, the sky plane has been imaged using different instruments, at different frequencies. In
each frequency band, the uv plane coverage can be significantly different from one observation to
another. This can lead to a variation in the flux estimate, shorter baselines being sensitive to larger
angular scale radio emission. After the RFI flagging step, the shortest baselines in the uv plane are
∼ 0.3 kλ at 240 and 610 MHz, ∼ 0.5 kλ at 325 MHz, 0.2 kλ at 74 MHz, and ∼ 0.05 kλ at 1400
MHz, corresponding to angular scales of ∼ 11.5′ , 6.9′ , 20′ and ∼ 1.2◦ respectively. The largest
extended radio emission detected in the XMM-LSS field is the ∼ 1′ radio halo candidate described
in Tasse et al. (2006) and is smaller than the maximum angular scale detectable at 325 MHz. This
effect is therefore assumed to have a negligible impact on the integrated flux density estimates.
Secondly, through the various frequency bands, the size of the synthesized beam varies from
′′
6.5 at 610 MHz to 45′′ at 1.4 GHz. As described in Sec. 3.4.2, since the Gaussian fitting method
may miss a significant part of the extended flux in some cases, we also extract the integrated
flux density based on pixel values within a 60′′ diameter aperture (see Sec. 3.4.2). This second
estimation corrects for that effect.
Finally, the overall flux density calibration is reliable on the level of ∼ 5% for the VLA and
∼ 10% for the GMRT. These systematics have been taken into account within the error bar calculation. We discuss the effect of these systematics on the radio spectra fits in Sec. 3.6.4.
In order to study the shape of the low frequency radio spectra, we build a 5-frequency band
catalog on the basis of the source lists at 74, 240, 325, 610, and 1400 MHz (Tasse et al. 2006;
Condon et al. 1998), by associating their Gaussian fitted components that are closer than 60′′ .
Considering the sum of all the number density of these various radio surveys, this radius makes
the probability of two components to be associated by mistake to be less than 2%.
3.6.2 Comparison with VLA data
For testing the 610 MHz source list flux densities, we select the ones being detected at 325 and
1400 MHz. In order to be not affected by the angular extend of the radio sources in any way, we
select only the sources being unresolved in all frequency bands. We define the spectral curvature as
1400
α610
325 − α610 . The spectral curvature expresses whether the synchrotron slope shows any difference
on the two sides of the 610 MHz point. In Fig. 3.7 we plot the spectral curvature as a function
1400
of the flux density at 610 MHz. The mean spectral curvature value is hα610
325 − α610 i = −0.15,
becomes ∼ 0 if we correct the overall flux density scale on the level of 6%, in agreement with the
∼ 10% error on the flux scale used to calculate the error bars. At low fluxes though, we see the
mean spectral curvature to reach negative values of ∼ −0.8, which is due to a selection effect: we
50
Host galaxies and environment of active galactic nuclei
Figure 3.7: These plots show the consistency of the flux density measurements of the VLA and the GMRT
instruments. Top panel: for the sources detected at 240, 325, and 1400 MHz, the spectral curvature α325
240 −
1400
α240 is plotted as a function of the flux density at 240 MHz. The overall shift is explainable in term of
flux density calibration being accurate on the level of ∼ 10% on true spectral curvature. The bottom panel
similarly shows the spectral curvature α610
− α1400
as a function of the 610 MHz flux density. Because each
325
610
survey is flux density limited, a range of spectral curvature is not reachable at a given 240 or 610 MHz flux
density level (dashed area). This explains the apparent decreasing of the spectral curvature at low 610 MHz
flux densities. Some points stand with the dashed area because the flux density limit is not homogeneous in
the radio maps, both at 325 and 1400 MHz. Vertical dotted lines stand for the 95% completeness limit.
GMRT observations of the XMM large scale structure survey field
51
have selected the point-like 610 MHz sources being detected at both 325 and 1400. Therefore at a
given 610 MHz flux density, and given the 325 and 1400 MHz flux density limit (Fig. 3.3), a range
of spectral curvature values is not observable. If we compute the observable spectral curvature
range as a function of the 610 MHz flux density, we clearly see how biased the spectral curvature
measurement is for faint 610 MHz sources.
We carry out the same analysis at 240 MHz (Fig. 3.7, left panel). The mean spectral curvature
1400
value is hα325
240 − α240 i = −0.11 corresponding to the 240 MHz flux density being overestimated.
Nevertheless, a correction of the overall flux density scale on the level of ∼ 4% brings the averaged
spectral curvature value to ∼ 0, in agreement with the 10% error on the gain calibration. The
agreement between flux density estimates at 240, 325, and 1400 MHz seems to hold until the 5σ
level although we note the presence of sources showing very low spectral curvature values. The
bias introduced by the different flux density limits at 325 and 1400 MHz do not seem to have much
influence. That is because assuming a spectral index of −0.8 the 240 MHz flux density limit is
higher than the ones at 325 and 1400 MHz by a factor af ∼ 2 (see Fig. 3.3).
3.6.3 Spectral fits
Theoretically, the classical power law spectrum of synchrotron emission is understood as a zero
aged relativistic electron population, having a power-law energy distribution, and being optically
thin to its own radiation (Kardashev 1962). Deviation from the power law occurs with (1) a low
frequency turnover due to synchrotron self absorption at low frequency and (2) a steepening at high
frequencies due to particle energy losses. When the major fraction of the flux density is emitted by
one, single electron population the radio spectra are well approximated by a simple synchrotron
radiation model, including self absorption and spectral ageing features. The presence of different
synchrotron emitting electron population will make the integrated radio spectra significantly deviate from the simple synchrotron spectrum. For example FRI radio sources (Fanaroff & Riley 1974)
show a compact flat spectrum core and steep spectrum extended lobes.
For a crude modeling of the synchrotron emission, we approximate the integrated radio spectra
to be a emitted by a single electron population, following the continuous injection model described
above. It is well known that under these assumptions the synchrotron spectrum S (ν) has a standard
shape (Kardashev 1962), modified by low frequency self-absorption (Pacholczyk 1970) as follows:
S (ν) = S 0 (ν/ν1 )5/2−α (1 − e−(ν/ν1 )
S aged =
(
να
να−1/2
α−5/2)
: ν < νc
: ν > νc
)S aged
(3.4)
(3.5)
where S 0 is a normalization factor, ν1 is the self absorption frequency break, α is the zero aged
spectral index in the transparent frequency range and νc is the break frequency above which the
power-law breaks to be spectrally aged. Our free parameter space for the spectral fit is composed
of {S 0 , α, ν1 , νc }. The initial condition of the fitting routine are set on a 15 × 21 × 21 × 21 parameter
grid. The ν1 and νc initial condition ranges are ∼ 50-250 MHz and ∼ 300-2000 MHz respectively
whereas the initial condition ranges for S 0 and α depend on individual flux density levels and error
bars.
52
Host galaxies and environment of active galactic nuclei
Figure 3.8: Examples of two radio sources, showing spectral ageing (top) and self absorption signatures
(bottom). Left panels show the flux density measurement at 74, 240, 325, 610 and 1400 MHz together with
their associated error bars. The full line shows the best fit spectrum described by equations 3.4 and 3.5. The
right panels show the dependence of the χ2 on the parameters ν1 and νc , that are used for the radio spectral
type classification. The parameters corresponding to the best fit is marked with a thick open circle. The
different contour lines show the χ2min + 1, χ2min + 2.71 and χ2min + 6.63 levels corresponding to the 68%, 90%
and 99% confidence intervals respectively (Avni 1976). The vertical and horizontal dashed lines correspond
to the lowest and highest available frequency respectively. In the final fitted parameters table, we consider
the best fit parameter together with the 68% confidence interval covering also the degenerated solution. In
these examples, based on the 68% confidence interval for ν1 and νc , we can see the model fits the flux density
points only if spectral ageing and self-absorption are present in the first and second case respectively.
3.6.4 Subsample definition
Because we want to build samples of sources showing clear signatures of spectral ageing and/or
self-absorption, we select in the multi radio frequency catalog described in Sec. 3.6.1 the objects
having been detected at least at 240, 325, 610, and 1400 MHz, which makes the total number of
fitted sources to be 318 (38% of the sources detected at 325 MHz). When the object is not detected
GMRT observations of the XMM large scale structure survey field
53
at 74 MHz, we have used the flux limit at 74 MHz to constrain the fitting parameters.
In order to reject the objects that could not be well fitted we choose a reduced7 χ2 cut above
which the model described with Eq. 3.5 is assumed to be irrelevant. Fig. 3.9 shows that if we
select the spectral fits with an associated χ2reduced < 4.3 then the observed χ2reduced distribution
follows the theoretical distribution8 , meaning we can consider the model to be relevant for these
objects. We visually inspected the radio images of the objects having an associated χ2reduced > 4.3
(∼ 25% of the selected sample). They are found to be mainly resolved, having a few to many radio
emitting components. For these objects, the assumptions of a single synchrotron emitting electron
population (Sec. 3.6.3) is wrong, and the simple model we have used is most probably irrelevant.
In order to be conservative, we list the fitted parameters for all objects in Tab. B36 (including the
χ2reduced > 4.3 objects).
The minimum χ2 is recorded in the 4-parameter space, and the errors on the fitted parameters
are derived from the 68% confidence interval calculated at χ2min + 1 (Avni 1976). In the case of
degenerated solutions, instead of considering the list of all the χ2 minima with their respective
associated error bars, only the best fit parameters are considered, and the error bar associated with
them is the interval including all the multiple χ2 minima (See Fig. 3.8). This method allows us
to define a robust sample of sources having detected self absorption of spectral ageing feature. A
source is considered to be showing self absorption if and only if the 68% confidence interval of
ν1 falls within the frequency range [νlow ,1.4 GHz]9 probed by our survey, meaning good solutions
cannot be found without observed self absorption. We proceed the same way for the νc free parameter. This allows us to define robust subsamples of radio spectra showing self absorption, or
spectral ageing or both.
The best fit parameters with their associated 68% confidence interval as well as their classification appear in table B36 .
On the subsample of fitted spectra, ∼ 68% are found to show no significant signs of spectral
ageing or self absorption, ∼ 6% show self absorption only, ∼ 26% show spectral ageing only,
and none show both. It has been suggested in the past that compact steep spectrum (CSS) and
gigahertz peaked spectrum (GPS) may correspond to the early stages of evolution of classical
FRI and FRII radio sources, consistent with the observed subarcsecond angular size of CSS and
GPS radio sources (Fanti et al. 1995; O’Dea & Baum 1997; Snellen et al. 2000). Conversely,
the radio sources showing spectral ageing in their radio spectra are the ones in which no high
energy electron is injected into the radio emitting lobes. These radio sources should correspond
to the latest evolutionary stage of radio sources. In order to test the consistency of the three radio
spectral classes 10 , we compare their respective angular size distribution. As the resolution at
325 and 610 MHz is higher, we estimate the angular size of the objects in the sample based on
the Gaussian fitting components positions and their elongation deconvolved from the beam size
at these two frequencies. Fig. 3.10 shows the cumulative distribution of the angular sizes for
the three subsample. ∼ 60% of the steep spectrum sources are unresolved, compared to ∼ 40%
7
To calculate the reduced χ2 we have used the formula χ2reduced = χ2min /(n − p) where n if the number of data points
and p is the number of parameter in the model. The value of p depends on whether the parameters νc and ν1 of Eq.
3.5 have to be used to fit the radio spectra. For example if we use a single power law then p = 2
8
The reduced χ2 distribution theoretically follows a normal distribution with one degree of freedom.
9
Because we have the objects detected in at least 4 bands, the lower available frequency νlow value depends on
whether the object is detected at 74 MHz. In all cases, if this is not the case then νlow = 240 MHz.
10
Steep spectrum, Self absorbed and Spectrally aged
54
Host galaxies and environment of active galactic nuclei
Figure 3.9: The cumulative distribution of the reduced χ2 . Choosing a χ2red cut of 4.3, we see that ∼ 75%
of the population behaves like the ajusted theoretical χ2red distribution (black line).
and ∼ 80% for the sources showing signatures of spectral ageing and self-absorption respectively.
Consistently, the distribution of spectrally aged sources is clearly biased towards sources having
larger angular sizes than the single power law sources by a factor of ∼ 1.5 − 2. In the subsample
of sources showing signs of self absorption, we note the presence of three sources having large
angular diameters & 10′′ (J0217.0-0308, J0218.3-0403, J0219.7-0448)11 .
In principle we should be allowed to compare a parameter distribution only for a homogeneously selected sample. Nevertheless, we have built subsamples of radio sources based on their
radio spectral shape, selecting the objects that are detected at 240, 325, 610, and 1400 MHz at least,
the various surveys having different flux density limits (see Fig. 3.3). For example let us consider
a source at a given redshift and 325 MHz luminosity: in the observer frame, a self absorbed source
is more likely not to appear in the subsamples because its 240 MHz flux density may be below the
flux density limit of the associated survey. A similar effect acts on the spectrally aged subsample.
These preliminary results on the angular size distribution in the various radio source classes have
to be taken with caution because selection effect may play an important role.
3.7
C  F W
We have imaged the low frequency 325 MHz counterpart (Tasse et al. 2006) of the XMM-LSS
field at 240 and 610 MHz with the GMRT, leading to the detection of 467 and 667 radio sources
11
Detailed inspection of the radio images at 240, 325 and 610 MHz, show that the extended source J0217.0-0308
has its 240 MHz position in between two facets, likely making the flux to be badly estimated and the source to be
classified as self absorbed. J0218.3-0403 and J0219.7-0448 have an FRI-like morphology, suggesting the flux density
of the central component might dominates the overall flux density emission. Self absorption might therefore happen
within the central core of these sources although they are measured to be extended.
GMRT observations of the XMM large scale structure survey field
55
Figure 3.10: The angular diameter of the radio sources classified as spectrally aged or self absorbed. Spectrally aged sources appear larger on average than single power law sources and self-absorbed sources.
respectively. The GMRT survey covers 18.0 degree2 at 240 MHz and 12.7 degree2 at 610 MHz,
with average flux density limits of 12.5 and 1.5 mJy/Beam (5σ) respectively. We have corrected
for the various systematic errors introduced by the instrument and the data reduction procedure on
both astrometry and flux density scale. Comparison between the catalogs at 240 and 610 MHz with
other datasets at 325, and 1400 MHz show good agreement, although the spectral index distribution
suggests a spectral index flattening below 240 MHz.
We have fitted a simple continuous injection synchrotron emission model to the flux density
measured at 74 and 325 MHz by Tasse et al. (2006), 240 and 610 MHz presented in this paper, and
1.4 GHz (NVSS, Condon et al. 1998). On the basis of fitted parameters and error bars estimates, we
define a sample of radio sources showing signatures of self absorption or spectral ageing feature in
their radio spectra. Consistent with other studies suggesting a link between radio spectra shape and
radio source evolution, we found that spectrally aged sources have larger angular sizes, whereas
sources showing self-absorption are smaller.
In the near future, on the basis of the available X-ray and optical data we will study the effect
of environment on the fundamental properties of the various classes of radio sources.
A
We thank the staff of the GMRT that made these observations possible. GMRT is run by the National Center
for Radio Astrophysics of the Tata Institute of Fundamental Research. We thank Ishwara Chandra, Walter
Jaffe, Dave Green, Niruj Mohan Ramanujam, Amitesch Omar, Ignas Snellen for useful discussion on the
data reduction and analysis.
56
Host galaxies and environment of active galactic nuclei
R
Avni, Y. 1976, ApJ, 210, 642
Becker, R. H., White, R. L., & Helfand, D. J. 1995, ApJ, 450, 559
Chandra, P., Ray, A., & Bhatnagar, S. 2004, ApJ, 612, 974
Cohen, A. S., Röttgering, H. J. A., Kassim, N. E., et al. 2003, ApJ, 591, 640
Condon, J. J. 1997, PASP, 109, 166
Condon, J. J., Cotton, W. D., Greisen, E. W., et al. 1998, AJ, 115, 1693
De Breuck, C., van Breugel, W., Röttgering, H. J. A., & Miley, G. 2000, A&AS, 143, 303
Fanaroff, B. L. & Riley, J. M. 1974, MNRAS, 167, 31P
Fanti, C., Fanti, R., Dallacasa, D., et al. 1995, A&A, 302, 317
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O’Dea, C. P. 1998, PASP, 110, 493
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Pacholczyk, A. G. 1970, Radio astrophysics. Nonthermal processes in galactic and extragalactic sources
(Series of Books in Astronomy and Astrophysics, San Francisco: Freeman, 1970)
Perley, R. A. 1999, in ASP Conf. Ser. 180: Synthesis Imaging in Radio Astronomy II, 383–+
Pierre, M., Valtchanov, I., Altieri, B., et al. 2004, Journal of Cosmology and Astro-Particle Physics, 9, 11
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Seymour, N., McHardy, I., Gunn, K., & Moss, D. 2005, in The Dusty and Molecular Universe: A Prelude
to Herschel and ALMA, ed. A. Wilson, 323–324
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Wieringa, M. H. 1991, Ph.D. Thesis
References
57
A
A
R 
Figure A1: At 240 MHz, the 20 largest sources. Sorting order is increasing right assenssion. Contours corresponds
to levels of 3σ × (−1.4, −1, 1, 1.4, 2, 2.8, 4, 5.6, 8, 11, ...) and greyscale is scaled from −3σ to the maximum value in
the image. On the bottom-left corner of each image appears the name of the corresponding source in the source list,
its flux weighted coordinate as well as the local noise level. The crosses are marking the Gaussian fitting components.
58
Host galaxies and environment of active galactic nuclei
Figure A1: Continued.
References
59
Figure A2: At 610 MHz, the 20 largest sources. Sorting order is increasing right assenssion. Contours corresponds
to levels of 3σ × (−1.4, −1, 1, 1.4, 2, 2.8, 4, 5.6, 8, 11, ...) and greyscale is scaled from −3σ to the maximum value in
the image. On the bottom-left corner of each image appears the name of the corresponding source in the source list,
its flux weighted coordinate as well as the local noise level. The crosses are marking the Gaussian fitting components.
60
Host galaxies and environment of active galactic nuclei
Figure A2: Continued.
CHAPTER 4
Radio-loud AGN in the XMM-LSS field:
optical identification and sample selection
C. Tasse, D. Le Borgne, H. Röttgering, P. N. Best, M. Pierre, B.
Rocca-Volmerange
Submitted
 e XMM-Large Scale Structure survey field (XMM-LSS) is an extragalactic window surveyed in the X-ray with the XMM-Newton satellite.
It has also been observed in the optical with the Canada-France Hawaı̈ Telescope (CFHTLS survey), and in the infrared with the Spitzer Space Telescope
(SWIRE survey). These surveys have been carried out to study the structure
and evolution of the baryonic as well as dark matter on cosmological scales.
In two previous papers, we have presented deep low frequency radio surveys
of the XMM-LSS field. These radio surveys were motivated by the need to
understand the various connections between radio sources’ hosts and their environments.
Using the Very Large Array (VLA) and the Giant Meterwave Radio Telescope
(GMRT), radio observations were carried out at 74, 230, 325 and 610 MHz
(Tasse et al. 2006, Tasse et al. 2007). In paper, we proceed to identify optical
counterparts to the low frequency radio sources, using the CFHTLS optical
catalogue and images. We use a likelihood ratio method and estimate that
∼ 75% of the radio sources have a detected optical counterpart. Using the
CFHTLS and SWIRE data, we derive photometric redshifts for the galaxies
that are identified with a radio source, as well for those that are not. We discuss
the selection of a sub-sample of host galaxies of radio sources, wherein we
estimate the remaining contamination by Type-1 AGN to be ∼ 2%.
T
62
Host galaxies and environment of active galactic nuclei
4.1
I
With the recent achievement of large surveys it becomes possible to study in great detail the relationship between the various classes of active galactic nuclei (AGN), their host galaxies and their
environments (see Heckman & Kauffmann 2006, for a review). Recent findings indicate that the
criteria used to select AGN have a significant influence on the observed properties of the AGN
population. In the local z . 0.3 universe, AGN as selected using optical emission line criteria have
rather high accretion rates, and are preferentially situated in massive galaxies (Kauffmann et al.
2003; Heckman et al. 2004). Quite strikingly, it appears that samples of AGN selected based on
radio luminosity, are statistically independent at low radio power (P1.4GHz < 1025 W.Hz−1 ), from
samples of emission-line selected AGN (Best et al. 2005). This suggests that those two populations
are fundamentally different.
It has been suggested by many authors, that the unified scheme is not always satisfying for
the low-power radio-loud AGN. Specifically, Hine & Longair (1979) have observed that many
radio galaxies do not have the luminous emission lines expected in the framework of the unified
scheme (see also Laing et al. 1994; Jackson & Rawlings 1997). These low-excitation radio galaxies
(LERGs) are very common at low radio power, but some of the powerful FRII radio galaxies are
LERGs as well. In addition the expected infrared emission from a dusty torus is in general not
observed (Whysong & Antonucci 2004; Ogle et al. 2006) nor is an accretion related X-ray emission
(Hardcastle et al. 2006; Evans et al. 2006).
Altogether these arguments suggests that the AGN phenomena actually enclose two distinct
classes of AGN: a radiatively efficient accretion mode (the “Quasar mode”), and a radiatively
inefficient accretion mode (the “Radio mode”) for which there is no evidence that the unified
scheme applies. The physical reasons for the rise of these two accretion modes are still speculative.
It has been suggested that the quasar mode is produced by the accretion of cold gas onto the supermassive black hole, while the accretion of hot gas might drives a radiatively inefficient accretion
(see Hardcastle et al. 2007, for a discussion). These two accretion modes might rise as due to the
nature of the process that brings the gas to the central super-massive black-hole. In that framework,
galaxy mergers trigger a cold gas, radiatively efficient accretion. In contrast, the hot intergalactic
medium gas that is seen to be cooling in the atmosphere of massive elliptical galaxies (see Mathews
& Brighenti 2003; Best et al. 2005, and references therein), triggers a hot gas accretion, that is
radiatively inefficient. A good way of testing this scenario in which the nature of the triggering
process drives the accretion type, is to study the properties and environment of quasar mode and
radio mode AGN.
The XMM-Large Scale Structure (XMM-LSS) field is surveyed at low radio frequencies, infrared, optical, UV, and X-rays over ∼ 10 degree2 (for a general presentation of the associated
surveys see Pierre et al. 2004). This combination of data is well suited for testing the possible link
between triggering process and accretion mode: the near infrared data may provides information
on the presence of hot dust (radiatively efficient accretion), while the combination of width and
depth of the optical data allows for a detailed study of the influence of the environment on the
AGN activity, that might constrain the nature of the triggering mechanisms. In this third paper
of the series, we build a sample of radio-loud AGN that may contain both quasar and radio mode
AGN. In the next paper of the series, we will study their properties including the stellar mass
function, radio luminosity function, and environmental dependence down to low 109−10 M⊙ stellar
mass.
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
63
The outline of the paper is as follows. In Sec. 4.2 we briefly present the radio and optical
data used throughout this paper. In Sec. 4.3 we describe the optical and infrared identifications.
In Sec. 4.4, we derive the photometric redshifts and in Sec. 4.5 we define a subsample of radio
sources’ hosts for which the estimated physical parameters are reliable. The uncertainties on these
parameters are discussed in Sec. 4.6. We discuss the results and their consistency in Sec. 4.7 and
conclude our results in Sec. 4.8.
4.2
S   XMM-LSS 
4.2.1 VLA Radio data at 74 and 325 MHz
We use the radio data described in detail in Tasse et al. (2006). The observations were carried
out using the Very Large Array (VLA) at 74 and 325 MHz simultaneously (4P mode). The radio
survey consists of four pointings, observed in June, July and August 2003 in the A configuration
and in June and July 2002 in the B configurations (see Fig. 4.1). The total integration time was
∼ 60 hours in total, split over 4 pointing centers. The extended A configuration provides the
high angular resolution needed for any optical identification work, whereas the B configuration is
necessary to detect any low surface brightness radio emission. Great attention has been paid to
properly calibrate any corrupting influence of the ionosphere (Cohen et al. 2003).
The different pointings have been assembled into single maps covering ∼ 130 degree2 and
∼ 15 degree2 at 74 and 325 MHz respectively. Since the noise is highly variable over the fields,
we extracted the sources in the maps normalised by the local noise.
At 325 MHz, we have an angular resolution of 6.7′′ , a median 5σ sensitivity limit of 4.0
mJy/beam, and we detect ∼ 850 sources (> 5σ). At 74 MHz, the angular resolution is 30′′ , with a
5σ sensitivity limit of ∼ 160 mJy/beam, and we detect 650 sources. The position accuracy at 325
MHz of ∼ 2′′ is good enough for the optical identification of radio sources.
4.2.2 GMRT Radio data at 230 and 610 MHz
Low-frequency radio observations of the XMM-LSS field have been carried out with the Giant
Meterwave Radio Telescope (GMRT) at 230 and 610 MHz (Tasse et al. 2007). These data provide
two additional flux density measurements for 41% of the radio sources detected at 325 MHz with
the VLA. These observations were motivated by the need of study the influence of the optical
host galaxy and environmental properties on the observed radio spectra for large samples of radio
sources.
At 610 MHz, the coverage is 12.7 degree2 , reaching an average noise level of ∼ 0.3 mJy/beam,
leading to the detection of 767 sources. At 230 MHz, we reach an average noise level of ∼ 2.5
mJy/beam, leading to the detection of 467 radio sources over ∼ 18.0 degree2 . The position accuracies are typically 3′′ and 2′′ at 230 and 610 MHz respectively.
64
Host galaxies and environment of active galactic nuclei
Figure 4.1: The location of the various available surveys in the XMM-LSS field. The thin, grey circles
show the positions of the observed XMM X-ray pointings. The four solid thick circles show the 325 MHz
pointings observed with the VLA, and the dots are at the positions of the sources detected at 325 MHz (Tasse
et al. 2006). The CFHTLS-Wide optical observations are indicated by the dotted lines for the T02 release
and dashed lines for the T03 release. The SWIRE field is indicated by dashed lines.
4.2.3 CFHTLS-W1 optical data
The aim of the Wide component of the Canada France Hawaii Telescope Legacy Survey1 (CFHTLS)
is to cover 170 square-degrees spread over 3 areas of the sky (W1, W2, W3). The W1 field patch
covers 7 × 7 square degrees and is centered at α(J2000)= 02h 18m 00s , δ(J2000)= −07◦ 00′ 00′′ , and
partly covers the XMM-LSS field (Fig. 4.1). The observations are carried out through queued
service observing, using the 1 degree2 MegaCam CCD detector. Typical exposure time are ∼ 1
hour in each u∗ , g’, r’, i’ and z’ band, leading to a limiting magnitude of iAB ∼ 25. The observations
were carried out between June 1, 2003 and Sept. 12, 2005. Of the 13 degree2 of CFHTLS data
used throughout this paper, 10 were part of the Terapix T02 release, and the 3 others were part of
the Terapix T03 release (see Fig. 4.1). The 13 degree2 catalog contains ∼ 3 × 106 objects.
The u∗ g’r’i’z’ magnitudes and associated error bars are Kron-like, in the AB magnitude system. Also, for each object the Terapix catalog contains a flag indicating whether:
1
http://www.cfht.hawaii.edu/Science/CFHLS/
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
65
Figure 4.2: The masks designed by Terapix being very restrictive, we have redesigned new masks by
suppressing some of the Terapix masks. The original masks are hashed, with the masks we kept for our
purpose are double hashed.
• it is an extended or a point-like source. This flag is extracted from the SExtractor “fluxradius” parameter, which measures the radius enclosing 50% of the flux. This classification
cannot be applied to objects fainter than i = 21, due to low signal-to-noise.
• it is masked or not. The masks are built on the basis of the i-band images, and their role is
to reject the field boundary, surrounding zones of saturated stars, satellite tracks, and image
defects.
• it is saturated or not. An object is flagged as saturated when its i-band magnitude satisfies
i < 17.8.
The masks have been designed to be optimal for weak lensing studies. They are therefore very
restrictive. On the basis of the i-band images we have redesigned new, less restrictive masks by
suppressing the less relevant Terapix masks (see Fig. 4.2). These zones unmasked are mainly the
ones situated between the 36 CCDs of the MegaCam detector. We have also included a new flag in
the catalog. The new masks typically mask ∼ 20 − 25% of the total surveyed area, against ∼ 50%
for the original Terapix masks.
66
Host galaxies and environment of active galactic nuclei
4.2.4 SWIRE survey data
The Spitzer Wide-area InfraRed Extragalactic legacy survey (SWIRE, Lonsdale et al. 2003) is a
∼ 50 degree2 high galactic latitude, imaging survey. The SWIRE survey is spread over 6 regions
observed in 4 bands with the IRAC instrument from 3.6 to 8.0 µm and in 3 bands with MIPS
from 24 to 160 µm. Spitzer space observatory has observed 9.1 degree2 of the XMM-LSS field
as part of the SWIRE legacy survey in July 2004 (See Fig. 4.1). Throughout this paper we have
used the data release 2 (DR2 hereafter) band-merged catalog available online2 containing the flux
density measurements at 3.6, 4.5, 5.8, 8.0 and 24 µm for a total of ∼ 2.5 × 105 objects. This catalog
contains the sources detected above 5σ from 3.6 to 8.0 µm and above 3σ at 24 µm, corresponding
to sensitivities of 14, 15, 42, 56, and 280 µJy respectively, and positional accuracies better than
0.5′′ (2σ). Following Rowan-Robinson et al. (2005) we have used the Kron flux density estimates
for the IRAC bands objects brighter than S (3.6)Kron > 1 mJy, and the aperture flux density for the
fainter ones as well as for all the objects at 24 µm. The data reduction and quality assessment is
extensively discussed in Surace et al. (2004).
4.2.5 Field selection
The optical T02/T03 and infrared DR2 data do not entirely cover the radio maps of the XMM-LSS
field (Fig. 4.1). Since our approach is based on photometric redshift estimates for z . 1 radio
sources hosts, the u∗ g’r’i’z’ optical data is of primary importance for our purpose. We therefore
include the radio sources from the multifrequency radio catalog (Tasse et al. 2007) only when they
are in an area covered by at least 3 optical bands. This leads to a remaining fraction of 56.6%,
59.1%, and 56.1% at 230, 325 and 610 MHz respectively. Furthermore we have restricted the area
of study to the 325 MHz field which leads to an additional 6.0% and 0.2% of the objects to be
rejected at 230 and 610 MHz respectively. No sources in that sample are detected only at 230
MHz. The resulting sample contains 604 radio sources.
4.3
O      
We quantify the probability of an optical object to be the true host of a given radio source, by
using the likelihood ratio method which was first described by Richter (1975), and subsequently
modified by de Ruiter et al. (1977), Prestage & Peacock (1983), Benn (1983), and Wolstencroft
et al. (1986). The version of the likelihood ratio method we use in this paper allows us to derive
for each optical candidate, an association probability, which potentially takes into account their
magnitude, location, colour, etc (Sutherland & Saunders 1992).
Prior to the likelihood ratio estimate, we proceed with a visual inspection of the i’-band images,
and classify their radio morphologies into different classes (Sec. 4.3.1). In Sec. 4.3.2, we describe
the likelihood ratio method, and based on the magnitude of each optical candidate, we estimate
a probability of association with a given radio source. We show in Sec. 4.3.3 that this technique
drives a contamination caused by the background sources. Using Monte-Carlo simulations, we
2
see http://swire.ipac.caltech.edu/swire/ for more information.
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
67
correct the estimated probabilities for that effect. We address the issue of completeness and reliability in Sec. 4.3.4. We associate the optical candidates with their infrared IRAC counterparts in
Sec. 4.3.5.
4.3.1 Visual inspection and classification
All the optical images of radio sources have been inspected visually. When needed, the Gaussian
fitting components of multiple component sources (Tasse et al. 2006, 2007) have been considered
separately. This occurred for example in the case of a radio source having each of its component
lying close to a bright optical source. In such cases we have renamed the source following the
convention used by Tasse et al. (2006) and Tasse et al. (2007). Table C1 gives the correspondence
between the original name and the new names. The resulting number of sources to be identified is
621. For all3 sources, we present the i-band images overlaid with the radio contours in Fig. D1.
Preliminarily to the likelihood ratio calculation presented in Sec. 4.3.2, a strong subjective a
priori was given on the relation between the radio emission position and the position of its optical
counterpart.
- Class 1: Sources are classified as Class 1 when the radio emission is assumed to be produced
at the physical location of an optical emission (detected or not). This occurs in sources such as
starbursts, compact core dominated radio sources or radio sources where the radio core can be
clearly identified. In these cases, knowing the errors on the radio and optical positions, a statistical
approach can directly be used to identify the optical progenitor of a considered radio source at a
given position (Sec. 4.3.2).
- Class 2: When no radio core is identified, as often in classical double lobes FRII (Fanaroff
& Riley 1974) radio sources, only a weak a priori can be considered for the optical host position.
Following Best et al. (2003) we have used a case-to-case approach: when the morphology does
not give any clue on the location of the optical host, we classify the radio source as Class 2. This
aspect is described in Appendix A and discussed in greater detail in Best et al. (2003).
- Class 3: When the environment has a large effect on the radio morphology, the flux weighted
radio centroid and associated error bars can be very far from the real optical host. When suggested by the combination of radio and optical properties (such as an elongated lobe pointing at
a bright object), we use the radio morphology to determine the position of the optical counterpart
and we classify the object as Class 3. Note that, because of that case-to-case approach, we cannot calculate the completeness and reliability level of the Class 3 subsample as we do for Class 1
and 2 objects in Sec. 4.3.4. Comments on the Class 3 individual sources are given in Appendix B1.
- Class 4: We have classified as Class 4 the resolved radio sources for which the morphology
does not suggest the presence of jets. Radio halos and relics are a part of this class. Comments on
3
Irrespective of whether the identified host is saturated or flagged. Six of the identified radio sources do not appear
(J0220.5-0348, J0220.4-0350, J0226.0-0542, J0225.9-0545, J0229.9-0447 and J0230.0-0440) in Fig. D1 because the
i-band image was either totally corrupted or not available at this location.
68
Host galaxies and environment of active galactic nuclei
these sources can be found in Appendix ??.
- Class 5: When the radio source overlaps a bright saturated source, or a satellite track for
example, we have classified the source as Class 5, meaning we cannot proceed with the identification. The Class 5 category should in principle correspond to a masked region with overlapping
objects being flagged (see Sec. 4.2.3)
4.3.2 Optical identification: the likelihood ratio method
Figure 4.3: Left panel: the cumulative distribution of the angular distance from the radio source centroid to
the closest neighbour in the optical catalog. In the simulated catalog, the magnitude cut is mi = 24, and the
fraction θ(m) of radio sources having an optical counterpart is a variable. The first dash-dotted line on the
left correspond to θ = 100%, while dash-dotted line on the right corresponds to θ = 0%. In this case, the
best fit to the distribution of the actual data is obtained with θ = 60%. The right panel shows the best values
of θ found for different limiting magnitudes. We model θ(m) as being constant above i = 27 and below
i = 17 and fit by a polynomial between these values (dashed line).
We quantify the probability that an optical candidate is the true optical counterpart of a given
radio source, by calculating the likelihood ratio as described in (Sutherland & Saunders 1992):
2
θ(< m, x1 , x2 ...) exp(− r2 )
(4.1)
LR(r, m, x1 , x2 ...) =
2πσα σδ ρ(< m, x1 , x2 ...)
where m is the i-band magnitude of the optical candidate, and the values {x1 , x2 , ...} stand for a list
of parameters {X1 , X2 , ...} that can be any numerical quantity considered as relevant, such as the optical colours, or derived photometric redshifts. θ(m, x1 , x2 ...) is the a priori probability that a radio
source has an observed optical counterpart with magnitude < m and values {x1 , x2 ...}. ρ(m, x1 , x2 ...)
is the surface number density of objects having their magnitude < m and values {x1 , x2 ...}. The parameter r is the uncertainty-normalised angular distance between the radio core and the optical host
candidate in the band merged u∗ g’r’i’z’ optical catalog, defined as r = ((∆α /σα )2 + (∆δ /σδ )2 )1/2 ,
where ∆ stands for the positional difference, σ for the uncertainty, and α and δ for right ascension
and declination respectively. On the axis α and δ, the uncertainty is the quadratic sum of the uncertainty on the radio position and on the optical position σ2α = σ2α,radio +σ2α,opt and σ2δ = σ2δ,radio +σ2δ,opt .
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
69
We adopt the T02/T03 astrometry accuracy estimate σopt ∼ 0.3′′ being independent on the magnitude m. The accuracy on radio position σradio is different for every source depending on various
parameters such as local noise level in the radio data, and Gaussian fitting parameters (Tasse et al.
2006). As explained in Sec. 4.3.1, we take into account the radio source morphology class to
estimate σradio .
Using the formula given by Sutherland & Saunders (1992), the probability Pid (i) of the ith
candidate to be a true identification is:
Pid (i) = X
j
LRi (r, m, x1 , x2 ...)
LR j (r, m, x1 , x2 ...) + (1 − θ(mlim ))
(4.2)
where θ(mlim ) is the fraction of radio sources having a detected optical counterpart at the limiting
magnitude of the survey, i refers to the candidate under consideration and j runs over the set of all
possible candidates. Contrary to the formulae given by de Ruiter et al. (1977), Benn (1983) and
Wolstencroft et al. (1986), this equation includes information from the other candidates, and is self
P
P
consistent in the sense that i Pid (i) < 1 and h i Pid (i)i = θ(mlim ) (Sutherland & Saunders 1992).
We give a first estimate of the probability of association by assuming θ and ρ depend only on
the object magnitude m. In practice, m is taken as the i-band magnitude of the optical candidate,
and for each radio source we calculate the function ρ(m) in a 2′ square centered on the radio source
centroid. This has the advantage of potentially taking into account the effect of clustering, ie the
variation of the surface density, as a function of position. At the limiting magnitude of the survey,
ρ ∼ 2 × 10−2 arcsec−2 . We estimate the values of the function θ(m) as follows. We consider i-band
magnitude cuts in the interval 16 < i < 30 with an increment ∆i = 0.5. For each of these cuts we
generate a radio catalog having uniformly distributed positions, and a corresponding optical catalog
in which a given fraction θ(m) of radio sources have an optical counterpart. The optical hosts of
radio sources have their position scattered by the radio and optical positional uncertainties. We then
consider the distribution of the angular distance between radio sources and their closest object in
the optical catalog. For each value of the limiting magnitude m, we compare the distributions of
the smallest angular distance for the random catalog and the actual dataset through a KolmogorovSmirnov test. The retained fraction θ(m) is the one corresponding to the maximum KolmogorovSmirnov probability (see left panel of Fig. 4.3 for an example). For each i-band magnitude cut, the
test is repeated 10 times, so that we can estimate an error bar on θ(m). The right panel of Fig. 4.3
shows the variation of θ(m) with the limiting magnitude. We model θ(m) using a fit composed of
two linear parts and a polynomial of degree 5. We assume the function θ(m) we have derived for
the Class 1 sources is valid for the extended Class 2 sources as well.
For each of the 621 radio sources, we have derived the likelihood ratio of the optical counterparts situated in a 20′′ radius around the assumed radio centroid. For each radio source, we
have retained the 5 objects having the highest likelihood ratios. The total number of radio sources
P
having an optical counterpart is Pid ∼ 482. Thus ∼ 76% of radio sources have a detected optical
counterpart. For comparison, Simpson et al. (2006) probe radio quiet AGN and/or closeby starburst
lim
∼ 100 µJy.beam−1 )
radio sources, found that 90% of their faint radio source population (S 1.4GHz
have a detected optical counterpart in the Subaru/XMM-Newton Deep Field that has an i-band
limiting magnitude mi ∼ 27.5 (Kashikawa et al. 2004). In the CENSOR survey, Best et al. (2003)
lim
found that 63% of their brighter radio sample (S 1.4GHz
∼ 7.8 mJy.beam−1 ) that are uniquely com-
70
Host galaxies and environment of active galactic nuclei
posed of radio-loud AGN have an optical counterpart in the optical i-band limited (mlim
∼ 23)
i
images. Considering the i-band limiting magnitude of our survey is mlim
∼
25,
our
fraction
∼
76%
i
seems reasonable.
4.3.3 Contamination correction
As explained in the introduction, we aim to study the properties and environment of radio sources’
hosts over a wide range of stellar mass. In order to derive a reliable estimate of stellar mass function, it is important to understand the effect of contamination by miss-identifications. As show in
Fig. 4.4, using the probability estimates from the previous section, we find that ∼ 8% of radio
sources have a host galaxy with a stellar mass in the range . 108 M⊙ . Running a Monte-Carlo
simulation (described bellow), show that most of this fraction is due to contamination from background sources. In order to derive the probability of association we have only taken into account
the magnitude of the optical hosts candidates, and not their u∗ g’r’i’z’ magnitude measurements of
which the stellar mass, star formation rate and photometric redshifts estimates depend on. Therefore the distribution of the radio sources optical host population along other dimensions than the
i-band magnitude may be contaminated by the normal galaxies parameter distributions (See Fig.
4.4 for example). In this section, we address this issue statistically by estimating the dependence
of the probability density functions θ and ρ mentioned in Sec. 4.3.2, on the stellar mass, redshift,
and specific star formation rate (that we derive in Sec. 4.4). We use this a priori knowledge to
derive a new estimate of the association probability Piid ( j). As shown in Fig. 4.4, this approach
minimises the effect of contamination by miss-identification.
The parameter space {z ph , M, sS FR} is first gridded onto: i-band optical magnitude m (15 <
z ph < 30), redshift (0 < z ph < 2), stellar masses (6 < log(M/M⊙ ) < 13) and specific star formation
rates (−13 < log(sS FR/yr−1 ) < −7), with steps of ∆m = 1, ∆z = 0.2, ∆ log(M/M⊙ ) = 0.5, and
∆ log(sS FR/yr−1 ) = 0.3 respectively.
We estimate the values of the function ρ over the parameter space {m, z ph , M, sS FR} as follows.
For a random subsample of normal galaxies, we calculate the observed number of galaxies in each
cell of the parameter space. One obtains ρ(m, z ph , M, sS FR) by normalising the total number of
sources in such a parameter space to the surface density at the limiting magnitude.
For the radio sources’ hosts, in each cell C of the parameter space, the observed number of
P
sources is nobs (C) = Ωi, j (C) [Piid ( j)], where Piid ( j) is the association probability between the jth
optical candidate and the ith radio source (Sec. 4.3.2), and the Ωi, j (C) is the set of {i, j} optical
candidates which are located within the cell C. One can write the observed number of sources
nobs (C) as nobs (C) = ntrue (C) + nmID (C), where ntrue (C) and nmID (C) are the true and miss-identified
observed sources in the cell C respectively. The function θ(m, M, sS FR, z) is estimated in each
cell by removing the miss-identified contribution, and by normalising the total number of radio
sources in the {z ph , M, sS FR} parameter space to unity. In order to estimate nmID (C), we generate
10 simulated radio catalogs in which we associate a fraction θ(mi ) of radio sources with galaxies
of magnitude mi (see Sec. 4.3.2), introducing a scatter between the radio and optical positions
corresponding to the astrometrical errors of the individual sources in the original radio catalog.
We proceed with the optical identification for these simulated catalogs, and derive the association
probabilities as described in Sec. 4.3.2. Knowing the input true optical counterpart, and removing
them from our catalog, we can compute the miss-identification contribution nmID (C) (see Fig. 4.4).
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
71
Figure 4.4: Stellar mass distributions in the redshift range 0.1 < z < 1.2, for the observed radio sources’
hosts (solid line) and for the estimated contribution from the miss-identifications (grey dashed line). The
dotted line (’Non Normalised Difference’) corresponds to the difference between these two distributions.
We have used such a miss-identification contribution subtraction to estimate θ(m, M, sS FR, z), and give
estimates of the association probabilities corrected from the miss-identification contribution.
We have re-computed the probabilities of association using these estimates of θ and ρ. Fig. 4.4
shows the miss-identifications have been properly removed from the observed mass distribution
of radio sources’ hosts. For example while the contribution of low stellar mass galaxies with
log(M/M⊙ ) < 8.5 was as high as 8% and was mainly due to miss-identification, the corrected
number is ∼ 2%. Results appear in Tab. C2.
4.3.4 Completeness and reliability
In this section, we discuss the completeness and reliability of the identified sample presented in
Tab. C2. In practice it is useful to define two samples from the association probabilities we
have defined in Sec. 4.3.3. The first (S1 hereafter) contains for each radio source all the optical
candidates and their individual association probabilities and is used to estimate the radio sources’
optical host density in the parameter space. In the next paper of the series, we will mostly use the
S1 sample, to estimate comoving number density down to low stellar mass. In a given region R of
the parameter space we estimate the mean number hnid (R)i of radio sources optical counterparts
P
as hnid (R)i = Ωi, j (C) [Piid ( j)] where Piid ( j) is the association probability between the jth optical
candidate and the ith radio source (Sec. 4.3.2), and Ωi, j (C) is the set of all {i, j} optical candidates
which are located in the region R of the parameter space. The second sample (S2) is derived from
S1, and contains for each radio source the optical candidate that has the highest likelihood ratio.
S2 is handy for displaying discrete properties of radio sources’ hosts (see for example Fig. 4.9).
In practice, the S1 sample is an extensive list of optical candidates. Many of those have low or
72
Host galaxies and environment of active galactic nuclei
negligible probability of association with a given radio source. In order to reject the most unlikely
optical candidates from the table presented in this paper (Tab. C2), we apply a likelihood ratio cut
LRcut to the samples S1 and S2. The completeness and reliability of such a selected source list will
be affected by the value of LRcut , since a fraction of candidate that are true optical identifications
will be filtered out. Given an LRcut , the completeness and reliability levels can be written as:


X


Pid (i) /Nid
C(LRcut ) = 1 − Nre j +
(4.3)
LRi <LRcut
R(LRcut ) = 1 − (1/Nid )
X
LRi ≥LRcut
(1 − Pid (i))
(4.4)
where Nre j is the number of true optical identifications rejected prior to the likelihood ratio cut, Nid
is the total number of true optical identifications, and Pid (i) is estimated following Eq. 4.2. For
the S1 sample we have Nre j = 0, while the S2 sample, prior to the likelihood ratio cut a number
P
Nre j = i,i0 Pid (i) of true identification has been rejected, where for each radio source i0 is the
PP
optical candidate that has the highest likelihood ratio. We estimate Nid as i j [1 − Piid ( j)], where
i runs over all radio sources and j over the individual optical identification candidates.
In Fig. 4.5, the completeness and reliability levels for the samples S1 and S2 are plotted as a
function of LRcut for Class 1 and Class 2 sources. The lower completeness level for the Class 2
sources is due to the error on the a priori positions of their optical host being higher. For the S2
sample, for both classes we choose LRcut = 0.5 corresponding to completeness levels of ∼ 88% and
∼ 83%, and reliability levels of ∼ 87% and ∼ 83% for the classes 1 and 2 respectively. As shown
in Fig. 4.5, this value for LRcut allows us to reject ∼ 75% of all optical candidates from the original
optical counterpart candidates source list, without affecting the reliability and completeness level
of the most likely optical identification source list.
As mentioned in Sec. 4.3.1, because Class 3 sources are identified subjectively, completeness and reliability level cannot be derived. All the information about each radio source optical
identification appears in Tab. C2. A flag allows to construct the S2 sample.
4.3.5 Infrared association
Following Surace et al. (2004), we associate to the optical candidates the infrared objects of the
SWIRE DR2 that are closer than 1.5′′ . This provides flux density measurements at 3.6, 4.5, 5.8,
8.0 and 24 µm (Sec. 4.2.4) for the radio source optical hosts identified above. Considering the
source density in the SWIRE DR2 band merged catalog being ∼ 3.2 × 104 deg−2 , and assuming a
Poisson statistics, the chance of association with a random background source is ∼ 2%. In the case
of detection of more than one source within the search radius we have only considered the closest
object.
Of the sample of radio sources optical counterparts, ∼ 61% have been associated with an
infrared counterpart at 3.6 and 4.5 µm, against ∼ 33%, ∼ 27% and ∼ 18% at 5.8, 8.0 and 24
µm respectively.
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
73
Figure 4.5: Reliability level for the S2 sample and Completeness for the S1 and S2 samples as a function
of the likelihood ratio cutoff LRcut . These quantities are displayed for the Class 1 (black) and Class 2 (grey)
sources. The dotted lines indicate the fraction of optical candidates from the original source list that remain
in the S1 source list
4.4
S E D 
The photometric redshift method consists of fitting spectral energy distribution (SED) templates
to the observed magnitude measurements and their associated error bars using a standard χ2 minimisation. Such galaxy templates can be built from stellar synthesis code, and physical properties
such as age, stellar mass, or star formation rate can be inferred. However, the radio selected galaxy
population is dominated by a population of AGNs, whose optical emission can dominate over the
contribution from the stellar population to the overall SED, such as in the extreme case of an optical
quasar.
In this section, we use two photometric redshift approaches, the combination of which allows us
to (i) derive physical quantities related to the observed galaxies in our survey, (ii) address the issue
of Type-1 AGN contamination, and (iii) assess the reliability of the photometric redshifts. The first
method (Sec. 4.4.1) uses the ZPEG stellar synthesis code (Le Borgne & Rocca-Volmerange 2002),
which yields quantitative information on the physics of these objects, such as stellar masses, and
star formation rates. Dust emission has not been included in these models, and hence this method
can only be used in the wavelength range λ . 1 µm. The second method (Sec. 4.4.2) uses SED
templates built mostly from observations, which will provide us a more qualitative understanding.
This method has the advantage of covering a large wavelength range from far infrared to soft uv
light, as well as probing a wide range of objects from normal galaxies to Quasars. In Sec. 4.5 we
select a subsample of galaxies for which the ZPEG output parameters are reliable.
74
Host galaxies and environment of active galactic nuclei
4.4.1 Theoretical approach: ZPEG
In this section, we compute photometric redshifts for ∼ 3 × 106 galaxies of the T02/T03 release by
using the photometric redshift code ZPEG4 (Le Borgne & Rocca-Volmerange 2002).
The ZPEG template library is synthesised from nine evolutionary scenarios defined by a minimum number of free parameters. Assuming an universal Initial Mass Function (IMF, Kroupa
2002), the time scale τ of star formation is derived from both a star formation efficiency associated
with a Schmidt law and an e-folding time scale for the infall of gas onto the galaxy. The epoch of
galactic winds in the galaxy’s history is also a free parameter in the models. Details of these physical parameters defining the various scenarios can be found in Le Borgne & Rocca-Volmerange
(2002). The templates used by ZPEG are constructed from these models for ages ranging from 10
Myr to 14 Gyr after the birth of the first stars, with an additional constrain on the age of the universe at every redshift. For example, for an age > 10 Gyr, a short star formation timescale (τ < 1
Gyr) is more appropriate for an early type galaxy while τ ∼ 2 Gyr corresponds to a typical spectrum of an Sb galaxy (see Bruzual A. & Charlot 1993; Fioc & Rocca-Volmerange 1997). Effects
such as metal enrichment, dust extinction, and nebular emission lines are coherently taken into
account depending on evolution scenarios (see Le Borgne & Rocca-Volmerange 2002, for more
details). As mentioned earlier, the infrared emission from dust is not taken into account within the
model, and hence this approach is used only in the optical and near infrared domains (CFHTLS
u∗ g’r’i’z’ bands).
Figure 4.6: Examples of the synthetic templates used by ZPEG. Because the stellar synthesis model does not
include dust emission, we only use the CFHTLS magnitude points in the u∗ g’r’i’z’ bands. These templates
correspond to spiral (Sa), elliptical (E) and starburst (SB) galaxies.
In each of the nine scenarios, 57 time steps were used between 10 Myr and 14 Gyr, for the age
of the galaxy template. The stellar mass varies between 106 and 1013 M⊙ . Assuming a ΛCDM
4
The ZPEG code is available online at http://www2.iap.fr/cgi-bin/pegase/zpeg.pl
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
75
Figure 4.7: Examples of SED templates of optically active AGNs retrieved from the SWIRE template
library (Polletta et al. 2007) that we have used to derive photometric redshifts and spectral types. Because
these SEDs include the infrared dust emission in addition to the u∗ g’r’i’z’ optical data we could use the
infrared flux density measurements to constrain the best fit associated parameters.
cosmology, the SEDs are k and e-corrected (cosmologically and evolutionary respectively). The
redshift varies between z = 0 and z = 2, in steps of ∆z = 0.01. Fig. 4.6 shows examples of
synthetic templates used by ZPEG. The value of the χ2 is recorded in the parameter space of the
input parameters of the model. Error bars on best fit parameters are taken at χ2min + 1 if χ2min < 1
and at 2 × χ2min if χ2min > 1 (see Sullivan et al. 2006, for a detailed discussion on these estimates). In
addition, assuming a given object is located in the redshift range probed within the redshift grid,
the function χ2 (z) is directly translatable into p(z) the redshift probability function. Multiplied by
dz, p(z) gives the probability of an object to be located between z and z + dz. In the next paper, we
will use this information to derive an overdensity parameter, and study the environment of radio
sources.
The ZPEG output parameters that are specially relevant to our study are the estimates and
associated error bars of the redshift, the stellar mass, and the star formation rate. However, the
true star formation history of a galaxy can deviate from the idealised scenarios outlines above. In
order to give a reliable estimate of the star formation rate, this quantity is averaged over 0.5 Gyr
(SFR0.5 hereafter). The uncertainties associated with these quantities are discussed in more detail
in Sec. 4.6 and Sullivan et al. (2006).
76
Host galaxies and environment of active galactic nuclei
Figure 4.8: The reduced χ2 distribution from SED fitting using SWIRE template library. The secondary
bump at χ2 & 100 shows ∼ 30% of the population is not properly fit. Assuming this does not drive any
selection effect, we have rejected these sources from the spectral type distribution study.
4.4.2 Semi empirical approach
The SWIRE template library5 (Polletta et al. 2007) contains 25 templates including three ellipticals, seven spirals, six starbursts, seven AGN (three Type-1 AGNs, four Type-2 AGNs), and two
composite (starburst+AGN) templates. These templates cover the wavelength range between 1000
Å and 1000 µm, including spectral features such as stellar emission, emission and absorption
lines, dust extinction and emission. These are partly based on theoretical SED models, as for elliptical, spiral and starburst templates (GRASIL, Silva et al. 1998), and partly on observations, as in
the case of the AGN templates. For more information on the SWIRE template library see Polletta
et al. (2007).
Using a standard χ2 minimisation procedure with the redshift and the SED normalisation as
free parameters, we have fitted the u∗ g’r’i’z’ and IRAC flux density measurements of the radio
source sample defined in Sec. 4.3. The redshift varies in the range 0 < z < 3 with steps of 0.05,
and the SED normalisation is unconstrained. The value of the χ2 has been recorded in the space
{t, z}, where t stands for the template type. Assuming the statistics to be normal, the probability
density p of observation of a given χ2 follows p ∝ χr−2 exp(−χ2 /2) where r is the number of
degrees of freedom. Converting the χ2 in the {t, z}-space to probability, and normalising to unity,
and integrating through the z-axis at t, we obtain the probability of a template t to be the true SED.
We will make full use of these probability estimates in Sec. 4.5.2.
Fig. 4.8 shows that the reduced χ2 distribution of the best fit templates is bimodal: ∼ 26% of the
normal galaxy population have a χ2red > 100 against ∼ 34% for the radio sources’ hosts. This effect
5
The SWIRE template library is available online at http://cass.ucsd.edu/SWIRE/mcp/templates/swire templates
.html
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
77
can be caused by the underlying true SED of the objects having χ2red > 100 being represented by
one or more SED templates that are very different from the SEDs present in the SWIRE template
library. We have investigated that issue by comparing the ZPEG output parameter distribution of
the χ2red > 100 objects, to the rest of the population. The stellar mass, redshift and star formation
rate distribution look similar for these two populations, suggesting that this bi-modality is due to
variability that is higher in infrared than in optical: if the objects having χ2red > 100 would be
constituting a special SED type population, one would expect to find biased optical properties,
which is not the case. Therefore, in the following, we only include the galaxies having spectral fits
with χ2red < 100, assuming this does not drive any selection effect on the observed properties of
radio sources.
Results from the SED fitting appear in Tab. C2.
4.5
S 
As discussed in the introduction, our goal is to build a sample of radio-loud AGN with reliable
physical parameter estimates, as derived using the stellar synthesis code ZPEG, and study their
properties and environment. However, many AGN (mostly Type-1) have their SED dominated by
the central core light in the optical domain, in the form of optical and UV continuum emission,
or luminous emission lines, such as in the extreme case of optical quasars. The presence of such
objects within our dataset introduce a contamination, as their physical parameter estimates are
corrupted. In this section, we pay special attention to select a subsample of Type-2 radio-loud
AGN for which the physical parameter estimates are reliable. The result of the following selection
is encapsulated into a single flag that appears in Tab. C2.
4.5.1 Selection of the basic sample
We first proceed with basic selections based on the u∗ g’r’i’z’ optical data. A given optical object
at a location {α0 , δ0 }, being detected in Nb optical bands, having an i-band magnitude i, and a
stellaricity flag s is included if it satisfies all of the following conditions:
1- i < 24
2- Nb ≥ 3
3- i > 18
4- {α0 , δ0 } corresponds to a non-masked area
5- s = 0 (non point-like, Sec. 4.2.3)
6- 0.1 < z ph (ZPEG) < 1.2
7- 0.1 < z ph (S WIRE) < 1.2
78
Host galaxies and environment of active galactic nuclei
The selection criterion (1) removes faint objects having large flux density error bars as well
as false detections close to bright stars. (2) filters out the objects for which too few flux density
measurements are available. The selection criteria (3) and (4) remove the saturated and masked
objects respectively, for which the magnitude measurements are corrupted. We remove point-like
objects, corresponding to a contribution from i < 21 contaminating Type-1 quasars using (5).
Finally, the 4000Å break of the stellar population being the most constraining feature of SEDs
in the optical regime, we restrict our study to the redshift range z ph < 1.2, corresponding to the
4000Å break being in z’ filter (6-7). Furthermore, we restrict our study to z > 0.1, as we expect
significant radio emission from starbursts at z < 0.1 (this aspect is further discussed in Sec. 4.5.3)
4.5.2 Type-1 AGN contamination
In order to reject the remaining contaminating Type-1 AGN, we have used a combination of criteria
based upon (i) the optical colours, and (ii) the goodness of the SWIRE library SED template fits.
The optical colour classification criteria is based on the g-r versus r-i colour-colour diagram
(bottom panel of Fig. 4.9). Computing the tracks of a Type-1 AGN, a starburst, and an elliptical
galaxy in that colour-colour space, it is clearly seen that the Type-1 AGN occupy a restricted area.
This is due to the SED of Type-1 objects being a power law in the optical domain, while the SED
of normal galaxies show a high to moderate 4000Å break. We classify a source as contaminating
if it lies in the region R(g′ , r′ , i′ ) defined as follows:
R(g′ , r′ , i′ ) ≡
(g′ − r′ < 0.38 ∧ r′ − i′ < 0.5)
∨ (g′ − r′ > 0.38 ∧ r′ − i′ < 0.2)
(4.5)
where ∧ and ∨ stand for the AND and OR logical connectives.
For the SED-type criteria, we first classified the SWIRE templates in two groups. The first
group contains the SEDs in which there is either no contribution, or moderate contribution from an
AGN (“N/NL” for Normal/Narrow Line), while the second class contains the templates with strong
AGN contribution such as the QSOs (“BL” for Broad Line). Tab. 4.1 shows how the SWIRE
templates have been classified in these two categories. We have classified an object as BL when
its probability PBL (Sec. 4.4.2) of being a BL-type object satisfies:
C BL (PBL ) ≡ (PBL > 60%)
(4.6)
Finally, combining the two selection criteria (Eq. 4.5 and 4.6) gives: [C BL (PBL )] ∨ [R(g′ , r′ , i′ )].
We investigate below the consistency of the selection.
Stern et al. (2005), from the study of a large sample of spectroscopically identified sources
in the AGN and Galaxy Evolution Survey (AGES, Cool 2006), have shown that broad line AGN
can be well separated from the mean galaxy population in the [3.6]-[4.5] versus [5.8]-[8.0] colourcolour space. In Fig. 4.9 we plot the location of radio source hosts in this colour-colour plot.
Of the 10 objects classified as BL, 1 (10%) lies outside the area given by Stern et al. (2005), in
good agreement with the 9% given by that author. In the g-r vs r-i colour-colour diagram, the area
defined by R(g’,r’,i’) includes ∼ 5% of the objects classified as N/NL using SED fitting, against
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
79
Table 4.1: The distribution of the original SWIRE template name through our classification (see Sec. 4.4.2).
N/NL
BL
Ell13 Sb N6090 Spi4 I19254 BQSO1 Torus
Ell2
Sc N6240 I20551 Mrk231 QSO1 TQSO1
Ell5 Sdm Sey18 I22491
S0
Sd
Sey2 M82
Sa Arp220 QSO2
80% of the objects classified as BL sources. Furthermore, we show in Fig. 4.10 of Sec. 4.6
that the ZPEG photometric redshift estimates are in good agreement with the SWIRE template fits
for the N/NL, but not for the contaminating BL objects (σ(z) ∼ 0.1 against σ(z) ∼ 0.3). This
coherence suggests that when all IRAC and optical bands are available, we are able to detect the
contaminating broad line AGN in an efficient way.
However, only ∼ 37% of our radio sources’ hosts are detected in 9 bands, and it is likely that
band availability affects the effectiveness of our SED-type selection technique. In order to address
this, we assume that for the sample of sources detected in all 9 bands, we have effectively detected
all the true contaminating Type-1 sources. For this bright sample, we recalculate the photometric
redshifts using the SWIRE template library only with their (1) u∗ g’r’i’z’ + 5.8 and 8.0 µm (2)
u∗ g’r’i’z’ and (3) g’r’i’ flux density measurements. It can be seen from Fig. 4.9 that the removal
of infrared data has a large influence on the SED-type classification. In the cases where only
optical data are available, it appears that our selection criteria leads to a remaining contamination
of 2/37 ∼ 5.4%, while there should be no remaining contamination when infrared data is available.
The fraction of radio sources not having infrared IRAC measurements being ∼ 39%, we estimate
the remaining contamination to be ∼ 2%.
Results of the N/NL/BL classification for the S1 and S2 samples appears in Tab. C2.
4.5.3 Starburst selection
The intense star formation occurring in starburst galaxies is known to produce a significant amount
of radio emission. Since our purpose is to study the triggering processes and the evolution of the
radio-loud AGN population there is a need to identify and remove these starbursts.
Fig. 4.11 shows the relation between the SFR and 1.4 GHz radio power given by Cram (1998),
as well as the location of our sources in that plane. We have used the ZPEG star formation rate
estimator SFR0.5 and the 1.4 GHz radio power as estimated using the ZPEG photometric redshift
and the spectral index (Tasse et al. 2006, 2007). We reject a source if the contribution from star
formation to the radio luminosity is higher than 10%, which leads to the selection of 5 radio sources
within the subsample selected in Sec. 4.5. As mentioned in Sec. 4.4.1, the ZPEG SFR estimate
is averaged over 0.5 Gyr, while a starburst may occur on time scales ∼ 0.1 Gyr. We investigate
this issue by estimating the number of starbursts we should observe within our dataset. To do this
we consider a starburst radio luminosity function given by Oliver et al. (1998), combined with the
starburst luminosity function evolution as given by Pozzi et al. (2004) up to z = 1. Given our flux
80
Host galaxies and environment of active galactic nuclei
Figure 4.9: Top panel: The g-r vs r-i colour-colour plot for the S2 sample detected in all the u∗ g’r’i’z’ and
IRAC bands (black dots), and for a subsample of normal galaxies similarly selected (grey dots). We classify
as contaminating those objects which lie in the hashed area. The open circles indicate the classification
based on the SED-type criteria (See Bottom panel). We plot the colour-colour tracks for a Type-1 QSO, an
elliptical, and a starburst galaxy. The square, star, diamond and triangle symbols stand for redshifts 0, 0.5,
1.5, and 2 respectively. Bottom panel: The [3.6]-[4.5] versus [5.8]-[8.0] colour-colour plot for the sources
of the same sample. The grey dotted line indicates the region in which Stern et al. (2005) finds ∼ 90% of the
spectroscopically identified broad-line AGN. Based on the SWIRE template library, the objects best fit by a
BL AGN templates are plotted with small black circle. The bigger circles indicate the sources classified as
BL contaminating AGN when progressively removing the information on the infrared bands.
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
81
Figure 4.10: For a subsample of sources detected at 3.6 and 4.5µm, we can compare the photometric redshifts as estimated by the methods. The top panel shows the photometric redshift zZPEG against zS WIRE
as estimated using ZPEG and the SWIRE template library respectively, for the contaminating sources (“rejected”) and for the selected sources. The bottom panel shows the cumulative distribution of zZPEG −zS WIRE .
The agreement is σ ∼ 0.13 for the selected sample, suggesting the physical parameters as estimates by ZPEG
will be reliable for these objects.
82
Host galaxies and environment of active galactic nuclei
density limit at 1.4 GHz of ∼ 1.5 mJy, we calculate there should be 6.2 starburst galaxies within
our sample. This good correspondence with the number of sources classified as starbursts suggests
that the remaining sources are radio-loud AGN.
Figure 4.11: The distribution of the radio sources in the P1.4 - SFR plane. The open circles indicate upper
limits. The solid line is the SFR-P1.4 relation given by Cram (1998). Below the dashed line, the contribution
by star formation to the radio power is higher than 10%. In order to retrieve a purely radio-loud AGN
sample, in the final source list, we have flagged these sources as being starburst-like.
4.6
O  
4.6.1 ZPEG standards
In this section we discuss the uncertainties of ZPEG output parameters for the sample of radio
sources’ hosts selected in Sec. 4.5.
As part of the SNLS (SuperNovae Legacy Survey), Sullivan et al. (2006) have discussed the
uncertainties of ZPEG physical estimates in great detail. Their photometric dataset is very similar to ours as they use the broad band CFHTLS-D1 and D4 deep survey data, while we use the
CFHTLS-W1. These fields were imaged in the same u∗ g’r’i’z’ filters, but the observations differ
in that the Deep surveys are deeper by a factor of ∼ 2, which should not give rise to any systematic
differences between the accuracy of their photometric redshifts and ours.
Using a sample of 116 galaxies having measured spectroscopic redshift Sullivan et al. (2006)
estimate the uncertainties of the ZPEG photometric redshifts and associated parameters. Specifically, the distribution of ∆z = z spec − z phot has a median (or 50% quantile) offset of q0.5 (∆z) = 0.02,
and a 90% quantile of q90 (∆z) = 0.15. Assuming the distribution to be normal, this corresponds
to a standard deviation σ(∆z) = 0.09. In order to check that this estimate is compatible with our
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
83
Figure 4.12: Effects of the AGN activity in the form of emission lines on the ZPEG estimate of the photometric redshifts derived from Monte-Carlo simulation for L[OII] = 1040 erg.s−1 (dots), and L[OII] = 1042
erg.s−1 (open circles). The top panel shows the zest -zreal versus zreal , where the zreal is the real redshift and
zest is the estimated photometric redshift. In the plot of the bottom panel, we proceed to the same analysis
with the stellar mass estimates.
dataset, we plot the distribution ∆z′ = zZPEG − zS WIRE of photometric redshifts as estimated by the
ZPEG and SWIRE template libraries respectively for the normal (N/NL) and contaminating (BL)
objects being detected in at least 7 bands (Fig. 4.10). Although the ZPEG and SWIRE libraries
are built in a very different manner (Sec. 4.4), for the N/NL sources the distribution of ∆z′ fits
a normal distribution with a standard deviation of σ(∆z′ ) = 0.13. This higher value is expected
84
Host galaxies and environment of active galactic nuclei
as we compare
two independent photometric redshift estimates, corresponding to a difference of
√
factor 2 between σ(∆z) and σ(∆z′ ), in agreement with the factor ∼ 1.4 observed.
Sullivan et al. (2006) also discussed in detail the accuracies of the SFR0.5 and stellar mass
estimates. In the most extreme case of galaxies experiencing recent star formation events, they derive q90 (∆ log(M/[M⊙ ])) ∼ 0.3 and q90 (∆ log(S FR/[M⊙ .yr−1 ])) ∼ 0.6 for the stellar mass and SFR
respectively, corresponding to standard deviations of σ(∆ log(M/[M⊙ ])) ∼ 0.14 and σ(∆ log(S FR/
[M⊙ .yr−1 ])) ∼ 0.28. More importantly, the error bars as estimated by ZPEG based on the χ2 statistics are consistent with the observed errors, suggesting that the uncertainties for the individual
objects are properly estimated.
Using the stellar masses and photometric redshifts estimates, in the next paper of the series
(Tasse et al. 2007 in prep), we will show that the stellar mass function, and radio luminosity
function derived using our dataset are all consistent with previous results.
4.6.2 The influence of emission lines
The activity in the central core of powerful radio-loud AGN is known to produce luminous emission lines with L[OII] ∼ 1040 − 1044 erg.s−1 (e.g. McCarthy 1993; Zirbel & Baum 1995).
In order to investigate whether these emission lines can influence the photometric redshift
estimates, we generate a catalog of galaxies with SED taken randomly from the ZPEG SED library,
corresponding to random stellar masses, age, star formation rate, and redshift. To each SED, we
add emission lines with [OII] line luminosities between 1038 and 1044 erg.s−1 , while other lines
are generated considering the emission lines luminosity ratios given by McCarthy (1993). We
generate the corresponding u∗ g’r’i’z’ magnitudes, and estimate the photometric redshifts using
ZPEG. In Fig. 4.12 we compare the true redshifts and stellar masses to the estimated ones, while
Tab. ?? shows the statistics of the photometric redshifts and stellar masses. At L[OII] < 1040
erg.s−1 , the influence of emission lines is negligible, and although there are a few outliers that is
comparable to case in which there are no emission lines. As shown in Fig. 4.12, for L[OII] > 1042
erg.s−1 , a systematic redshift offset seems to be introduced for some sources, while there are no
systematic deviation on the stellar mass estimates. However, by using the photometric redshifts
estimates by ZPEG, we estimate the radio power range of the sources in our selected sample (Sec.
4.5) to be log10 (P1.4GHz /W.Hz−1 ) . 26 for ∼ 90% of the sources. Using the [OII] line luminosity
- radio power relation (McCarthy (1993), Best et al. (2005)), this radio power corresponds to
L[OII] . 1040 − 1041 erg.s−1 . We conclude that within the radio power range probed by our survey,
the presence of emission lines should not significantly affect the photometric redshift estimates.
4.7
R ’  
In this section, we compare the distribution of the radio sources’ optical hosts and normal galaxies
in the parameter space.
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
85
Table 4.2: Influence of the presence of emission lines at the level L[OII] on the estimates of photometric
redshifts and stellar masses. q0.5 refers to the median value, while q0.9 refers to the 90% quantile.
L[OII]
[erg.s−1 ]
< 38
40
42
44
q0.5
−0.003
−0.004
0.008
−0.027
∆z
q0.9 − q0.5
0.09
0.09
0.20
0.34
∆ log(M/M⊙ )
q0.5
q0.9 − q0.5
−0.03
0.45
−0.04
0.45
−0.04
0.76
−0.68
0.64
4.7.1 Basic observed properties
4.7.1.1
Optical properties
Fig. 4.13 shows the cumulative distribution of the i-band magnitude and z-r colour for the mean
galaxy population and for the S1 radio selected sample (corrected for contamination as described
in Sec. 4.3.3). The distribution of the mi and mz − mr parameters are very different for the true
identifications and for the mean population. As expected θ(mi ) is well matched by the magnitude
distribution of the identified sample, although there are more mi < 20 galaxies than expected.
Inspecting Fig. 4.3 we see that the error bars on θ(m) are quite large in that magnitude range.
These results confirm that the optical identification has been conducted properly since the identified
hosts are different from the mean population. Specifically, in the left panel, the mi distribution of
the radio sources’ optical hosts is, on average, brighter than the mean population in the optical
catalog by ∼ 2 − 3 magnitudes. In the right panel, it can be seen that the radio sources’ optical
hosts are redder by a ∼ 0.5 magnitudes.
Two effects can contribute to the differences between these distributions: (i) radio sources are
known to be preferentially hosted by massive elliptical galaxies (eg. Best 1998) and (ii) the comoving density of powerful radio sources is known to increase from the local universe to redshifts
z ∼ 2 − 3 by ∼ 2 − 3 order of magnitude (Dunlop & Peacock 1990). These two effects cause the
colour of radio sources to redden as seen in the right panel of Fig. 4.13.
4.7.1.2
Infrared properties
For the S2 sample, Fig. 4.9 shows the distribution of radio sources’ hosts in the [3.6]-[4.5] versus
[5.8]-[8.0] colour-colour space. Inside the region marked by dotted line, Stern et al. (2005) find
7% of normal galaxies, ∼ 40% of the narrow-line AGN and ∼ 90% of the broad-line AGN.
In order to compare the distribution of the radio sources’ optical hosts in this diagram to non
radio loud objects, we select a random sample of infrared sources in the SWIRE DR2 catalog
with which we associate the u∗ g’r’i’z’ optical objects closer than 1.5′′ (see Sec. 4.3.5). On this
combined optical and infrared sample, we apply the same basic selection criteria described in
Sec. 4.5.2 corresponding to a magnitude selection 18 < i < 24. Retaining the objects that are
detected in all the IRAC bands, we are left with 300 and 37 objects classified as N/NL and BL
types respectively. Out of the BL and N/NL objects, 30 (81 ± 19%) and 36 (12 ± 2%) lie in the
broad-line AGN area defined in Stern et al. (2005). These estimates are slightly different to the
86
Host galaxies and environment of active galactic nuclei
Figure 4.13: The top panel shows the i-band magnitude distribution for the true radio source optical identifications and for the mean galaxy population in the optical catalog. The overplotted grey line shows the
function θ(m) that has been used to the likelihood ratios and reliability levels. The bottom panel shows the
mz − mr colours for these two samples.
fraction given by Stern et al. (2005). However, differences are expected as their optical magnitude
selection criterion R< 20 is different from ours (i > 18, corresponding to R& 19). Our samples
populating the [3.6]-[4.5] versus [5.8]-[8.0] diagram have little optical overlap. Specifically we
expect to select galaxies having higher redshifts on average.
The statistics of the radio sources’ hosts classified as BL-type is in agreement with Stern et al.
(2005) with 1 object over 10 (90%) lying in the broad-line AGN region. Out of the 38 N/NL-type
Radio-loud AGN in the XMM-LSS field: optical identification and sample selection
87
radio source hosts, 8 are in the broad-line AGN region, corresponding to a fraction of ∼ 21 ± 8%,
which is more than the 12 ± 2% found above for the normal galaxy population. This indicates
that the mid-infrared spectrum of the radio selected population may be different from the normal
galaxy population.
4.7.2 ZPEG outputs
We use the S1 sample selected as described in Sec. 4.5. Contamination from Type-1 AGN has
been removed as discussed in Sec. 4.5.2. The normal galaxy comparison sample is built from a
random sample of normal galaxies, to which we apply the basic cuts outlined in Sec. 4.5.1.
Since the SED templates used in ZPEG are defined using a common stellar population synthesis scheme, the observed output templates synthesised for different scenarios can overlap. For
example, a young E and an old S0 look similar. Instead of relating template type to galaxy type,
following Sullivan et al. (2006) we classify the galaxies into three groups on the basis of their
specific star formation rate (ie the SFR per unit stellar mass). The specific SFR (sSFR) is a measure of how quickly the stars will be formed in a galaxy. Starburst galaxies will have high sSFR,
whereas passively elliptical galaxies will have a zero sSFR. The first category contains the passively evolving galaxies with sSFR∼ 0 (Class A), and basically contains the early type galaxies
of the Hubble classification. The second group is defined to comprise late type galaxies having
an old evolved stellar population as well as young stars. We set the criterion on the sSFR as
−12 ≤ log(sS FR/[yr−1 ]) ≤ −9.5 (Class B). The third and final class has −9.5 ≤ log(sS FR/[yr−1 ]),
and contains galaxies actively forming stars, such as dwarf and starburst galaxies (Class C).
The upper panel of Fig. 4.14 shows the distribution of the parameters derived with ZPEG for
the radio source optical hosts and for the normal galaxies, for the three different sSFR0.5 classes.
The mean galaxy population is composed of ∼ 16% of Class A (passively evolving galaxies),
∼ 21% of Class B and ∼ 63% of class C (actively evolving galaxies).
According to the results from the previous section, the radio source host galaxy population is
largely biased towards passively evolving systems: ∼ 61% are class A, ∼ 22% are class B and
∼ 17% are class C. Also, inside class B for instance, the radio loud galaxy population is biased
towards lower sSFR than the average population.
In all the classes, the redshift distribution of the radio sources’ hosts is found to be biased
toward higher redshifts on average, than the normal population. The shape of the stellar mass
distribution of all radio sources’ hosts is similar in all A, B, and C classes while being significantly
different to the normal galaxy population. Consistently with previous studies, the radio sources
tend to be found in galaxies more massive than the average by a mean factor of ∼ 5 − 10, with
stellar masses between ∼ 1010 and ∼ 1011.5 M⊙ . This confirms the validity of the stellar synthesis
code used for our sample.
4.8
C
In this paper we have carried out the identification of 621 low frequency radio sources (Tasse et al.
2006) overlapping with the CFHTLS-W1 field (T02/T03 release). A proper use of the likelihood
ratio method (Sutherland & Saunders 1992) and a good control over both the optical and radio
88
Host galaxies and environment of active galactic nuclei
Figure 4.14: Upper panel: The specific star formation rate distribution of radio source identified hosts
(dashed-dotted line), and the same distribution for the normal galaxy population (thick solid line). We have
classified the population in three groups (Class A, B and C, see Sec. 4.7) on the basis of their sSFR0.5 (specific star formation rate). ZPEG does not give sSFR0.5 estimates below sSFR0.5 ∼ 10−11 yr−1 . The plots
showing the redshift and mass distributions (middle and bottom panel), are normalised in each class to the
fraction of galaxies in that class. In order to compare the shape of the distributions, the grey line shows the
average galaxy population whose fraction is scaled to the radio emitting population. The radio emitter hosts
are largely biased towards low sSFR. The middle panel shows the redshift distribution for the three classes of
galaxies. The radio sources’ hosts seem to be located in average at higher redshifts than the normal galaxies.
The mass distribution shows the radio sources’ hosts are largely biased towards higher stellar mass systems.
References
89
catalog quantities, allow us to derive estimates of probability of association with a given optical
candidate. About 75% of the radio sources have been associated with an optical counterpart,
which is a result consistent with both our Monte-Carlo estimate and previous results from other
surveys. Using the infrared SWIRE survey catalogs, we have associated the radio sources’ optical
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that the optical identification has been conducted properly.
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of ∼ 5 − 10. These results are all consistent with previous studies of radio sources’ hosts in low
frequency radio surveys (Best et al. 1998; McLure & Dunlop 2000).
In the near future, we will study the influence and evolution of both the intrinsic and environmental host galaxy properties on the radio source fundamental properties.
A
The authors have made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the
Jet Propulsion Laboratory, Caltech, under contract with the National Aeronautics and Space administration.
The optical images were obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA,
at the CFHT which is operated by the National Research Council (NRC) of Canada, the Institut National
des Sciences de l’Univers of the Centre National de la Recherche Scientifique (CNRS) of France and the
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Astronomy Data Centre as part of the CFHTLS, a collaborative project of NRC and CNRS. The authors
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A
A
C   C 2 
For the class 2 sources (Sec. 4.3.1), no strong a priori can be assumed on the location of the radio
source optical hosts. The position of the optical host is estimated using the flux weighted radio
position of the various Gaussian fitting components. The error bars associated with these positions
are estimated using a priori statistical knowledge of the geometry of the radio lobes with respect
to the radio core.
From the 3CR radio sources (Laing et al. 1983), Best et al. (2003) retrieved the mean asymmetry angle hφi = 6.8◦ , defined as 180◦ minus the angle between the radio core and the two radio
lobes, and the mean separation quotient hQi = hθ1 /θ2 i = 1.42, where θ1 and θ2 are the angular
distances between the core and the two lobes. If φ is small then the angular distance between the
two radio lobes R can be written as R = θ1 + θ2 . The errors on the position of the radio source core
are then:
!
1
1 hQi − 1
σk = (θ1 − θ2 ) = R
(A1)
2
2 hQi + 1
and
hφi
1
(A2)
σ⊥ = R tan
2
2
where σk refers to the error on the position of the radio source host parallel to the radio axis (the
line passing by the two radio lobes), and σ⊥ is the position error orthogonaly to the radio axis.
These estimates relate to the uncertainties on right ascension and declination as:
σ2α,radio = (σk sin PA)2 + (σ⊥ cos PA)2
(A3)
σ2δ,radio = (σk cos PA)2 + (σ⊥ sin PA)2
(A4)
and
where PA is the position angle of the radio axis on the sky, as estimated using the Gaussian fitted
components. For a more detailed discussion, see Best et al. (2003).
92
Host galaxies and environment of active galactic nuclei
B
B1
C   
Class 3 sources
In this section, we give comments on the radio sources classified as Class 3 and Class 4 sources
(see Sec. 4.3.1 for the classes definitions).
J0216.6-0527 (Fig. D1): Although only the eastern lobe has been detected by Tasse et al.
(2007), a 3σ level component is clearly elongated along an east-west axis, suggesting the optical
host to be an mr = 19.2 galaxy.
J0217.6-0513 (Fig. D1): Only the east side 5σ lobe appears in the Tasse et al. (2006) source list,
but radio contours at 3σ suggest the source is extended in the east-west direction. The non-detected
west-side lobe corresponds to a mr = 20.51 object that we chose as the optical identification.
J0217.7-0541 (Fig. D1): The radio source appear to be a small head-tail radio galaxy. The
weighted radio position is ∼ 6 − 8′′ off the bright mi ∼ 21 galaxy that lies within the two radio
lobes. We have identified this object as being the progenitor.
J0218.9-0401 (Fig. D1): Only the eastern lobe appears in the Tasse et al. (2006) 325 MHz
catalog, although there appears to be a second lobe at the 4σ level. We have chosen the mr = 24.21
object lying at the center to be the optical counterpart.
J0219.1-0357 (Fig. D1) The asymmetry angle of this source is obviously negligible, but the
luminosity of the two radio lobes is quite different. This shifts the radio source centroid towards
the south-east of the region. We choose the faint mi ∼ 25.02 source to be the optical host.
J0219.5-0507 (Fig. D1): As for J0216.6-0527, only one of the two lobes present in the data
is detected by Tasse et al. (2007). Two objects might be considered to be possible candidates: the
one overlapping with the lobe on the north-east and the mr = 21.1 object lying in the middle of the
two lobes. Since the radio morphology being likely an FRII, we have considered this later object
to be the host.
J0219.9-0518 (Fig. D1): For this double lobe radio source, the flux weighted radio position is
too far from the obvious very bright mi ∼ 16 host galaxy.
J0220.6-0417 (Fig. D1): This radio source appears to be a double lobe with the second undetected on the east-side of the image. The detected lobe points at an mr ∼ 22 galaxy that we have
considered to be the optical counterpart.
J0223.4-0427 (Fig. D1): As for J0217.7-0541 the flux weighted central position is well off the
obvious mi = 20.6 optical counterpart.
J0224.2-0528 (Fig. D1): Although the identification is obvious, the flux weighted centroid
does not lead to the selection of that candidate. We have chosen the bright mr = 20.8 galaxy to be
the optical host.
J0225.5-0524 (Fig. D1): Only the western radio component appears in the radio catalog, the
eastern lobe being too faint. We consider the mr = 22.51 object at the center to be the optical
counterpart.
J0227.1-0543 (Fig. D1): This elongated east-west lobe points at a mr = 22.58 object that we
have considered to be the progenitor.
J0228.0-0400 (Fig. D1): This object, elongated along the north-south axis, has only one of
its component detected. We have considered the bright object lying at the center to be the optical
References
93
counterpart.
J0228.2-0503 (Fig. D1): Similar to the case of J0219.1-0357 the asymmetry of the flux inside
the two radio lobes makes the flux weighted centroid to be shifted towards the north, whereas the
optical host is obviously the bright candidate lying at the center.
B2 Class 4 sources
J0219.7-0400 (Fig. D2): This object has been reported to be a promising radio halo or radio relic
candidate in Tasse et al. (2006). The overlay with the i-band imaging shows that the diffuse radio
emission corresponds to an overdensity of optical galaxies.
J0217.0-0449 (Fig. D2): This object is one of the most extreme radio sources in the combined
radio sample. Its angular size is almost 2′ and its spectral index is one of the steepest in the sample
′
with α325
1400 = −1.65. The two lobes are separated by almost 1 and no bright optical counterpart
is present in that area. The most likely candidate has an r-band magnitude mr = 24.5 and a
+0.04
photometric redshift estimate of z p = 0.88−0.08
, which would make the physical diameter at the
radio source location to be D p ∼ 0.92 Mpc. We suggest that this radio source might correspond to
the latest stage of the evolution of a radio source, when no more high energy electrons are injected
into the IGM.
94
Host galaxies and environment of active galactic nuclei
C
T
Table C1: The 15 objects for which the Gaussian fitting componants where splitted and renamed. Positions
appearing in columns 3 and 4 are the flux density weighted ones.
Original name
J0215.0-0458
J0219.5-0539
J0224.4-0425
J0216.5-0447
J0218.0-0346
J0219.4-0404
J0220.8-0510
J0227.5-0411
J0228.1-0408
J0228.4-0435
J0229.1-0507
J0221.5-0402
J0224.3-0505
J0226.1-0459
J0229.0-0504
New name
J0215.0-0458
J0215.0-0457
J0219.5-0539
J0219.4-0539
J0224.4-0425
J0224.3-0425
J0216.6-0447a
J0216.6-0447b
J0216.5-0447
J0218.0-0346a
J0218.0-0346b
J0219.4-0404
J0219.4-0403
J0219.5-0403
J0220.8-0510
J0220.8-0509
J0227.6-0411
J0227.5-0411
J0228.2-0408
J0228.1-0408
J0228.4-0434
J0228.4-0435
J0229.1-0507a
J0229.1-0507b
J0221.5-0402a
J0221.5-0402b
J0224.3-0505
J0224.3-0506
J0226.1-0459a
J0226.1-0459b
J0229.0-0505
J0229.0-0504
RA
02 14 59.04
02 14 58.92
02 19 28.22
02 19 25.20
02 24 21.45
02 24 20.47
02 16 35.06
02 16 37.30
02 16 32.23
02 17 57.03
02 17 57.51
02 19 25.75
02 19 24.98
02 19 27.49
02 20 50.67
02 20 50.98
02 27 35.55
02 27 30.45
02 28 11.29
02 28 08.72
02 28 25.38
02 28 22.18
02 29 08.64
02 29 05.75
02 21 30.20
02 21 27.44
02 24 19.99
02 24 18.19
02 26 04.56
02 26 03.65
02 28 58.57
02 28 57.44
DEC
-04 58 11.32
-04 57 41.24
-05 39 48.21
-05 39 11.99
-04 25 48.22
-04 25 42.94
-04 47 01.11
-04 47 36.35
-04 47 41.59
-03 46 02.64
-03 46 48.95
-04 04 18.15
-04 03 52.49
-04 03 44.90
-05 10 17.85
-05 09 59.93
-04 11 22.83
-04 11 16.20
-04 08 42.76
-04 08 49.35
-04 34 39.56
-04 35 04.12
-05 07 24.59
-05 07 39.30
-04 02 28.39
-04 02 33.69
-05 05 25.03
-05 06 04.19
-04 59 32.41
-04 59 02.42
-05 05 21.97
-05 04 48.92
Name
J0214.9−0451
J0215.0−0506
J0215.2−0449
J0215.4−0344
J0215.6−0344
J0215.8−0433
J0215.9−0442
J0216.1−0507
J0216.1−0446
J0216.6−0454
J0216.6−0447a
J0216.8−0427
J0217.0−0516a
J0217.1−0430
J0217.7−0541
J0217.8−0541
J0217.9−0512
J0218.0−0346a
J0218.0−0344
J0218.1−0538
J0218.2−0459
J0218.4−0516
J0218.4−0542
J0218.4−0525
J0218.9−0509
J0218.9−0401
J0219.0−0355
J0219.1−0459
J0219.3−0552
J0219.4−0539
J0219.4−0404
J0219.5−0403
J0219.7−0448
J0219.8−0437
J0219.9−0444
J0220.2−0404
J0220.3−0346
J0220.3−0557
J0220.4−0448
J0220.5−0422
J0220.5−0450
J0220.6−0409
J0220.6−0408
J0220.8−0510
J0221.3−0457
J0221.3−0344
J0221.4−0405
J0221.4−0424
J0221.5−0504
J0221.6−0409
J0221.7−0413
J0221.7−0404
J0221.9−0505
J0221.9−0407
J0222.0−0550
RA
02 14 54.91
02 15 02.06
02 15 11.20
02 15 22.92
02 15 36.20
02 15 45.67
02 15 51.15
02 16 03.60
02 16 08.36
02 16 34.80
02 16 35.09
02 16 49.55
02 17 02.66
02 17 03.20
02 17 41.02
02 17 46.99
02 17 54.09
02 17 57.30
02 18 01.20
02 18 03.34
02 18 09.46
02 18 22.45
02 18 22.64
02 18 23.50
02 18 51.22
02 18 55.07
02 19 00.60
02 19 06.65
02 19 18.31
02 19 25.49
02 19 25.68
02 19 27.50
02 19 44.53
02 19 50.63
02 19 51.63
02 20 10.58
02 20 16.93
02 20 18.80
02 20 24.81
02 20 27.86
02 20 32.09
02 20 34.84
02 20 36.06
02 20 50.62
02 21 18.41
02 21 19.41
02 21 21.38
02 21 23.94
02 21 27.44
02 21 33.28
02 21 43.04
02 21 43.71
02 21 51.50
02 21 56.50
02 22 00.85
Optical Properties
DEC
u
g
−04 51 27.26 26.16 24.82
−05 06 00.32 > 25.30 23.94
−04 49 38.57 22.70 21.66
−03 44 40.71 26.33 23.75
−03 44 22.79 24.82 24.20
−04 33 32.96 24.54 23.77
−04 42 26.18 > 25.30 26.39
−05 07 56.24 22.97 22.57
−04 46 43.86 23.95 22.43
−04 54 33.27 22.30 20.88
−04 47 00.69 25.07 24.16
−04 27 37.98 24.63 22.04
−05 16 18.44 > 25.20 24.91
−04 30 40.35 > 25.60 24.04
−05 41 50.22 26.38 24.33
−05 41 36.39 22.99 22.14
−05 12 49.83 22.96 21.75
−03 46 00.73 22.42 21.54
−03 43 57.27 24.42 22.60
−05 38 29.58 26.13 25.24
−04 59 45.79 22.40 22.15
−05 16 48.65 24.94 22.23
−05 42 14.57 > 25.20 24.06
−05 25 00.54 26.60 22.30
−05 09 08.61 27.62 23.79
−04 01 34.44 > 25.60 25.77
−03 55 56.57 24.55 24.36
−04 59 01.88 23.08 21.27
−05 52 44.51 25.88 23.69
−05 39 11.45 24.24 23.63
−04 04 20.46 > 25.60 23.50
−04 03 43.27 25.79 23.14
−04 48 49.09 > 25.20 26.27
−04 37 18.58 21.87 20.44
−04 44 37.64 > 24.70 24.30
−04 04 20.04 > 25.40 24.59
−03 46 04.26 > 25.40 24.67
−05 57 31.25 25.43 25.02
−04 48 13.91 24.55 24.91
−04 22 50.88 22.06 20.35
−04 50 04.74 23.21 23.08
−04 09 52.66 25.06 23.84
−04 08 06.81 25.45 24.67
−05 10 18.50 24.59 24.75
−04 57 22.33 26.79 26.48
−03 44 41.86 23.19 21.22
−04 05 34.89 > 25.40 24.61
−04 24 16.34 24.98 24.26
−05 04 01.05 24.57 23.46
−04 09 01.69 > 25.40 24.37
−04 13 43.65 24.04 24.06
−04 04 27.55 24.85 24.47
−05 05 37.26 > 24.70 24.42
−04 07 57.82 21.00 20.66
−05 51 00.43 23.91 23.47
r
23.66
22.16
21.00
22.24
23.88
22.76
24.31
21.97
21.04
19.72
> 24.90
20.52
23.43
22.66
22.43
20.91
20.36
20.70
21.22
24.96
21.65
20.69
22.32
20.75
22.36
24.30
23.58
19.75
22.16
23.07
21.60
21.67
24.86
19.50
25.07
22.80
24.22
24.37
> 25.00
19.16
22.74
22.30
23.17
22.88
24.51
28.75
24.21
23.23
22.11
23.50
23.37
23.73
23.27
20.35
22.29
i
z
23.02 21.68
21.38 20.65
20.74 20.52
20.97 20.40
22.35 21.38
21.77 21.19
23.13 22.38
21.12 20.49
19.97 19.42
19.26 18.92
22.62 22.28
19.43 19.07
22.36 21.77
21.12 20.58
21.14 20.42
19.92 19.49
19.71 19.53
19.98 19.55
19.98 19.41
23.73 24.33
21.08 20.90
19.79 19.31
20.92 20.28
19.58 22.02
21.37 20.84
23.40 23.39
22.90 22.33
18.91 18.51
20.94 20.46
22.29 22.00
20.24 19.64
20.44 20.01
23.40 22.78
19.02 18.71
23.45 23.54
21.50 20.90
22.86 23.23
23.99 23.56
22.77 21.93
18.62 18.35
21.98 21.49
20.79 20.28
22.00 21.28
21.46 20.60
23.84 23.49
18.92 18.52
23.65 > 24.00
22.65 21.74
20.93 20.40
22.87 22.59
21.86 21.80
23.47 23.63
22.77 22.52
19.40 20.53
21.29 20.98
zp
1.18
0.99
0.15
0.78
1.10
0.84
0.85
1.01
0.90
0.15
0.72
0.63
0.94
0.73
0.83
0.69
0.53
0.56
0.80
0.76
0.71
0.50
0.78
0.15
0.92
0.67
0.99
0.49
0.72
0.72
0.78
0.71
0.78
0.15
0.89
0.79
0.79
0.99
1.06
0.19
1.02
0.72
0.87
0.88
0.40
0.48
1.07
1.16
0.90
0.58
0.82
0.56
0.47
0.73
0.67
ZPEG
log(M)
10.64
10.92
9.13
11.31
10.57
10.96
10.49
10.85
11.23
10.29
10.28
11.39
10.35
11.25
11.35
11.38
10.64
11.09
11.54
8.97
10.20
11.35
11.46
7.20
10.64
9.27
10.36
11.56
11.19
10.15
11.66
11.16
10.46
10.31
9.19
10.99
9.33
9.65
10.31
10.77
10.10
11.42
10.90
11.51
9.45
11.53
10.03
10.48
10.84
9.84
9.97
8.82
9.48
10.55
10.62
log(S )
−∞
−∞
−9.89
−∞
−∞
−10.85
−∞
−9.58
−∞
−∞
−9.87
−∞
−∞
−∞
−∞
−10.85
−∞
−9.99
−∞
−8.92
−9.27
−∞
−∞
−8.74
−∞
−∞
−9.67
−∞
−∞
−9.48
−∞
−∞
−∞
−∞
−9.04
−∞
−8.92
−9.27
−∞
−∞
−8.92
−∞
−∞
−∞
−∞
−∞
−9.35
−8.90
−∞
−9.73
−8.90
−8.61
−9.89
−8.93
−10.73
3.6µm
NC
NC
NC
0.00
19.60
19.23
20.23
19.16
18.55
18.59
> 21.03
18.13
20.34
0.00
18.76
18.41
19.06
19.89
0.00
> 21.03
19.10
18.67
18.64
18.11
20.16
> 21.03
19.70
18.11
19.28
> 21.03
18.27
19.01
> 21.03
17.84
> 21.03
19.52
20.42
> 21.03
> 21.03
18.58
> 21.03
18.72
19.47
18.53
> 21.03
18.38
> 21.03
> 21.03
19.18
> 21.03
19.51
> 21.03
19.59
18.37
19.11
Infrared Properties
4.5µm
5.8µm
NC
NC
NC
NC
NC
NC
0.00
0.00
19.63
> 19.84
19.44
> 19.84
20.48
> 19.84
19.39
> 19.84
18.92
19.03
18.67
19.21
> 20.96 > 19.84
18.62
19.11
20.70
> 19.84
0.00
0.00
19.19
19.10
18.93
19.38
19.48
> 19.84
20.22
> 19.84
0.00
0.00
> 20.96 > 19.84
18.59
17.71
19.02
19.19
19.15
> 19.84
18.56
18.78
20.55
> 19.84
> 20.96 > 19.84
19.65
> 19.84
18.46
18.71
19.77
> 19.84
> 20.96 > 19.84
18.85
19.02
19.52
> 19.84
> 20.96 > 19.84
17.65
17.23
> 20.96 > 19.84
19.95
> 19.84
20.41
> 19.84
> 20.96 > 19.84
> 20.96 > 19.84
18.79
19.54
> 20.96 > 19.84
19.09
> 19.84
19.87
> 19.84
18.85
19.21
> 20.96 > 19.84
18.61
19.14
> 20.96 > 19.84
> 20.96 > 19.84
19.55
> 19.84
> 20.96 > 19.84
19.71
19.39
> 20.96 > 19.84
19.45
19.21
17.68
16.88
19.32
19.88
8.0µm
NC
NC
NC
0.00
> 19.53
19.61
> 19.53
> 19.53
19.16
17.94
> 19.53
19.42
> 19.53
0.00
> 19.53
19.32
> 19.53
> 19.53
0.00
> 19.53
16.52
> 19.53
> 19.53
19.25
> 19.53
> 19.53
> 19.53
19.23
> 19.53
> 19.53
> 19.53
> 19.53
> 19.53
15.83
> 19.53
> 19.53
> 19.53
> 19.53
> 19.53
19.03
> 19.53
> 19.53
> 19.53
> 19.53
> 19.53
> 19.53
> 19.53
> 19.53
> 19.53
> 19.53
> 19.53
> 19.53
19.48
16.18
19.13
74
< 126.7
< 121.1
< 128.2
< 129.2
< 130.1
< 118.5
< 111.6
< 115.5
< 109.5
< 119.0
< 118.5
< 124.5
< 121.5
< 126.5
535.9
< 118.7
< 117.6
< 128.4
< 122.4
< 115.9
< 127.8
< 126.7
< 123.4
< 119.0
< 126.4
< 126.2
< 124.4
< 124.1
305.4
< 135.4
< 123.7
< 124.0
< 118.7
< 121.7
< 119.0
< 117.5
< 127.4
< 134.6
< 119.6
< 126.2
< 118.9
< 129.4
< 125.5
< 114.6
< 121.6
< 125.5
< 133.1
< 123.9
< 130.7
< 123.7
2405.6
< 120.1
< 124.5
< 115.7
< 125.1
Radio properties
240
325
610
log(P1.4 ) log(D p )
< 15.0
6.4
NC
25.0
.
134.1 116.6
NC
26.2
2.1
25.5
9.2
NC
23.6
.
28.4
16.0
NC
25.1
.
61.4
45.0
NC
25.9
1.9
< 11.5
4.5
NC
24.5
.
20.7
13.3
NC
25.3
1.6
48.7
45.7
NC
25.9
1.8
38.9
28.8
NC
25.7
1.6
< 10.1
4.4
2.7
23.2
.
70.8
49.3
32.0
25.6
2.0
< 12.2
4.4
< 2.0
24.4
.
< 13.1
4.6
2.7
24.5
.
< 11.0
4.8
< 1.7
24.4
.
212.4 171.2 168.2
26.3
2.5
39.9
32.8
19.7
25.3
1.5
19.5
15.2
10.2
24.6
.
15.7
11.0
8.6
25.0
1.6
14.2
11.4
7.4
25.0
1.4
26.1
27.0
20.6
25.4
1.4
< 10.8
6.2
3.5
24.5
.
< 13.0
5.4
4.7
24.3
.
33.1
32.8
17.3
25.6
1.9
< 13.8
5.7
6.8
23.7
.
71.8
53.9
40.1
25.8
.
9.9
10.5
5.6
24.7
1.9
58.2
32.9
27.3
25.8
1.4
< 10.9
4.0
< 1.4
24.4
.
203.0 118.9
71.0
25.9
2.3
97.7
76.7
44.2
25.7
.
12.3
11.7
6.4
25.1
1.7
NC
7.4
5.9
24.8
.
14.2
16.4
13.8
25.2
1.9
< 9.4
3.3
2.2
23.1
.
< 12.5
6.2
2.7
24.6
.
< 7.9
5.0
3.5
24.5
.
< 6.8
5.4
4.0
24.7
.
< 13.9
7.5
< 3.5
25.1
.
< 9.6
3.2
< 1.3
24.5
.
< 8.0
5.2
4.2
23.2
.
< 8.4
5.5
3.7
24.8
.
< 7.4
3.9
2.5
24.2
.
< 7.4
6.9
4.5
24.8
1.3
15.1
8.7
5.2
25.2
1.6
< 13.0 16.3
24.5
24.8
.
24.8
15.8
13.9
24.9
.
< 6.9
4.9
< 1.7
24.8
.
< 8.2
6.8
8.5
25.4
.
< 12.5
6.9
3.9
24.8
.
25.9
23.2
18.5
25.1
1.3
1595.6 1269.7 1127.9
27.2
1.8
< 7.3
7.0
< 1.8
24.4
.
13.4
7.9
12.4
24.5
.
16.1
10.1
10.9
24.9
.
11.2
8.7
NC
24.8
.
References
Table C2: The optical counterparts properties.
95
Optical Properties
DEC
u
g
−04 48 34.84 > 24.70 23.15
−04 02 34.55 21.67 20.08
−03 48 13.78 25.09 24.94
−04 32 13.84 24.11 24.02
−04 16 46.47 23.96 23.26
−05 25 30.85 27.14 25.03
−04 24 48.05 23.91 23.35
−04 07 01.71 22.13 20.21
−04 09 35.81 26.30 24.72
−04 58 43.77 > 24.70 24.51
−05 41 48.18 24.43 24.17
−04 27 24.44 25.16 23.34
−04 01 45.30 25.20 24.63
−05 51 14.36 23.92 23.06
−05 31 05.46 24.64 23.59
−03 50 16.66 24.96 25.04
−03 55 57.42 22.00 20.83
−05 28 19.37 25.08 23.17
−04 49 52.08 > 25.00 21.30
−05 04 22.08 24.26 24.56
−03 54 37.58 > 25.60 25.84
−03 47 13.80 25.09 23.50
−03 57 41.84 22.44 21.38
−04 31 40.47 24.80 23.21
−04 35 36.21 25.40 24.95
−05 36 47.97 23.29 22.23
−05 19 59.33 24.47 23.84
−04 06 54.18 24.32 23.60
−04 01 02.30 24.75 23.25
−05 09 49.15 21.94 20.16
−04 24 25.79 21.73 20.92
−04 17 53.43 21.98 21.57
−05 24 57.59 26.42 24.10
−04 17 56.43 > 25.60 24.17
−04 15 27.92 25.33 24.64
−05 00 18.55 24.02 24.28
−04 28 49.44 26.90 25.90
−05 12 07.96 21.81 20.00
−04 16 32.07 23.33 22.25
−04 03 19.38 20.46 19.49
−04 25 35.76 26.19 25.56
−05 55 04.43 24.81 24.18
−04 11 26.58 25.33 25.31
−04 32 28.13 25.68 24.55
−03 51 28.77 21.48 19.87
−05 55 46.48 24.11 24.03
−05 49 59.24 22.18 20.33
−04 33 50.74 21.92 21.25
−05 12 42.88 24.68 25.88
−03 43 54.77
NC
24.61
−04 08 49.59
NC
24.75
−05 02 59.55
NC
20.18
−04 33 30.89
NC
21.81
−04 53 36.54
NC
23.09
−04 34 40.76
NC
24.92
−04 17 51.46
NC
24.84
−04 46 08.19
NC
23.01
r
22.11
18.89
24.36
23.52
22.58
24.65
22.22
18.85
22.96
24.30
23.78
21.81
23.64
21.83
22.55
24.30
19.70
21.99
19.91
24.20
23.27
21.95
20.24
21.78
23.58
21.17
22.67
22.91
21.97
18.80
20.25
21.09
22.52
22.55
23.51
23.73
24.29
18.68
20.90
18.64
24.70
23.84
24.57
22.87
18.99
23.31
19.03
20.35
24.06
24.18
24.07
18.71
20.28
21.98
24.28
24.36
21.83
i
z
20.80 20.48
18.27 17.90
23.88 22.60
22.52 22.12
21.81 21.44
23.72 23.53
21.42 21.06
18.26 17.92
21.82 20.97
23.72 > 24.10
23.13 > 24.20
20.65 20.25
22.73 21.67
20.59 19.97
21.35 20.77
23.09 22.11
19.17 18.84
20.81 20.33
19.06 18.67
23.32 24.31
22.02 21.34
20.70 20.03
19.38 19.07
20.46 19.89
22.42 21.49
20.20 19.73
21.36 20.87
22.62 22.28
20.75 20.30
18.23 17.90
19.96 19.75
20.45 20.16
21.12 20.66
21.18 20.57
22.35 21.74
22.95 21.79
23.13 22.33
18.13 17.87
20.25 19.99
18.31 17.93
23.57 22.67
22.79 22.11
23.10 22.12
21.85 21.41
18.52 18.26
21.97 21.36
18.43 18.05
19.76 19.45
22.88 22.67
23.78
NC
23.47
NC
18.09
NC
19.35
NC
21.36
NC
23.39
NC
23.84
NC
20.86
NC
zp
0.84
0.45
1.19
0.96
0.70
0.81
0.60
0.45
1.17
0.86
1.12
0.63
1.15
0.95
0.96
1.09
0.44
0.70
0.50
0.78
0.82
0.83
0.58
0.78
1.10
0.69
0.89
0.28
0.72
0.24
0.24
0.72
0.68
0.78
0.97
1.16
0.99
0.22
0.46
0.16
1.08
1.05
1.08
0.77
0.11
0.99
0.45
0.55
0.79
0.67
0.65
0.39
0.51
0.60
0.76
0.70
1.10
ZPEG
log(M)
10.62
11.50
9.95
9.83
10.43
9.39
10.45
11.26
11.30
9.03
9.91
10.96
10.53
11.08
10.66
10.17
10.98
11.06
11.44
8.98
10.97
11.54
11.20
11.51
10.68
11.28
10.57
8.99
11.05
11.15
9.92
10.55
11.19
11.21
10.28
10.24
10.70
11.09
10.72
10.55
10.04
10.03
10.23
10.47
10.22
10.33
11.24
11.05
9.69
8.72
9.33
11.51
11.25
9.99
9.97
8.94
11.24
log(S )
−∞
−10.85
−8.91
−8.92
−9.60
−9.01
−10.25
−∞
−∞
−8.63
−8.92
−∞
−∞
−∞
−∞
−8.90
−10.25
−∞
−∞
−8.93
−∞
−∞
−10.73
−∞
−∞
−10.85
−∞
−9.48
−∞
−∞
−9.51
−9.15
−∞
−∞
−∞
−8.91
−∞
−∞
−10.85
−10.35
−∞
−8.91
−∞
−∞
−∞
−8.90
−∞
−9.70
−∞
−8.86
−9.26
−∞
−∞
−9.58
−9.73
−9.14
−∞
3.6µm
18.30
> 21.03
> 21.03
19.76
18.86
21.01
20.36
18.23
19.04
> 21.03
> 21.03
19.13
18.26
NC
19.14
19.60
18.41
19.30
18.28
19.81
19.26
18.52
17.90
18.61
19.98
18.51
19.47
21.15
18.81
18.12
19.35
18.47
19.43
19.11
19.92
19.60
> 21.03
18.06
19.18
17.57
19.26
NC
18.98
19.78
NC
NC
NC
18.45
19.69
NC
NC
17.72
NC
NC
NC
NC
NC
Infrared Properties
4.5µm
5.8µm
18.63
18.84
> 20.96 > 19.84
> 20.96 > 19.84
19.89
> 19.84
18.13
17.14
20.84
> 19.84
20.49
> 19.84
18.49
19.06
19.39
19.69
> 20.96 > 19.84
> 20.96 > 19.84
19.47
19.12
17.69
17.29
NC
NC
19.52
19.37
19.66
> 19.84
18.56
18.54
19.72
19.44
18.60
18.86
19.40
18.72
19.65
> 19.84
19.05
19.05
18.32
18.27
19.09
19.32
20.35
> 19.84
18.71
18.60
19.84
> 19.84
21.17
> 19.84
19.27
19.31
18.31
18.91
19.55
> 19.84
18.70
18.76
20.00
> 19.84
19.64
> 19.84
20.09
> 19.84
19.68
> 19.84
> 20.96 > 19.84
18.27
18.70
19.25
18.44
17.42
17.18
19.20
19.70
NC
NC
18.99
19.21
20.30
> 19.84
NC
NC
NC
NC
NC
NC
18.82
18.76
19.97
> 19.84
NC
NC
NC
NC
17.94
18.28
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
8.0µm
19.13
> 19.53
> 19.53
> 19.53
16.02
> 19.53
> 19.53
19.42
> 19.53
> 19.53
> 19.53
19.21
16.69
NC
20.00
> 19.53
18.86
18.86
> 19.53
18.84
19.87
> 19.53
18.31
> 19.53
> 19.53
18.53
> 19.53
> 19.53
> 19.53
19.17
18.84
18.81
> 19.53
> 19.53
> 19.53
19.86
> 19.53
18.95
17.46
16.00
> 19.53
NC
19.54
> 19.53
NC
NC
NC
18.48
> 19.53
NC
NC
18.59
NC
NC
NC
NC
NC
74
< 115.8
< 121.6
< 132.0
< 117.1
< 126.3
< 109.6
189.9
< 121.9
< 127.5
< 116.3
< 117.2
< 117.3
< 127.0
< 122.9
< 118.5
< 117.9
< 113.7
1309.1
< 129.4
< 123.5
< 112.3
< 112.1
< 112.7
< 108.6
< 118.6
1464.9
< 121.7
< 110.9
< 109.1
< 116.9
< 111.0
< 108.1
< 119.2
< 111.4
< 111.2
< 113.5
< 109.5
< 118.9
< 114.8
< 110.2
301.8
< 123.7
< 115.1
< 113.3
< 110.7
2462.8
< 122.4
< 115.7
< 114.1
< 126.7
< 117.5
< 117.7
< 119.3
< 118.1
< 121.3
< 116.3
< 120.0
Radio properties
240
325
610
log(P1.4 ) log(D p )
< 7.8
5.7
5.1
24.8
.
< 5.8
3.4
8.9
24.2
.
53.7
32.6
30.0
26.0
.
< 6.9
5.6
3.6
25.0
.
11.8
9.7
8.7
24.8
.
< 10.5
7.0
< 1.7
24.7
.
91.4
70.0
49.7
25.4
2.0
8.5
4.2
5.4
24.3
.
< 6.5
6.6
6.2
25.3
.
< 9.7
5.7
3.9
24.7
.
< 10.4
5.1
3.9
25.1
.
71.2
46.0
35.2
25.4
2.4
< 5.9
3.5
4.1
25.1
.
201.5 129.9 164.9
26.5
1.1
< 10.4
8.1
6.3
25.1
.
31.3
18.7
13.2
25.6
.
8.1
4.5
4.4
24.4
.
765.3 494.5 308.9
26.5
2.7
27.4
24.2
17.2
25.0
2.1
29.3
20.9
9.6
25.1
.
< 6.9
6.0
5.5
25.0
.
19.8
15.4
8.9
25.2
1.6
< 6.9
3.2
1.7
23.9
.
< 6.5
5.0
2.3
24.4
.
< 6.9
5.0
3.5
25.0
.
876.3 596.3 429.5
26.6
1.4
13.2
9.0
7.7
25.1
.
< 6.9
2.9
3.1
23.4
.
30.7
24.0
15.4
25.2
.
< 8.0
3.6
2.4
23.5
.
< 6.4
3.9
2.0
23.2
.
< 6.5
2.5
2.1
24.0
.
< 8.8
3.8
2.7
24.2
.
11.2
6.0
3.4
24.8
.
8.7
5.5
5.3
25.0
.
11.3
14.0
3.3
25.4
.
29.9
23.5
16.5
25.5
2.3
< 7.0
3.5
1.9
23.5
.
< 6.7
2.8
< 1.1
23.7
.
9.5
8.4
5.0
23.3
.
171.6 115.1
72.0
26.3
1.9
< 17.1
5.1
NC
24.8
.
< 7.5
3.7
< 1.2
24.6
.
< 7.0
3.7
2.0
24.5
.
< 11.5
5.8
< 1.2
22.7
1.3
NC
1017.2
NC
27.1
2.2
NC
6.3
< 2.5
24.3
.
10.3
6.4
3.9
24.3
.
22.8
3.6
3.5
24.8
.
23.5
19.3
12.1
25.0
1.6
14.1
11.9
7.9
24.8
.
53.6
50.0
22.0
25.0
1.9
< 6.6
5.3
3.5
24.1
.
< 6.5
3.8
< 1.2
24.1
.
19.9
12.5
9.2
25.1
.
11.2
6.9
3.7
24.6
.
32.0
21.2
11.0
25.6
1.5
Host galaxies and environment of active galactic nuclei
RA
02 22 03.99
02 22 44.33
02 22 47.63
02 22 51.12
02 22 52.09
02 22 54.11
02 22 56.39
02 22 58.03
02 23 01.11
02 23 16.66
02 23 22.82
02 23 25.30
02 23 32.33
02 23 48.09
02 23 49.80
02 24 10.79
02 24 12.15
02 24 13.94
02 24 28.31
02 24 29.31
02 24 32.95
02 24 39.41
02 24 39.68
02 24 50.84
02 24 59.46
02 25 05.11
02 25 05.89
02 25 08.37
02 25 09.15
02 25 09.71
02 25 16.39
02 25 18.00
02 25 27.14
02 25 40.10
02 25 45.80
02 25 53.14
02 25 55.33
02 26 01.35
02 26 06.57
02 26 12.68
02 26 19.93
02 26 21.13
02 26 35.02
02 26 35.82
02 26 50.42
02 26 59.20
02 27 15.25
02 27 22.74
02 27 23.07
02 27 53.52
02 28 08.53
02 28 14.07
02 28 14.15
02 28 22.39
02 28 25.36
02 28 26.65
02 28 31.66
96
Name
J0222.1−0448
J0222.7−0402
J0222.8−0348
J0222.9−0432
J0222.9−0416
J0222.9−0525
J0222.9−0424
J0223.0−0407
J0223.0−0409
J0223.3−0458
J0223.4−0541
J0223.4−0427
J0223.5−0401
J0223.8−0551
J0223.8−0531
J0224.2−0350
J0224.2−0355
J0224.2−0528
J0224.5−0449
J0224.5−0504
J0224.6−0354
J0224.7−0347
J0224.7−0357
J0224.8−0431
J0225.0−0435
J0225.1−0536
J0225.1−0519
J0225.1−0406
J0225.2−0401
J0225.2−0509
J0225.3−0424
J0225.3−0417
J0225.5−0524
J0225.7−0417
J0225.8−0415
J0225.9−0500
J0225.9−0428
J0226.0−0512
J0226.1−0416
J0226.2−0403
J0226.3−0425
J0226.4−0554
J0226.6−0411
J0226.6−0432
J0226.8−0351
J0227.0−0555
J0227.3−0549
J0227.4−0433
J0227.4−0512
J0227.9−0343
J0228.1−0408
J0228.2−0503
J0228.2−0433
J0228.4−0453
J0228.4−0434
J0228.4−0417
J0228.5−0446
RA
02 28 38.47
02 28 38.51
02 29 05.01
02 29 07.80
02 29 17.22
02 29 35.64
02 29 39.86
02 30 02.72
02 30 04.47
02 30 07.15
02 30 11.47
02 30 27.33
02 30 27.48
Optical Properties
DEC
u
g
−05 05 12.65 NC 20.74
−04 06 48.28 NC 21.11
−04 21 22.25 NC 21.61
−05 10 14.67 NC 21.51
−04 03 28.37 NC 22.27
−04 40 11.08 NC 22.89
−05 31 53.72 NC 21.76
−03 59 35.12 NC 24.20
−05 12 09.69 NC 23.49
−04 57 16.12 NC 22.64
−04 57 44.03 NC 22.77
−04 42 44.57 NC 22.66
−04 10 36.60 NC 22.58
r
19.22
19.80
20.36
20.43
21.05
21.38
20.49
22.77
22.06
21.16
21.86
21.09
21.74
i
18.58
19.11
19.46
19.89
20.00
20.16
19.62
21.75
20.89
20.19
20.96
19.94
21.43
z
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
zp
0.32
0.63
0.67
0.46
1.04
0.76
0.66
0.59
0.70
0.53
0.69
0.91
0.60
ZPEG
log(M)
11.26
10.86
10.86
10.89
11.45
11.37
10.77
10.37
10.95
10.90
10.66
11.36
9.60
log(S )
−∞
−8.90
−∞
−9.99
−∞
−∞
−∞
−∞
−∞
−∞
−9.89
−∞
−8.93
3.6µm
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
Infrared Properties
4.5µm
5.8µm
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
8.0µm
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
74
240
< 128.6 < 7.0
< 129.3 37.5
< 129.8 32.2
< 131.6 17.0
< 137.7 36.1
< 122.4 < 7.8
< 125.4 < 10.6
< 129.6 < 9.5
< 127.2 36.5
< 125.0 43.6
< 124.9 22.1
< 130.2
9.1
< 132.4 < 11.3
Radio properties
325
610
log(P1.4 ) log(D p )
5.8
3.8
24.3
.
30.0
25.6
25.4
.
21.4
12.7
25.1
.
8.4
3.2
24.3
.
24.3
14.8
25.6
1.4
5.3
NC
24.5
.
5.7
< 2.6
24.7
.
5.5
NC
24.3
.
24.7
NC
25.1
1.3
25.2
NC
25.2
1.8
13.1
NC
25.0
1.6
6.6
NC
25.0
.
13.9
NC
24.6
1.3
References
Name
J0228.6−0505
J0228.6−0406
J0229.1−0421
J0229.1−0510
J0229.3−0403
J0229.6−0440
J0229.7−0531
J0230.0−0359
J0230.1−0512
J0230.1−0457
J0230.2−0457
J0230.5−0442
J0230.5−0410
Description of columns:
1. Radio source Name.
2. Optical counterpart right Ascension (J2000).
3. Optical counterpart declination (J2000).
4. u-band magnitude. Quote ’NC’ means the field has not been observed in that band.
5. g-band magnitude.
6. r-band magnitude.
7. i-band magnitude.
8. z-band magnitude. Quote ’NC’ means the field has not been observed in that band.
9. Photometric redshift as estimated by ZPEG.
10. Stellar mass as estimated by ZPEG in logarithm base-10 scale.
11. Specific SFR as estimated by ZPEG in logarithm base-10 scale.
12. 3.6µmmagnitude (AB). Tag ’NC’ (standing for ’Not Covered’) has been used when the location of the object is not overlapping with the SWIRE field. Tag ’NI’ stands for ’Not investigated’, and
is used when the object has no optical counterpart, so we didn’t investigate if either the radio source has an IRAC counterpart. We have used the same notation for the columns bellow.
13. 4.5µmmagnitude (AB).
14. 5.8µmmagnitude (AB).
15. 8.0µmmagnitude (AB).
16. Total flux density of the radio source at 74 MHz in mJy.
17. Total flux density of the radio source at 240 MHz in mJy.
18. Total flux density of the radio source at 325 MHz in mJy.
19. Total flux density of the radio source at 610 MHz in mJy.
20. Radio power in W/Hz in logarithm base-10 scale.
21. Physical diameter in kpc in logarithm base-10 scale.
97
98
Host galaxies and environment of active galactic nuclei
D

Figure D1: The optical identifications in the S2 sample. Greyscale: The i-band image image. Axes are labeled in
arcsecond. In contour: The radio contours drawn for the 325 of 610 MHz maps at levels of 3σlocal ×1, 1.4, 2, 2.8, 4, ....
The local noise σlocal appears in each overlay, and is in units of mJy.beam−1 . The cross indicates the position of
the optical identification. Small crosses indicate the positions of the Gaussian fitting componants appearing in Tasse
et al. (2006) source list, used for the centroı̈d position calculation.
References
99
Figure D1: Continued.
100
Host galaxies and environment of active galactic nuclei
Figure D1: Continued.
References
101
Figure D1: Continued.
102
Host galaxies and environment of active galactic nuclei
Figure D1: Continued.
References
103
Figure D2: Greyscale: The i-band image. In contour: The 325 MHz contours drawn at levels of 3σlocal ×
1, 1.4, 2, 2.8, 4, .... Small crosses indicate the positions of the Gaussian fitting componants appearing in
Tasse et al. (2006) source list, used for the centroı̈d position calculation. The object on the top if the radio
halo/relic candidate reported in Tasse et al. (2006). The objects on the bottom image have a spectral index
α325
1400 = −1.65.
104
Host galaxies and environment of active galactic nuclei
CHAPTER 5
Radio-loud AGN in the XMM-LSS field: a
dichotomy on environment and accretion
mode?
C. Tasse, P. N. Best, H. Röttgering, D. Le Borgne
Submitted
 though the unified scheme of active galactic nuclei (AGN) gives a good
description of the observed properties of radio quiet AGN, it does not explain many features of radio loud AGN. Several authors have argued that optically active and radio loud AGN correspond to different modes of accretion
(“Quasar mode” versus “Radio mode”) that are triggered by different physical
mechanisms.
In this third paper of the series we independently study the internal and
environmental properties of the radio sources’ optical hosts sample described in Tasse et al. (2007). We do this by building a comoving scale dependent overdensity parameter, based on the photometric redshifts probability
functions, and use it to constrain the small (∼ 75 kpc) and large (∼ 450 kpc)
scale environments of radio sources independently from their stellar mass estimates. We compare our results with other surveys, confirming the robustness
of our stellar mass and photometric redshifts estimates. The results of this paper support the picture in which the comoving evolution of radio sources in
the redshift range . 1 is caused by two distinct galaxy populations, where radio loudness is triggered by two different mechanism. The first component of
this population is made of massive elliptical galaxies, lying in galaxy groups
or clusters. Their radio loudness is triggered by the cooling of the hot gas in
their atmosphere. The second population are star forming, low stellar masses
(M . 1011 M⊙ ) systems, and lie in large scale underdensities.
A
106
5.1
Host galaxies and environment of active galactic nuclei
I
Active galactic nuclei (AGN) have regained attention in the last decade since they are thought to
play a major role in the framework of galaxy formation. During their short lifetime, the enormous
amount of energy they produce in the form of ionising radiation or relativistic jets can have a
significant effect on their small-scale (internal) and large scale (external) surroundings. It appears
from semi-analytical models and high resolution numerical simulations that the AGN energetic
feedback is a vital ingredient for reproducing some of the observed features of the Universe, such
as the stellar galaxy mass function (Croton et al. 2005; Best et al. 2006), or the black hole mass
versus bulge mass relationship (Gebhardt et al. 2000; Springel et al. 2005a).
The unified scheme gives a good description of the observed properties of radio-quiet AGN. In
this picture, the nuclear activity is produced by matter accreted onto a super-massive black hole,
with an optically thick dusty torus surrounding it. The most powerful radio sources also follow
the unified scheme, but there is a subset of radio loud AGN (especially at low radio power) for
which the unified scheme does not seem appropriate: these sources lack infrared emission from
the dusty torus (Whysong & Antonucci 2004; Ogle et al. 2006), as well as luminous emission
lines (Hine & Longair 1979; Laing et al. 1994; Jackson & Rawlings 1997) and accretion related
X-ray emission (Hardcastle et al. 2006; Evans et al. 2006). These observations are supported
by recent results from large surveys (Best et al. 2005) indicating that low-luminosity radio-loud
and radio-quiet AGN phenomenon are statistically independent. Many authors have argued that
the low luminosity radio-loud and the optically active AGN correspond to two different accretion
modes (“Radio mode” vs “Quasar mode”). In this picture, the quasar mode is radiatively efficient,
and is caused by accretion of cold gas onto the super-massive black hole, while the radio mode
results from the accretion of hot gas and is radiatively inefficient (see Hardcastle et al. 2007, for
a discussion). As we show in this paper, the nature of the processes that trigger the black hole
activity might be important in giving rise to these two AGN modes.
It has often been proposed that galaxy mergers and interactions both trigger a starburst and
fuel the central super-massive black hole. Although the situation remains controversial for the low
luminosity optically active AGN (Veilleux 2003; Schmitt 2004), observations of large samples of
optically-selected AGN from the Sloan Digital Sky Survey show clear evidence that the luminous
optically active AGN are associated with young stellar populations (Kauffmann et al. 2003). At
the extreme end, this scenario is supported by observations of ultra-luminous infrared galaxies
(ULIRGs, Sanders & Mirabel 1996), that are in general associated with galaxy mergers, and have
bolometric luminosities and luminosity function similar to that of quasars (Sanders et al. 1988a),
while some ULIRGs hide a buried AGN in their nucleus (eg. Sanders et al. 1988b). High resolution
numerical simulations (Springel et al. 2005a,b) have consistently shown that the AGN activity
remains obscured during most of the starburst and AGN activity phase. However, at low redshift,
low-luminosity radio-loud AGN are seen to be preferentially hosted by massive elliptical galaxies,
that tend to be found in richer, cold-gas poor environments, where gas-rich galaxy mergers are less
likely to occur. The cooling of the hot X-ray emitting gas observed in the atmospheres of massive
elliptical galaxies (Mathews & Brighenti 2003) has been proposed as an alternative triggering
process to galaxy mergers. Based on a large sample of radio sources in the SDSS, Best et al.
(2005) argued that the gas cooling rate has the same dependence on stellar mass as the fraction of
low luminosity radio-loud galaxies. This suggests that the gas that has radiatively cooled from the
X-ray emitting atmosphere may trigger the AGN activity.
Radio-loud AGN in the XMM-LSS field: a dichotomy on environment and accretion mode? 107
In this paper, we study the properties of a well-controlled sample of ∼ 110 radio loud AGN
situated at z . 1.2, to put constraints on the triggering mechanisms, and the evolution of the radioloud AGN population. Our results support the picture in which galaxy mergers and gas cooling
from the hot atmosphere of massive ellipticals compete to trigger the quasar and the radio mode
respectively (Hardcastle et al. 2007). The evolution of these two processes through cosmic time
might play an important role in the evolution of the radio luminosity function.
In Section 5.2, we present the sample, and its associated parameters. In Section 5.3, we build
the stellar mass function of normal galaxies and radio sources’ hosts, and derive a radio loud fraction ( fRL ) versus stellar mass relation equivalent to that which has been estimated at low redshift
in the SDSS (Best et al. 2005). In Section 5.4, we construct a scale dependent overdensity parameter that allows us to study the environment of radio sources independently from their intrinsic
properties estimates. We discuss the results in Section 5.5.
5.2
A     AGN   XMM-LSS 
In this section we briefly introduce the XMM-LSS survey, and the sample of radio sources that has
been described in full detail in Tasse et al. (2007a).
The XMM-Large Scale Structure field (XMM-LSS) is a 10 square degree extragalactic window
observed by the XMM-Newton X-ray satellite in the 0.1 − 10 keV energy band. The XMM-LSS
area has been followed up with a broad range of extragalactic surveys. The Wide-1 component of
the Canada France Hawaı̈ Telescope Legacy Survey (CFHTLS-W1) will image 7 × 7 degree2 in
the 5 broad band u∗ g’r’i’z’ filters, reaching an i-band magnitude limit of iAB ∼ 25. As part of
the Spitzer Wide-area InfraRed Extragalactic legacy survey (SWIRE, Lonsdale et al. 2003), the
XMM-LSS field was imaged in 7 infrared bands from 3.6 to 160 µm over ∼ 9 degree2 (see Pierre
et al. 2004, for a layout of the associated surveys). Low frequency radio surveys of the XMM-LSS
field have been carried out with the Very Large Array (Tasse et al. 2006) at 74 and 325 MHz, and
with the Giant Meterwave Radio Telescope (GMRT) at 230 and 610 MHz (Tasse et al. 2007b).
In Tasse et al. (2007a) we derived estimates of photometric redshifts, stellar masses (M), and
specific star formation rates sSFR0.5 (averaged over the last 0.5 Gyr) for ∼ 3 × 106 galaxies in the
CFHTLS-W1 field, using the ZPEG photometric redshift code (Le Borgne & Rocca-Volmerange
2002). We estimated the uncertainties were typically σ(z) ∼ 0.1, σ(log(M/[M⊙ ])) ∼ 0.15 and
σ(log(sS FR0.5 /[yr−1 ])) ∼ 0.3.
We matched the radio sources detected at 230, 325, and 610 MHz (Tasse et al. 2006, 2007b)
with their optical counterpart using the the CFHTLS optical images. To do this we used a modified
version of the likelihood ratio method described in great detail in Sutherland & Saunders (1992),
which allows us to derive for each radio source i, a probability Piid ( j) of association with a given
optical candidate j. Using Monte-Carlo simulations, we have quantified and corrected for the contamination from missidentifications. Each optical candidate was also cross-identified with infrared
SWIRE sources at 3.6, 4.5, 5.8, 8.0 and 24 µm.
In order to select a subsample of objects having reliable photometric redshifts estimates, we
applied a few basic cuts to the identified sample, rejecting the masked, saturated, and point-like
objects. Furthermore, the objects that did not satisfy the following properties were rejected:
- 18 < i < 24
108
Host galaxies and environment of active galactic nuclei
Figure 5.1: Using the 1/Vmax comoving number density estimator, we have derived the stellar mass function
for normal galaxies and for radio sources’ hosts in different redshift bins. For the normal galaxies, at all
redshifts our estimate of the stellar mass function is in good agreement with its measurement in the GOODS
surveys (Fontana et al. 2006), which suggests the stellar masses estimates are reliable. The underestimate
of the mass function at low stellar masses in the redshift bins 0.4 < z < 0.9 and 0.7 < z < 1.2 is due to
incompleteness. The stellar mass function of the radio sources’ hosts shows a very different, evolving shape.
- Nb ≥ 3
- 0.1 < z ph < 1.2
where Nb is the number of bands the object is detected in, and z ph is the photometric redshift
estimate.
We also rejected the Type-1 AGN objects, since these will have corrupted physical parameter
estimates. To do this, we fitted the u∗ g’r’i’z’ and IRAC magnitude measurements with SED
Radio-loud AGN in the XMM-LSS field: a dichotomy on environment and accretion mode? 109
Figure 5.2: These plots show the fraction of radio sources that are radio loud as a function of the stellar
mass in a given comoving volume. These relations have been derived using the mass function estimates of
the normal and radio loud galaxies presented in Fig. 5.1. In the lower redshift bins, our measurement of
the fRL − M relation matches its SDSS/NVSS z . 0.3 estimate (Best et al. 2005) both on normalisation and
shape. Whereas the fraction of high stellar mass object M & 1011 M⊙ stays fairly constant with redshift, it
seems the fraction of lower stellar masses objects (1010.0 M⊙ < M < 1010.5 M⊙ ) undergoes a strong evolution.
templates retrieved from the SWIRE library (Polletta et al. 2006). These templates contain both
normal galaxies and AGN. We rejected the objects best-fitted by a template having a strong AGN
contribution, and those selected with a selection criteria in the g-r vs r-i color-color diagram. We
estimate that only ∼ 2% of the remaining objects are contaminating type-1 AGN. In addition, for
our purpose, we reject the radio emitting starburst galaxies, since these would contaminate our
radio loud AGN sample.
110
5.3
Host galaxies and environment of active galactic nuclei
I        
In this section, we study the intrinsic properties of the radio sources’ hosts sample described above.
Specifically, in Section 5.3.1 we compare their stellar mass function to that of the normal galaxies
in different redshift bins and in Section 5.3.2 we address the evolution of radio sources using the
V/Vmax estimator. In Section 5.3.3, we compute an infrared excess estimator.
5.3.1 Stellar mass functions
We derived the stellar mass function for normal galaxies (φOpt ) and for the radio sources’ hosts
(φRad ) by using the 1/Vmax estimator (Schmidt 1968), which corrects for the fact that our sample
is magnitude limited. This procedure is described in detail in Appendix A. Fig. 5.1 show the
stellar mass functions in the redshift ranges: 0.1 < z < 1.2, 0.1 < z < 0.6, 0.4 < z < 0.9, and
0.7 < z < 1.2.
A number of authors have estimated the galaxy mass function using different techniques, in
various redshift ranges (see Fontana et al. 2006, for a general review). We compare our estimates
of φOpt to the stellar mass function as estimated by Fontana et al. (2006) in the redshift intervals
{0.4, 0.6, 0.8, 1.0, 1.3}, using the GOODS-MUSIC catalogs (Grazian et al. 2006), which contains
broad band photometry from the optical to infrared regime, as well as a spectroscopic data for
∼ 27% of the sample. At all redshifts, our estimates of φOpt show good agreement with Fontana
et al. (2006) over the full mass range, both on normalisation and shape. The low values obtained
at low stellar masses in the higher redshift bin are discussed below.
As expected from the SDSS-NVSS analysis (Best et al. 2005), the shape of φRad is different
from φOpt , with the radio sources’ hosts being biased towards more massive systems. Interestingly,
while the comoving number density of normal galaxies decreases with redshift, the radio sources’
hosts having M < 1011 M⊙ show strong positive redshift evolution. In the redshift bin 0.7 < z <
1.2, the stellar mass function is rather flat.
This effect is clearly shown in Fig. 5.2 which displays the fraction of galaxies that are radioloud galaxies ( fRL = φRad /φOpt ), as a function of stellar mass in the four redshift bins. At low
redshift and at M & 1010.5 M⊙ , the shape and normalisation of our estimate of fRL matches fRL ∝
M 2.5 as found by Best et al. (2005) in the redshift range z . 0.3. However, we find evidence
that the fRL (M) relation flattens at M . 1010.5−11.0 M⊙ . In the higher redshift bins the fraction of
radio-loud objects agrees with the low redshift measurements for high stellar masses, but the lower
stellar masses M . 1010.5−11.0 show a strong evolution. The physical implication of these results
are discussed in Section 5.5.
We investigate below the possibility that this effect is caused by (i) an incompleteness effect
caused by our flux limited survey and (ii) the scatter along the stellar mass axis, due to the uncertainties on that parameter.
Fontana et al. (2004) have extensively discussed a common incompleteness effect arising when
computing comoving number densities from flux limited surveys. The 1/Vmax estimator corrects
for the number densities of the galaxies detected in each given stellar mass bin. However, these
galaxies have different spectral types and may have very different mass-to-light ratios. Therefore,
at high redshifts especially, galaxies of some spectral type may just not be detected, and the comoving number density estimate although corrected using the 1/Vmax estimator, will still be an
Radio-loud AGN in the XMM-LSS field: a dichotomy on environment and accretion mode? 111
underestimate. Radio sources’ hosts may be significantly different from normal galaxies, hence
may have mass-to-light ratios that differ on average to those of the normal galaxy population, leading to a different incompleteness for φOpt and φRad , thereby driving a bias of fRL . We investigate
the possibility that this effect causes the flattening of the fRL − M relation by estimating an upper limit to that bias. In the most extreme case all radio sources’ hosts are detected, but not all
normal galaxies. The good match between our mass function for the normal galaxies and that of
Fontana et al. (2006) indicates that this effect should not significantly affect φOpt in the redshift bin
0.1 < z < 0.6 and 0.4 < z < 0.9. However, the lower estimate of the comoving number density
for M < 1010 M⊙ in the higher redshift bin indicates that the effect of incompleteness may affect
our comoving number density estimate by a factor of ∼ 2. The bias should therefore be less than a
factor of ∼ 2, while the flattening involves differences by factor of ∼ 100. We therefore conclude
that this effect cannot explain the observed flattening.
We investigate the possibility that this flattening is produced by the uncertainty on the stellar
masses estimate, that are higher at higher redshift. For this, we generate radio sources’ hosts mass
functions corresponding to a fraction fRL = C11 M α , where α is the slope of the relation and C11
is its normalisation at 1011 M⊙ . We assume that the Vmax within a given stellar mass bin will be
similar for all galaxies of that bin. Given the average Vmax of the objects of a given stellar mass, we
estimate the true number of sources to be observed in a given stellar mass bin for each α. In order
to generate the catalog corresponding to a fRL ∝ M α relation, we have to scatter the true stellar
mass estimates. Each object in a given stellar mass bin is given the stellar mass of the ith object of
the S1 sample with a probability pi = Pid (i) × pi (∆M), where Pid is the identification probability
(Tasse et al. 2007a) and pi (∆M) is the probability that the true stellar mass of object i is in the mass
bin ∆M. The operation is repeated 10 times, and the fraction fRL is re-evaluated in each mass bin.
As expected the mass scatter has the effect of increasing the observed fraction of low stellar mass
objects. We quantify this effect by calculating the χ2 on a grid where the free parameters are α
and C11 , and associated error bars are taken at χ2min + 1 (Avni & Bahcall 1976). Fig. 5.3 shows the
best fit parameters in different redshift slices. The normalisation C11 of fRL stays roughly constant
through redshift. At low redshift, the slope measurement gives a good fit to the α ∼ 2.5 found by
Best et al. (2005), while it progressively flattens towards higher redshift. This shows that the effect
of the stellar masses uncertainty cannot explain the flattening of the fraction-mass relation at low
stellar masses.
As a check, we have computed the radio luminosity function (RLF) of the radio loud AGN in
our sample by using the comoving number density estimator described in Appendix A. The RLF
(Fig. 5.4) of radio sources’ hosts in our sample is in good agreement with the Willott et al. (2001)
RLF estimates of the 7CRS, 3CRR, and 6CE radio sources samples selected at 150 MHz.
5.3.2 V/Vmax statistics
In this section we address the issue of the evolution of radio sources’ hosts within our sample
using the V/Vmax test (Schmidt 1968), where V is the comoving volume corresponding to the
observed redshift of the radio sources’ hosts, and Vmax is the maximum available volume, described
in Appendix A. If the radio source population is not evolving, then V/Vmax is uniformly distributed
over the interval [0, 1] and hV/Vmax i = 0.5 ± (12N)−0.5 where N is the number of sources in the
sample. Values of hV/Vmax i > 0.5 implies a higher comoving number density at high redshifts,
112
Host galaxies and environment of active galactic nuclei
Figure 5.3: In order to investigate the possibility of the flattening seen in Fig. 5.2 to be due to the uncertainty
on stellar mass estimates, we generate radio sources’ hosts samples being characterised by a relation fRL =
C11 M α . We set C11 and α to be free variables, and after introducing a scatter on the stellar mass estimate, we
measure the χ2 corresponding to each C11 and α. This figure shows (i) the scatter introduced by the stellar
mass uncertainty cannot explain the flattening of the fRL − M relation, and (ii) our sample agrees with the
Best et al. (2005) measurement at low redshift.
Figure 5.4: In this figure, we compare our estimate of the radio luminosity function in 0.1 < z < 1.2 with
the Willott et al. (2001) RLF estimate at z = 0.9. Both the normalisation and slope are in good agreement
confirming the photometric redshifts are reliable.
Radio-loud AGN in the XMM-LSS field: a dichotomy on environment and accretion mode? 113
and therefore a negative evolution with cosmic time, whereas hV/Vmax i < 0.5 indicates a positive
evolution. A number of authors have used this estimator to address the cosmological evolution of
radio sources selected at low frequency (Dunlop & Peacock 1990; Willott et al. 2001).
Figure 5.5: We compute hV/Vmax i in different radio power ranges. Our measurement is in good agreement
with Clewley & Jarvis (2004), with the low power radio sources (P . 1025 W.Hz−1 ) evolving positively
with cosmic time, whereas the higher power evolve negatively.
Fig. 5.5 shows the comparison between the hV/Vmax i radio power relation for our sample and
that of Clewley & Jarvis (2004), which was built from SDSS galaxies selected at 325 MHz. There
is a good agreement between the two estimates.
In Fig. 5.6 we compute the hV/Vmax i in different stellar mass bins. Although radio sources
are seen to evolve more than normal galaxies on average, their respective evolution show a similar
trend with the stellar mass: low stellar mass systems evolve more than high stellar mass ones.
These results are further discussed in Section 5.5.
5.3.3 Infrared properties of radio sources’ hosts
As described in Tasse et al. (2007a), we have associated to each radio source the infrared IRAC
flux density measurements at 3.6, 4.5, 5.8 and 8.0 µm. Because ZPEG does not include infrared
dust emission, the photometric redshifts have been computed from the magnitude measurements
in the u∗ g’r’i’z’ bands. We degine an infrared excess parameter as:
∆IR = log(Fν (λIRAC )/FνZPEG (λIRAC ))
(5.1)
where Fν (λIRAC ) is the IRAC flux density measurement at λIRAC and FνZPEG (λIRAC ) is the flux density
measurement from the ZPEG best fit template at λIRAC . The infrared excesses are computed in the
observer frame.
114
Host galaxies and environment of active galactic nuclei
Figure 5.6: The averaged V/Vmax in different stellar mass bins, for the normal galaxies, and for the radio
sources’ hosts. The high stellar mass radio sources’ hosts show a similar evolution to the non radio loud
galaxies of the same mass, while the low stellar masses galaxies show strong evolution.
Fig. 5.7 shows the infrared excess at 3.6 µm computed for the normal galaxy population and
for radio sources’ host galaxies . The infrared excess is higher for the radio sources’ hosts than
for the normal galaxies, especially at low stellar masses. Yet the radio sources’ hosts and the
normal galaxies population have different properties, notably in terms of redshift and magnitude
distribution. In order to compare the infrared properties of these two distinct population, for each
galaxy we compute the quantity ∆RIR − < ∆NIR (dz, dM) >, where ∆RIR is the infrared excess of the
given radio source host, that is in the mass bin dM and in the redshift bin dz, and < ∆NIR (dz, dM) >
is the averaged value of the infrared excess for the normal galaxies that lay in the same mass and
redshift bin. Fig. 5.7 shows that an infrared excess remains observed for the low stellar mass radio
sources’ hosts. The high stellar mass radio sources’ hosts do not show an infrared excess. This
result is further discussed in Section 5.5.
5.4
T        
In order to study the environment of radio sources, we use a scale-dependent estimator of the overdensity around a given galaxy, which is based on the photometric redshift probability functions.
The overdensity estimator is described in detail in Appendix B. This estimator has the advantage
of (i) having a physical comoving scale as input, (ii) fully using the information contained within
the photometric redshift probability function, and (iii) controling edge effects. Overdensities found
on large scales may refer to galaxy clusters, whereas smaller scales may refer to small groups of
galaxies, or pairs of galaxies.
Radio-loud AGN in the XMM-LSS field: a dichotomy on environment and accretion mode? 115
Figure 5.7: In order to retrieve information of the infrared emission of radio sources’ hosts, we compute
the infrared excess ∆IR . The top panel shows the infrared excess at 3.6 µm for the radio sources’ hosts and
for the normal galaxies. In order not to bias the observation, in the bottom panel, we compare the infrared
excess of individual radio sources’ hosts with normal galaxies that are in the same mass and redshift range.
The low stellar masses radio sources’ hosts present an infrared excess in all the 3.6, 4.5, 5.8 µm bands,
while the high stellar mass M & 1010.8−11 M⊙ do not.
116
Host galaxies and environment of active galactic nuclei
Figure 5.8: We have derived an overdensity estimator based on the individual photometric redshifts probability functions. The top left panel show a given region of the CFHTLS field in which we have computed
the overdensity parameter at different scales for the objects brighter than i = 23. The other panels show
the overdensity for each object on 450, 250 and 75 kpc scales, following the color code of top right panel.
The clustering at the different scales looks different. The galaxy cluster that appears visually obvious in
the i-band image is detected with a 450 kpc scale giving many galaxies an overdensity parameter ρ450 & 5.
Decreasing the overdensity scale enhances small groups of galaxies or even galaxy mergers.
Radio-loud AGN in the XMM-LSS field: a dichotomy on environment and accretion mode? 117
5.4.1 The overdensity parameter
The derivation of the overdensity parameter is described fully in Appendix B, but we summarised
here the basic idea. The χ2 (z) that were available for all the objects of the CFHTLS-W1 field (Tasse
et al. 2007a) are first converted into probability functions p(z). Given an object, its associated
p(z), and a comoving scale Rkpc , we estimate the number of objects n enclosed in the co-cone of
radius Rkpc . Because the optical survey is flux limited, the estimate of n strongly depends on the
probability function of the considered object: if the object is at high redshift, the probability of
detecting nearby object is low, which biases the number density towards lower values. Therefore,
we define the overdensity parameter by the significance of a given observed n. For doing this,
we generate 20 catalogs containing the same objects, with uniformly distributed positions (no
clustering). In each of these catalogs, the number density nuni f around the given object is calculated
and the mean hnuni f i and standard deviation σ(nuni f ) are estimated. The overdensity ρ is then
computed as ρ = (n − hnuni f i)/σ(nuni f ).
We have derived the overdensity parameter on 75, 250, and 450 kpc scales for both the radio
sources’ host sample and for the normal galaxies. Fig 5.8 shows an example of the overdensity
parameters estimates derived for the i < 23 objects within a 5′ × 5′ field. We chose this location
because it contains galaxies belonging to a galaxy cluster as well as field galaxies. Qualitatively,
our algorithm looks efficient: a high overdensity parameter corresponding to an overdense region
is seen at the location where the overdensity is obvious in the sky plane.
5.4.2 The environment of radio sources
The overdensity parameter is likely to be quite sensitive to redshift, since the optical survey is
flux limited. Comparing the overdensity distribution of two population having different magnitude
and redshift distribution can therefore be misleading. Therefore, in the following, we compare the
environment of radio sources’ hosts given galaxy to the normal galaxy population that are in the
same mass and redshift range. We do this by computing the quantity ∆ρi = ρi − q0.5 [ρN (dz, dM)],
where ρi is the overdensity of the given galaxy being in the redshift and mass bins (dz, dM), while
q0.5 [ρN (dz, dM)] is the median overdensity parameter of normal galaxies in the same redshift and
stellar mass interval. In practice, dM is taken to be the stellar mass bin, and we set dz = 0.1.
Fig. 5.9 shows the median value of ∆ρ in different stellar mass bins and at different scales.
The observed relations were quite bin dependent, therefore we smooth the observation with a box
of width ∆M = 0.4. In order to quantify the uncertainty in the median value estimate, we follow
a Monte-Carlo approach. We assume the ∆ρ distributions have the same shape in all stellar mass
bins. By generating samples of n sources following the same distribution we estimate the error bar
on the median as the standard deviation between the estimated median and the true median.
A stellar mass dichotomy appears in Fig. 5.9, with the two different environmental regimes
occuring above and below a stellar mass range of ∼ 1010.5−10.8 M⊙ . The higher stellar mass radio
sources’ hosts lie in a 450 kpc scale environment that is on average denser than the environment of
the non-radio-loud galaxies of the same mass by ∆(ρ) ∼ 0.7, while their small scale environment
has ∆(ρ) ∼ 0. An inverse relation is observed for the low stellar masses objects: their small
scale 75 kpc scale environment is denser than the average by ∆ρ ∼ 0.3, while their large scale
environment is significantly underdense on average, with ∆ρ ∼ −0.5. However, the estimated
118
Host galaxies and environment of active galactic nuclei
Figure 5.9: The top panel shows the difference in overdensity parameter ∆ρ between the radio sources’
hosts, and the normal galaxies, as a function of the stellar mass. Because these two populations are significantly different in terms of redshift and magnitude distribution notably, we compare the overdensity of each
radio source to the overdensity around normal galaxies in the same mass and redshift bin. This quantity is
plotted for different input scales. The massive radio sources’ hosts preferentially lie in large scale (∼ 450
kpc) overdensities, while the less massive ones lie in large scales underdensities, and small scales (∼ 75 kpc)
overdensities. However, the overdensity estimates on a given scale may depend on the overdensity estimate
on another scale. In order to address that issue, in the bottom panel we compute the overdensity differences
∆ρ on small and large scale for galaxies situated in similar large and small scale environment respectively
(see discussion in the text). The environmental dichotomy remains observed.
Radio-loud AGN in the XMM-LSS field: a dichotomy on environment and accretion mode? 119
overdensities may be dependent at the different scales: high 450 kpc scale overdensities may lead
to higher 75 kpc scale overdensities. In order to study the 75 kpc overdensities of radio sources’
hosts independently from their large 450 kpc environment, we compute the quantity ∆ρ(75|450) =
N
N
(dz, dM, dρ450 )] is the median overdensity of non(dz, dM, dρ450 )], where q0.5 [ρ75
ρi,75 − q0.5 [ρ75
radio-loud galaxies that lie in similar large scale environment and that have comparable stellar
mass, and redshift estimates. Similarly, we compute ∆ρ(450|75), and we take dρ = 0.3. Fig. 5.9
shows ∆ρ(75|450) and ∆ρ(450|75): the environmental dichotomy remains observed with the stellar
mass cut in the range ∼ 1010.8−11.0 M⊙ . These results are further discussed in Section 5.5.
5.4.3 Comparison with X-ray selected galaxy clusters
In this section, we compare the overdensities found around radio sources to the overdensity estimates of the galaxies aligned with X-ray groups and clusters. Studying the dependence of the
overdensity estimate on the bolometric luminosity of these clusters (ie their dark matter halo mass),
allows us to put further constrains on the environment of radio sources determined in Section 5.4.2.
Figure 5.10: This figure shows the comparison between our estimate of the bolometric luminosity
(LX (zphots)) with the bolometric luminosity LX (zspec) as deduced using spectroscopic redshift and X-ray
spectral fits. Except for one source, the two estimates are in agreement.
We here consider the sample of X-ray clusters detected as extended X-ray emission (Pacaud
et al. 2006) in the initial ∼ 5 degree2 of the XMM-LSS field (Pierre et al. 2004). By fitting a
model of free-free emission to the X-ray spectra of 29 sources, Pierre et al. (2006) and Pacaud
et al. (2007) measured bolometric luminosities as well as temperatures. Only 12 of those sources
overlap with the CFHTLS-W1 field. In order to increase the size of the X-ray cluster sample, we
also consider the X-ray sources classified as extended by the X-ray pipeline, but have not been
spectroscopically confirmed. The final sample of extended X-ray sources contains 35 sources in
120
Host galaxies and environment of active galactic nuclei
the redshift range z . 1.2. We describe below how we derived a crude estimation of the redshifts
and bolometric luminosities of these clusters.
Figure 5.11: Top left panel: the overdensity parameter for the galaxies aligned with X-ray cluster emission
and field galaxies in the same redshift ranges. The overdensity parameter appears to be quite efficient. Top
right to bottom right: the difference on overdensity parameter between the radio sources’ hosts, and the
normal galaxies for different X-ray luminosities. In each panel, the estimated redshift distribution of the
X-ray clusters is indicated (full line), and compared to the redshift distribution of the radio sources’ hosts
(dashed line). Although our overdensity parameter is biased by redshift, it seems that the increase of the
halo mass leads to a higher overdensity parameter estimate. Comparing this with Fig. 5.9, it seems that
massive radio sources lie in rather small clusters on average.
We estimated the overdensity on 75, 250 and 450 kpc scales for the galaxies that lie within
30′′ the galaxy clusters detected as extended X-rays sources. In most cases, inspecting the ρ450 − z
plane we can see a peak in the redshift distribution of the galaxies aligned with a given extended
X-ray source, and having a high ρ450 > 2 overdensity estimate. If a redshift peak was detected, we
assigned a redshift to the extended X-ray emission, otherwise we rejected the X-ray source. We
estimated the bolometric luminosity using the X-ray pipeline XSPEC. We have modelled the Xray emission with a bremsstralung emission model (named “APEC” in XPEC), and by assuming a
temperature of 3 keV, at each redshift in 0 < z < 2 we have derived a [0.5-2] keV flux to bolometric
luminosity conversion factor. Allowing the temperature to vary from 0.5 to 10 keV, affects the
conversion factor by a factor of ∼ 3. For the extended X-ray sources confirmed spectroscopically
Radio-loud AGN in the XMM-LSS field: a dichotomy on environment and accretion mode? 121
(Pacaud et al. 2007), Fig. 5.10 shows the comparison between the bolometric luminosities as
estimated using (i) the combination of photometric redshift and overdensity parameter and (ii)
the spectroscopic redshifts and spectral fits (Pacaud et al. 2007). The agreement is quite good on
average.
Fig. 5.11 shows the averaged values of ∆ρ (see Section 5.4.1) in different galaxy stellar mass
and bolometric luminosity ranges. Galaxies aligned within a luminous X-ray cluster, have higher
overdensity estimates: in the luminosity range LX > 1043.5 erg.s−1 , ∆ρ is as high as ∼ 9 whereas
∆ρ ∼ 3 at LX < 1043.0 erg.s−1 . We interpret this effect as being caused by an increase of the true
overdensity with increasing X-ray luminosity as it is well known that the bolometric luminosity of
the X-ray emitting gas correlates with the dark matter halo mass (Popesso et al. 2005).
Although the overdensity parameter might be biased by redshift effects, and probes number
density rather than mass, it seems we can further constrain the environment of radio sources.
We can already see from the overdensities estimates of the galaxies in the brightest (LX > 1043.5
erg.s−1 ) X-ray clusters that, although they have a similar redshift distribution to the radio sources’
hosts, their overdensities are far higher. The overdensity around radio sources is rather similar to
the overdensity found within the lower luminosity clusters, whose halo masses are on the order of
M ∼ 1014 M⊙ (Popesso et al. 2005). These results are consistent with previous studies in which
radio sources’ hosts were found to be preferentially located in environment of moderate density
(eg. Hill & Lilly 1991; Best 2000).
5.5
D  
In this paper we have carried out a series of analyses giving further evidence that our estimates
of photometric redshifts and stellar masses for the radio sources’ hosts sample built in Tasse et al.
(2007a) are reliable. Specifically, our estimate of the radio luminosity function as derived using
the 1/Vmax estimator (Fig. 5.4) shows a good fit with the Willott et al. (2001) radio luminosity
function that has been estimated using a complete sample of radio sources selected at 150 MHz.
Furthermore our estimate of the V/Vmax vs radio power relation fits the SDSS measurement of
(Clewley & Jarvis 2004), suggesting there should be no systematics between the radio luminosity
and the accuracies of the photometric redshifts. For the sample of normal galaxies, our estimate
of the stellar mass function is similar to the Fontana et al. (2006) stellar mass function from the
GOODS survey. Also, in the lowest redshift bin 0.1 < z < 0.6, the relation betweeen the fraction
of radio-loud galaxies and the stellar mass relation is in good agreement with the SDSS z . 0.3
measurement in the radio power range P1.4 > 1024 W.Hz−1 from Best et al. (2005).
In Section 5.3 and 5.4, we investigated the intrinsic and environmental properties of radio
sources’ hosts as compared to the normal galaxy population. The sample extends up to z ∼ 1.2,
and across the radio power range 1024−27 W.Hz−1 . The main results are as follows:
(i) The relationship between the fraction of radio-loud galaxies and the stellar mass shows a
break in the range M ∼ 1010.8−11 M⊙ and z & 0.5.
(ii) The low stellar mass radio source host galaxies show a stronger evolution than the high
stellar mass galaxies. At z ∼ 1, the mass function of radio sources’ hosts appears to be
significantly flatter than in the local universe.
122
Host galaxies and environment of active galactic nuclei
(iii) High stellar mass radio sources are seen to be preferentially located in poor clusters of galaxies.
(iv) The environment of the low stellar mass radio sources is biased towards large-scale underdensities, and small-scale overdensities.
(v) At M . 1010.8−11 M⊙ , galaxies have a hot dust component observed as an infrared excess,
while the galaxies with M & 1010.8−11 M⊙ do not.
These results suggest the existence of dichotomy in the nature of both the hosts and environment of radio sources. We argue below that the observed dichotomy might be caused by the
different ways of triggering the black hole activity as discussed in Section 5.1.
Best et al. (2005) used a large sample of low luminosity radio sources in the SDSS (z . 0.3)
to show that the fraction fRL of radio loud galaxies scales with the galaxy stellar mass as fRL ∝
M 2.5 , and argued that the IGM gas cooling rate Ṁ that has the same dependence on stellar mass
( Ṁcool ∝ M 2.5 ), provides a way of feedding the black hole and triggering the AGN. For our dataset,
in the redshift range 0.1 < z < 0.6 the fraction of radio loud galaxies show a similar dependence
on the stellar masses of galaxies. Furthermore, our results (iii) supports this picture as the high
stellar mass systems that are radio-loud are preferentially located in large 450 kpc scale overdense
environments as compared to non-radio-loud galaxies of the same mass. This environment resembles small clusters of galaxies with M ∼ 1014 M⊙ , in agreement with observations of low redshift
radio sources lying in moderate groups to poor clusters (Best 2004, and references therein). In
contrast, Best et al. (2007) found that the radio-loud fraction versus stellar mass relation flattens to
fRL ∝ M 1.5 for a sample of brightest cluster galaxies (BCGs), while there is evidence that the radio
sources observed at high redshift lie in rich cluster environment (Best et al. 2003; Kurk et al. 2004;
Venemans 2006). Interestingly, in the redshift bin 0.6 < z < 1.2, the radio sources with P1.4 > 1025
W.Hz−1 show a dependence of fRL with the stellar mass that flattens to fRL ∝ M ∼1.8 , which could
be due to a greater fraction of radio-loud galaxies that are located at the center of galaxy clusters
by z ∼ 1.
Result (iv) suggests that the low stellar mass, strongly evolving component of the radio sources’
hosts population inhabit a different environment than the radio-loud AGN with high stellar mass
host galaxies discussed above. Compared to normal galaxies of the same mass, radio-loud galaxies preferentially lie in large scale underdensities (450 kpc comoving), and overdensities at small
scales (75 kpc), suggesting their AGN activity may be triggered by galaxy mergers and interactions. Similarly, ULIRGs are found to be associated with galaxy interactions or galaxy mergers
(Section 5.1), and star forming galaxies have been shown to be preferentially located in underdense
environments, where the low velocity dispersion conditions favour the galaxy mergers and interactions (Gómez et al. 2003; Best 2004). Furthermore the low mass radio-loud AGN in our sample
have a significant infrared excess at 3.6 µm (observer frame) as compared to non-radio-loud galaxies of the same mass. Seymour et al. (2007) have already observed such infrared excesses in high
redshift radio galaxies, and concluded on the presence of hot (∼ 0.5 − 1 × 103 K) dust, heated by
an obscured, highly accreting AGN. This is consistent with AGN unified schemes whereby these
objects are radiatively efficient radio-loud quasars viewed edge-on. The infall of the cold IGM gas
in the potential well of those low stellar mass systems AGN might provide an alternative triggering
process to the galaxy mergers discussed above. In such scenarios, the tendency of those quasar
mode AGN to be located in underdense environment may indicate that the black hole accretes cold
References
123
gas as well since the IGM gas in underdense regions has a lower temperature than in overdense
regions.
As discussed in Hardcastle et al. (2007), the state of the gas that reaches the black hole might
play an important role in triggering the quasar and the radio modes (Section 5.1). The observed
environmental dichotomy reported here, with the low stellar mass (M < 1011 M⊙ ) systems having
a hot infrared excess, support the picture in which the galaxy mergers or the cold IGM gas infall
trigger high efficiency accretion, while the hot IGM gas cooling from the atmosphere of massive
galaxies trigger the radiatively inefficient accretion of low luminosity radio-loud AGN. It might
be that the number density of low-mass radio-loud AGN is low in the nearby Universe because
the combination of fairly massive black hole and a galaxy merger or interaction which can supply
cold gas, are quite rare. However, these conditions will be more common in the gas-rich early
Universe, which might explain the higher number density of low stellar mass radio-loud AGN at
higher redshift. As the large scale structure forms and the environment of galaxies changes, the
competing mechanisms discussed in this paper may play an important role in the evolution of the
AGN activity.
A
The optical images were obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA,
at the CFHT which is operated by the National Research Council (NRC) of Canada, the Institut National
des Sciences de l’Univers of the Centre National de la Recherche Scientifique (CNRS) of France and the
University of Hawaii. This work is based on data products produced at TERAPIX and at the Canadian
Astronomy Data Centre as part of the CFHTLS, a collaborative project of NRC and CNRS.
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A
A
N  
The S1 sample presented in Tasse et al. (2007a), contains for each radio source i, an association
probability Piid ( j) that its true counterpart is the jth optical candidate. As discussed in Tasse et al.
(2007a) the probabilities Piid ( j) have been modified in order to take into account contamination
from a remaining missidentification fraction.
In order to compute a number density in the comoving space, we use the standard 1/Vmax
estimator first described in Schmidt (1968). Using the S1 sample we estimate the mean comoving
number density in a region R of the parameter space as:
X
i
hφR i = C
[Piid ( j)/Vmax
( j)]R
(A1)
Ωi, j (R)
where Ωi, j (R) is the set of {i, j} optical candidates which are located within the region R, Vmax is the
maximum comoving volume over which a given source can enter our sample and C is a constant
designed to compensate for (i) the random selection of the objects in the optical catalog and (ii)
the ∼ 30% of the optical field that is masked. The error bar associated with hφR i is:
sX
i ( j)]2
σ(hφR i) = C
[Piid ( j)/Vmax
(A2)
R
Ωi, j (R)
In practice, we set Vmax = V(zmax ) − V(zmin ) where V(z) is the comoving volume enclosed
out to a given z, while zmax and zmin are the maximum and minimum redshifts for which a given
object is selected. Vmax depends on the effective surveyed area at each given flux density. We have
estimated that dependence by comparing our observed source counts to a field with deeper radio
source counts (Seymour et al. 2005). The redshifts lower and upper bounds zmin and zmax depend
upon (i) the redshift range corresponding to the cell R of the parameter space in which we estimate
hφR i (ii) the selection criteria of our optical data and (iii) the selection criteria of our radio surveys.
For each of the selection types (i), (ii) and (iii), we consider a zmin and a zmax .
Estimating zmin and zmax for the selection type (i) is trivial and just depends on the redshift bin
used to derive the comoving number density. To calculate the maximum redshifts corresponding
to the selection (ii) for a given object, we consider its radio power and the estimate of the spectral
index as derived by Tasse et al. (2006) and Tasse et al. (2007b). We estimate zmax as the redshift
corresponding to that object being detected at the limiting flux density of the radio survey at either
325 or 610 MHz. Estimating zmin and zmax based on the third selection criterion uses the magnitude
126
Host galaxies and environment of active galactic nuclei
selection criteria 1 and 2 of Sec. 5.2. For each optical object we consider the best fitting ZPEG
SED template. The lower bound zmin is then the redshift corresponding to an observed i-band
magnitude i = 18, whereas zmax is either the redshift for which i = 24 or the maximum redshift for
which the selection criteria 2 is satisfied (Sec. 5.2).
For each object, the final zmin and zmax to be used is derived as zmin = max({zmin (i), zmin (ii), zmin (iii)})
and zmax = min({zmax (i), zmax (ii), zmax (iii)}) where the indices (i) and (ii) and (iii) refer the selection
types (i), (ii) and (iii) defined above.
B
B1
O 
Probability functions
The use of photometric redshifts codes is generally limited to the determination of the values
associated to the best fitting template, which do not include multiple solutions for example. In
order to fully use the information derived from the fitting of the magnitude points, as described in
Tasse et al. (2007a), the least χ2 has been recorded as a function of the redshifts for 200 values in
0 < z < 2. Following Arnouts et al. (2002), for each object, we relate the χ2 (z) function to the
photometric redshift probability function p(z) as follows:
p(z) ∝ χr−2 (z) exp(−χ2 (z)/2)
(B3)
where r is the number
R of degrees of freedom. Assuming that all optical sources have their true
redshift in 0 < z < 2, p(z)dz is normalized to 1 over this redshift interval.
B2
Overdensity parameter
In order to build our overdensity parameter, we calculate the mean number density around a chosen galaxy within an arbitrary chosen comoving volume, using the information contained in the
probability function p(z).
A radius Rkpc is first chosen in the comoving space. It defines a comoving scale to which the
overdensity estimate refers. Overdensities over large scales may refer to galaxy clusters, whereas
smaller scales may refer to small groups of galaxies or even galaxy pairs.
The redshift space is then binned so that the volume V of the cone of radius Rkpc and line-ofsight comoving length Dc (∆zi ) stays constant. We choose V so that ∆zi = zi+1 − zi ≈ 0.1, the typical
error bar on photometric redshifts (Tasse et al. 2007a). This leads to zi = {0.05, 0.12, 0.19, 0.28,
0.38, 0.49, 0.62, 0.76, 0.92, 1.10, 1.29}. In each redshift bin i centered at (zi + zi+1 )/2, the angular
diameter Ri,deg corresponding to Rkpc is calculated. Then, we derive the density around the given
object inside each redshift slice:
!
X Z zi+1
p j (z)dz
(B4)
ni = feff
j∈Ωi
zi
where Ωi is the set of the objects found within Ri,deg around the considered objects, and feff is a
term designed to correct for edges effects, for example, when the circle of diameter Ri,deg overlaps
References
127
with a masking region or the edges of the field. In that case, we make the assumption that the
number density of sources within the masked area is the same as the unmasked area within Ri,deg .
We then have feff = πR2i,deg /(πR2i,deg − Amasked ), with Amasked being the masked area. Then, the mean
density around the considered source can be written as:
!
X Z zi+1
p(z)dz
(B5)
n=
ni
i
zi
where p(z) is the probability function of the considered object. The estimate of n greatly depends
upon the given object probability function. In order to quantify the significance of the number
density estimate around the given object with a given probability function, we determine the mean
and standard deviation of n (eq. B5) in a similar catalag but in the absence of clustering. In
practice, we extract a catalog in a 0.2◦ × 0.2◦ square around the source in the CFHTLS-Wide
catalog, and reassign them a uniformly distributed random position. We make 20 realisations
of such a catalog, and in each we derive the number density nunif around the considered objects
using Eq. B4&B5. We compute the mean hnunif i and the standard deviation σ(nunif ) of the number
density around the considered object when there is no clustering. The overdensity is finally defined
as ρ sc = (n − hnunif i)/σ(nunif ), giving us the significance of the number density estimate.
128
Host galaxies and environment of active galactic nuclei
CHAPTER 6
Internal and environmental properties of
X-ray selected AGN.
C. Tasse, H. Röttgering, P. N. Best
To be submitted
 ere is mounting evidence to suggest that active galactic nuclei (AGN)
selected through optical emission lines or radio luminosities actually comprise two distinct AGN populations. In this paper we study the properties of
a sample of Type-2 AGN which were selected using their [2-10] keV X-ray
flux. The X-ray luminosity function is in good agreement with previous studies. The fraction of galaxies that are X-ray AGN is a strong function of the
stellar mass of the host galaxy. The shape of this relation is similar to the
fraction of galaxies that are emission-line AGN, while it significantly differs
from that relation observed for radio selected AGN. The AGN in our sample
are preferentially located in underdense environment were galaxy mergers and
interactions are likely to occur. They display a strong infrared excess at short
(∼ 3.5 µm) wavelength, suggesting the presence of hot dust. The results of this
paper suggest that the X-ray selection criteria probes a population of AGN with
actively accreting black holes (quasar mode), which is similar to the emissionline selected AGN population.
T
130
6.1
Host galaxies and environment of active galactic nuclei
I
It is becoming increasingly clear that active galactic nuclei (AGN) play an important role in the
framework of galaxy formation. The enormous amounts of energy produced by AGN during their
short lifetime can dramatically influence the evolution of both their host galaxies and their surrounding environment (eg. Croton et al. 2006; Springel et al. 2005).
Although AGN have been studied for decades, many aspects of their physics remain poorly
understood. In the picture of the unified scheme of AGN, energy is produced by the accretion
of matter onto a super-massive black hole, which is surrounded by a dusty torus. This simple
scheme can explain many properties of the different classes of AGN in different wavelength bands.
However, observational evidence is mounting to suggest that this picture does not give a proper
description for low-luminosity radio-loud AGN. These objects produce weaker or no emission
lines (Hine & Longair 1979; Jackson & Rawlings 1997), while they lack the dusty torus infrared
emission (Ogle et al. 2006) and the accretion related X-ray emission (Hardcastle et al. 2006). It has
been suggested that there are indeed two very different modes of AGN activity named the “Quasar
mode” and the “Radio mode” (Best et al. 2005; Hardcastle et al. 2007). A physical interpretation
has been proposed in which the infall of cold gas onto the super-massive black hole gives rise to
the radiatively efficient quasar mode, while the hot gas infall produces the radiatively inefficient
radio mode (Hardcastle et al. 2007).
The undertaking of large surveys provides the opportunity to conduce tests on the nature of the
AGN activity (see Heckman & Kauffmann 2006, for a review of the SDSS results). Based on a
sample of radio selected AGN in the Canada France Hawaii Telescope Legacy Survey (CFHTLS)
field (Tasse et al. 2007a) we have argued in favour of a dichotomy on stellar mass with a separation
at Mcut ∼ 1010.5−10.8 M⊙ (Tasse et al. 2007b). The high stellar mass systems were preferentially
found in cluster-like environments, and were not showing any signs of hot dust emission in the
infrared. The properties of the lower stellar mass systems were quite different: they had a lower
radio power on average, were displaying a hot dust component, and were laying in large 500 kpc
scale underdensities, as well as small 75 kpc overdensities. We have argued (Tasse et al. 2007b)
that these radio selected AGN are indeed very different population, with the AGN activity of the
low mass population triggered by galaxy mergers and interactions, and the high mass systems with
their AGN activity triggered by the gas cooling in their hot atmosphere (Best et al. 2005). Based
on the hot infrared excess that is observed only in the low mass systems, we have argued that these
observations are consistent with the picture discussed in Hardcastle et al. (2007), where the hot
gas cooling produces radiatively inefficient accretion (radio mode), and the cold gas accretion is
triggered by galaxy mergers and interactions which drives radiatively efficient accretion (quasar
mode).
A good way to further test the scheme in which the type of the accretion mode is connected to
the nature of the triggering mechanism, is to select AGN based on their X-ray properties. In the
picture of unified scheme, the hard X-ray emission is produced in the hot corona that surrounds
the black hole, by the comptonisation of soft UV photons which are emitted by the accretion disk
(eg. Liu et al. 2002). In this paper we present a similar study to that of Tasse et al. (2007b), using
a sample of hard X-ray selected AGN ([2-10] keV band) from the XMM-Large Scale Structure
field Pierre et al. (XMM-LSS, 2004). By using the photometric redshifts, stellar masses, and
overdensity estimates (Tasse et al. 2007b), we study the internal and environmental properties of
the host galaxies of the X-ray selected AGN in an independent manner. Our results suggest that the
Internal and environmental properties of X-ray selected AGN.
131
X-ray selected AGN population is dominated by AGN in their quasar mode, which are triggered
by galaxy mergers and interactions (cold gas).
In Sec. 6.2 we present the infrared, optical and X-ray data available for the XMM-LSS field. In
Sec. 6.3, we proceed with the optical identification, and we select a subsample of Type-2 sources
for which we can derive physical parameter estimates. We present the results in Sec. 6.4, and
discuss them in Sec. 6.5.
6.2
M 
Fig. 6.1 shows the location of the XMM-Newton pointings with respect to the SWIRE, CFHTLSW1, and low frequency radio surveys.
6.2.1 XMM-LSS X-ray survey
The XMM-LSS field is a wide ∼ 10 degree2 extragalactic window situated at high galactic latitudes which was surveyed by the XMM-Newton satellite in the [0.5-10] keV energy band. Galaxy
clusters are detected as extended X-ray emission, and X-ray emitting AGN are detected as pointlike sources. Their surface densities reach ∼ 12 and ∼ 200 deg−2 , respectively (see Pierre et al.
2004, for a layout of the XMM-LSS and associated surveys).
In this paper we consider the X-ray catalog described in great detail in Pacaud et al. (2006).
The catalog was built from the raw X-ray data in three steps: (i) solar proton flares are removed,
(ii) the X-ray images are filtered using wavelets, and (iii) using a maximum likelihood procedure,
the profiles of detected sources are fitted to determine whether they are point-like or extended. This
pipeline has been characterised in great detail using extensive Monte-Carlo simulations (Pacaud
et al. 2006). The final band-merged catalog contains sources detected in the [0.5-2] and [2-10] keV
bands respectively, referred to as ’soft’ and ’hard’ band.
The absorption of X-ray photons in general produces a strong decline of the flux measurement
in the soft X-ray bands (eg. Reynolds 1997), while this effect is less important at higher energies.
For our purposes, we select those X-ray sources that have been classified as point-like and that have
a likelihood ratio of detection LRDET such that LRDET > 15 (see Pacaud et al. 2006, for a detailed
description of LRDET ). The flux in the two available bands was computed from the photon count
rates assuming a single power law spectrum Fν ∝ ν−0.8 and the average galactic column density of
the XMM-LSS field: NH = 2.61 × 1020 cm−2 (Dickey & Lockman 1990).
In order to proceed with the optical identification we have selected X-ray sources overlapping
with the CFHTLS-W1 field (Sec. 6.2.2). The final X-ray sample contains 1001 sources. Following
Chiappetti et al. (2005), we assume that the error on the position of X-ray sources is σα,δ = 3′′ .
6.2.2 Optical and infrared surveys
The XMM-LSS field is partially covered by the Wide-1 component of the Canada France Hawaı̈ Telescope Legacy Survey (CFHTLS1 , Fig. 6.1). Observations were conduced using the five u∗ g’r’i’z’ broad
1
http://www.cfht.hawaii.edu/Science/CFHLS/
132
Host galaxies and environment of active galactic nuclei
Figure 6.1: The location of the CFHTLS, SWIRE, XMM-LSS fields. The black dots show the X-ray sources
selected for optical and infrared identification.
band optical filters, with typical exposures of 1 hour in each filter. The i-band limiting magnitude
is i ∼ 24.5 (80% completeness level), with positional uncertainties of ∼ 0.3′′ . In this paper we
have used the band merged catalogs Terapix T02 and T03 releases2 .
The Spitzer Wide-area InfraRed Extragalactic legacy survey (SWIRE, Lonsdale et al. 2003)
covers the XMM-LSS field over 9.1 degree2 , using the IRAC instrument from 3.6 to 8.0 µm and
MIPS from 24 to 160 µm (See Fig. 6.1). Throughout this paper we have used the data release 2
(DR2 hereafter) band merged catalog, available online3 , containing the flux density measurements
at 3.6, 4.5, 5.8, 8.0 and 24 µm for a total of ∼ 2.5 105 objects. This catalog contains sources
detected at 5σ from the 3.6 to 8.0 µm images and at 3σ from the 24 µm images, corresponding to
sensitivities of 14, 15, 42, 56, and 280 µJy, respectively, with positional accuracies better than 0.5′′
(2σ). The data reduction and quality assessment is extensively discussed in Surace et al. (2004).
2
3
http://terapix.iap.fr/
see http://swire.ipac.caltech.edu/swire/ for more information.
Internal and environmental properties of X-ray selected AGN.
133
Figure 6.2: In order to identify the optical counterparts of X-ray AGN, we take into account the magnitude
distribution that is different from the confusing background sources. The estimated fraction (y-axis) of
X-ray AGN having an optical counterpart with i-band below magnitude m (x-axis), has been determined
using a Monte-Carlo simulation. Around 80% of X-ray sources have an optical counterpart at the limiting
magnitude of our survey.
6.3
A   X-  T-2 AGN
6.3.1 Optical identification
In this section we identify optical counterparts for the point-like X-ray sources in the sample described in Sec. 6.2, and using the SWIRE infrared data, we associate infrared flux density measurements to these optical objects. We follow the method of Tasse et al. (2007a) who used a modified
version of the likelihood ratio method (Sutherland & Saunders 1992). This likelihood ratio method
is discussed in detail in Tasse et al. (2007a), but for completion we briefly describe the technique
here.
The likelihood ratio is defined as the probability that the X-ray source has its true optical
candidate detected and laying at a distance r, over the probability that the given optical candidate
is a background or foreground source. As we do not possess a priori knowledge on the properties
of the optical counterparts of X-ray sources, the likelihood ratio is first estimated only using the
a priori probability that an X-ray counterpart has a magnitude m. This information is derived
through Monte-Carlo simulations and we find that nearly 80% of the X-ray sources have an optical
counterpart in the CFHTLS optical data (Fig. 6.2).
However, it appears that taking into account only the information on magnitude, drives a contamination effect by background sources (see discussion in Tasse et al. 2007a). The second step
consists in correcting for this effect, by using the identified population source list to extend the a
priori knowledge of the optical counterparts of X-ray sources to parameters other than the magni-
134
Host galaxies and environment of active galactic nuclei
tude (stellar mass, redshift, and star formation rate). Monte-Carlo simulations are used to separate
the properties of the backgroud sources from the intrinsic properties of optical hosts of X-ray
sources. This aspect is discussed in detail in Tasse et al. (2007a). For each X-ray source we obtain
a probability of association with its 5 closest optical objects.
In order to conduce an association between infrared and the optical counterparts of X-ray
sources, we follow Surace et al. (2004), and require them to be closer that 1.5′′ . The source density
in the SWIRE DR2 band merged catalog is ∼ 3.2 104 deg−2 . Assuming a Poisson statistics, the
chance of asscociation with a random background source is ∼ 2%. Of the sources associated with
an optical counterpart in the CFHTLS data, 78% are also associated with an infrared source as
detected by IRAC.
6.3.2 Spectral energy distribution fitting and sample selection
In Tasse et al. (2007a) we fit the u∗ g’r’i’z’ and IRAC flux density measurements with spectral energy distribution (SED) templates for the 2×106 galaxies detected in the CFHTLS optical data. We
have used two complementary SED fitting methods. The first method uses ZPEG, whose SED template library was built from the stellar synthesis model of Le Borgne & Rocca-Volmerange (2002).
For each of the best fitting templates, ZPEG returns estimates for the redshift, stellar masses, and
specific star formation rate (sSFR0.5 hereafter). Because the stellar synthesis model does not take
into account the dust emission, we have used only the u∗ g’r’i’z’ magnitude measurements to constrain the SED fitting. The second approach uses the SWIRE template library of Polletta et al.
(2006), which was built from both observations and theoretical modelling. This library contains
both normal galaxy templates and optically active AGN templates such as QSO type 1. The combination of these two methods allows us to (i) obtain a good understanding of the overall content
of our sample and (ii) reject the objects for which the ZPEG output parameters are unreliable.
In order to study the properties of X-ray AGN using the photometric redshifts, stellar masses,
and sSFR0.5 as estimated by ZPEG, we first need to determine and remove various contamination
effects. There are two main sources of contamination: (i) the CFHTLS u∗ g’r’i’z’ photometry
that has been used for the physical parameter estimates can be either corrupted (eg. by saturated
regions of the CCD) or too noisy for a reliable physical parameter estimate and (ii) the X-ray
selected population is known to be biased toward a population of optically active AGN, which
means that the photometric redshift will not be reliable for a significant fraction of the sample.
Our purpose is to study the physical parameter estimates of the X-ray emitting AGN population,
and thus we select a subsample of type-2 X-ray sources for which the photometric redshifts and
associated parameters estimates are reliable. We follow the method of Tasse et al. (2007a) who
have discussed in detail the criteria used to select Type-2 radio sources’ hosts: the rejected AGN
are sources best fit by a type-1 AGN template (open circles in Fig. 6.5), or the sources lying in the
dashed area of Fig. 6.5. The remaining sample has 18 < i < 24 and we estimate the remaining
contamination is approximately ∼ 1.8%.
The X-ray spectra of Type-2 AGN can show strong absorption at lower energies, due to the
presence of obscuring material in the line of sight (see Sec. 6.2.1). Following Tajer et al. (2007),
we define the hardness ratio as HR = (CRH − CRS )/(CRH + CRS ), where CRH and CRS are the
count rates in the hard [2-10] and soft [0.5-2] keV band, respectively. Fig 6.3 shows the distribution
of HR for the sources selected as contaminating or normal as described above. On average, the
Internal and environmental properties of X-ray selected AGN.
135
Figure 6.3: Hardness ratio distribution for the sources that have been selected (Type-2) and rejected (Type1). As is expected the sources showing signs of absorption in the optical and infrared domains, have higher
hardness ratios.
rejected AGN population has lower hardness ratios than the population of normal galaxies. As
expected, these results indicate that we selected Type-2 AGN, that have a higher hardness ratio
due to their higher column density (Tajer et al. 2007).
6.3.3 Extinction correction
In this section we estimate the hydrogen column density of the obscuring material in each of the
individual sources, and derive their intrinsic luminosities.
Sazonov & Revnivtsev (2004) have estimated column densities from the flux ratio F[8−20] /F[3−8] ,
where F[8−20] and F[3−8] are the flux measurements in the [8-20] and [3-8] keV X-ray bands respectively. They have shown that these estimates are good first-order approximations of the estimate derived from fitting the X-ray spectra with an absorption model. Using the X-ray pipeline
XSPEC, we follow a similar approach. We assume an X-ray AGN spectrum Fν ∝ ν−0.8 at a redshift z, absorbed with an equivalent hydrogen column density of nH (model named “zphabs*pow”
in XSPEC). From this model, we compute the observed ratio F[0.5−2] /F[2−10] in the {z, nH } parameter
space, where F[0.5−2] and F[2−10] are the fluxes measured in the soft and hard X-ray band respectively
(Sec. 6.2.1). We have further used the estimates of the hydrogen column density nH to convert the
observed luminosities to intrinsic luminosities.
Fig. 6.4 shows the estimated column density for the sample of optically selected Type-2 AGN.
As is expected, the population of selected ojects (Sec. ??) has higher hydrogen column density.
Tajer et al. (2007) have derived the column densities for optically selected Type-1 and Type-2
sources, by fitting the X-ray spectra with a photo-absorption model. In their sample of ∼ 130 Xray AGN they find that the objects selected as obscured by optical criterion, 63±18% and 36±12%
136
Host galaxies and environment of active galactic nuclei
Figure 6.4: Based on the observed flux ratio F[0.5−2] /F[2−10] , assuming a underlying Fν ∝ ν−0.8 X-ray
spectra, we have estimated the hydrogen column density for the sample selected in Sec. 6.3.2. As expected,
most of the AGN we have selected show signs of absorption in their X-ray spectra.
have nH > 1021 and nH > 1022 cm−2 , respectively, while only ∼ 20% of the objects classified as
unobscured have nH > 1021 cm−2 . In our selected sample, we find that 83 ± 10% and 45 ± 7% have
nH > 1021 and nH > 1022 cm−2 , respectively. These estimates are in quite good agreement, even
though we are using a simplistic approach to estimate nH .
6.4
P  X-  AGN
6.4.1 Basic properties of X-ray selected AGN
In this section we discuss the properties of the optical and infrared counterparts of X-ray sources
identified in Sec. 6.3.1, and compare them with the our radio loud AGN sample (Tasse et al.
2007a).
The top panel of Fig. 6.5 shows the location of the optical counterpart of the X-ray and radio
AGN (Tasse et al. 2007a) in a g’-r’ versus r’-i’ color-color diagram. The dashed area indicates the
selection criteria that have been used to classify the sources as Type-1, whereas the open circles
show the sources classified as contaminating, using the spectral fits as described in Tasse et al.
(2007a). Clearly, X-ray selected AGN optical counterparts show greater differences with the normal galaxy population than with the optical hosts of radio loud AGN. The major fraction of the
X-ray selected AGN (∼ 51%) lie inside the Type-1 area, versus a ∼ 12% fraction for the radio
loud AGN. A significant fraction of X-ray AGN that are located outside that area are classified as
contaminating (∼ 11%) by the spectral fit criteria. These objects were, in general, best fit by a
template having both starburst and bright AGN components.
Internal and environmental properties of X-ray selected AGN.
137
Figure 6.5: Top panel: The g’-r’ versus r’-i’ color-color diagram for the optical counterparts of X-ray
sources (black dots). Open circle indicate the optical counterparts of X-ray sources that have been classified
as Type-1 AGN by the spectral fit criteria using the IRAC bands. The dashed area indicates the region
corresponding to the optical selection criteria used to reject the contaminating Type-1 AGN. Radio sources’
hosts and X-ray optical counterparts clearly occupy different regions of this plot, with the radio loud AGN
being hosted by galaxies that do not show strong signs of AGN activity in the optical. Bottom panel: The
[3.6]-[4.5] versus [5.8]-[8.0] infrared color-color diagram. Stern et al. (2005) find ∼ 90% of the broad-line
AGN lying in the area delimited by the dotted line. The sources marked as open circle have been rejected
from our sample.
138
Host galaxies and environment of active galactic nuclei
The bottom panel of Fig. 6.5 shows the [3.6]-[4.5] versus [5.8]-[8.0] infrared color-color plot.
Stern et al. (2005) argues that 90% of the broad-line AGN lie within this area, as well as ∼ 40%
of the narrow-line AGN, and 7% of normal galaxies. We find that ∼ 51% of our sources that have
flux density measurement in all the IRAC bands lie within this region, while this fraction goes to
∼ 95% for the sources best fit by a Type-1 galaxy template, in agreement with the estimate of Stern
et al. (2005). However ∼ 60% of the sources classified as Type-2 lay in this region, in contrast
with the ∼ 20% found for the radio selected AGN (Tasse et al. 2007a).
6.4.2 Luminosity function
Figure 6.6: We have estimated the X-ray luminosity function in the 0.1 < z < 1.2, 0.1 < z < 0.5 and
0.5 < z < 1.0. Our estimates are in good agreement with Steffen et al. (2003) and Sazonov & Revnivtsev
(2004) at low redshifts.
In order to derive comoving number density estimates, we use the 1/Vmax estimator (Schmidt
1968), where Vmax is the maximum volume over which a given object is observable. We first
estimate the dependence of the effective area on the X-ray flux. For this, we follow Steffen et al.
(2003), and simply compare our source count at each flux to the source counts given within Cowie
et al. (2002). For each given X-ray source we then use XSPEC, as well as the estimated luminosity
and column density, to obtain the flux at each redshift, as well as the corresponding effective area.
We obtain Vmax by integrating between zmin and zmax the probed volume at each redshift. We have
estimated zmin and zmax for the optical data according to te method described in Tasse et al. (2007b).
Fig. 6.6 shows the X-ray luminosity function computed using the 1/Vmax estimator in the
0.1 < z < 1.2, 0.1 < z < 0.5 and 0.5 < z < 1.2 redshift ranges. Based on a sample of ∼ 150 sources
having spectroscopic redshifts, Steffen et al. (2003) have computed the X-ray luminosity function
for the ranges 0.1 < z < 0.5 and 0.5 < z < 1.0 in the [2-8] keV band. In order to compare our
results with theirs, we assume X-ray spectra with Fν ∝ ν−0.8 , and derive an L[2−8] -L[2−10] conversion
Internal and environmental properties of X-ray selected AGN.
139
factor. Our estimates are in good agreement with Steffen et al. (2003) for both the high and the low
redshift ranges. We also compare our estimate of the X-ray luminosity function with the estimate
of Sazonov & Revnivtsev (2004) at z . 1 in the [3-20] keV energy band. In order to to this, we
estimate the conversion factor as described previously. Our results are in good agreement and are
further discussed in Sec. 6.5.
6.4.3 Stellar mass function
Using the number density estimator described in Sec. 6.4.2, we have computed the mass function
(φX ) for the host galaxies of X-ray AGN and the fraction fX of galaxies that are X-ray AGN above
a certain X-ray luminosity. This is simply computed as fX = φX /φOpt , where φOpt is our estimate
of the mass function for normal galaxies. Fig. 6.7 shows the estimates of fX for the redshift ranges
0.1 < z < 0.6 and 0.6 < z < 1.2 and for X-ray luminosities LX > 1043 erg.s−1 .
For comparison, we have plotted the low redshift z . 0.3 fraction of AGN versus mass relation,
with emission line luminosities LO[III] > 106.5 L⊙ and LO[III] > 107.5 L⊙ , and with 1.4 GHz radio
luminosities P1.4 > 1024 W.Hz−1 and P1.4 > 1025 W.Hz−1 (Best et al. 2005). Interestingly, the slope
of the fX ∝ M ∼1.5 relation is in good agreement with the AGN fraction versus the stellar mass
relation for the emission line AGN, but disagrees with the relation for the radio selected AGN. In
order to directly compare our X-ray luminosities with emission line luminosities, we use the L[3−20]
versus LO[III] relation given by Heckman et al. (2005) in the [3-20] keV band (log(L[3−20] /LO[III] ) =
2.15), with a conversion factor between the [3-20] and [2-10] keV bands (assuming an X-ray
spectra with Fν ∝ ν−0.8 ). The LX > 1043 erg.s−1 Xray luminosity we consider here correspond to
[OIII] line luminosities of LO[III] > 107.5 L⊙ . In the lower redshift bin the difference is as high
as ∼ 1.5 dex, while in the higher redshift bin the shape of the fX − M relation follows that of the
emission-line AGN, with an average difference of 1 dex. Differences are to be expected: Heckman
et al. (2005) have shown that X-ray selection criteria miss a significant fraction of emission-line
AGN. Specifically, at z . 0.1 the AGN luminosity function using emission lines and X-ray criteria
are different by ∼ 0.5 dex (Heckman et al. 2005). In the framework of the unified scheme of
AGN these differences were often suggested to be due to the existence of an AGN population
heavily obscured in the X-ray regime (Levenson et al. 2002). The difference of ∼ 1 − 1.5 dex we
observe between comoving number density of the X-ray selected AGN, and that of emission line
AGN is higher than that observed by Heckman et al. (2005). This is to be expected however, as
we have rejected a significant fraction of X-ray AGN, classified as contaminating Type-1 (∼ 30%),
corresponding to 0.15 dex. Furthermore, Heckman et al. (2005) used the X-ray luminosity function
as estimated from harder [8-20] keV X-rays, meaning more objects are detected because the X-rays
are less absorbed at those higher energies. These results are further discussed in Sec. 6.5.
6.4.4 Infrared properties
In order to study the infrared properties of the optical hosts of X-ray AGN, leading on from Tasse
et al. (2007b), we have derived an infrared excess parameter at 3.6, 4.5, 5.8, 8.0 µm. This excess
is calculated in the observer frame, using the Z-PEG best fit template (which does not take into
account dust infrared emission), and by computing the difference of the observed flux density to
the flux density of the stellar population as deduced using the u∗ g’r’i’z’ magnitude measurements.
140
Host galaxies and environment of active galactic nuclei
Figure 6.7: The fraction of of galaxies that are X-ray AGN with L[2−10] > 1043 erg.s−1 , as a function of the
stellar mass, in the 0.1 < z < 0.6 and 0.6 < z < 1.2 redshift ranges. The slope of the relationship shows good
agreement with the fraction of galaxies that satisfy AGN based emission line criteria, while it disagrees with
this relation for radio selected AGN.
In order to compare the infrared excess of the optical hosts of AGN to the infrared excess of the
normal galaxy population, we compute the difference between their infrared excesses ∆IR in similar
stellar mass and redshift bins (see Tasse et al. 2007b, for details). Fig. 6.8 shows ∆IR for the the
X-ray selected AGN using different stellar mass bins. As suggested by the distribution of these
sources in the infrared [3.6]-[4.5] versus [5.8]-[8.0] color-color plot (Fig. 6.5), X-ray selected
AGN show an infrared excess at short wavelength.
Internal and environmental properties of X-ray selected AGN.
141
Figure 6.8: Following Tasse et al. (2007b) we have computed the infrared excess for the normal galaxies
and for the host galaxies of X-ray selected AGN. This plot shows the difference in infrared excess between
these two populations. The X-ray selected AGN show a hot infrared excess, at short wavelengths.
6.4.5 Environment
In Tasse et al. (2007b) we described an overdensity parameter that is based on the photometric
redshifts probability functions. It gives the significance of the number density found arround a
given object at a given comoving scale. Following Tasse et al. (2007b), we have computed this
overdensity parameter at 75, and 450 kpc for the sample of X-ray selected AGN, and compare the
overdensity of the AGN population with the overdensities of the normal galaxies. As discussed
in Tasse et al. (2007b), the estimated overdensity may be biased toward lower values when the
redshift increases. In order to compare the environment of distinct population, for each X-ray
selected AGN, we compute the quantity ∆ρ = ρi − q0.5 [ρN (dz, dM)], where ρi is the estimated
overdensity of the given object and q0.5 [ρN (dz, dM)] is the median overdensity of non-radio-loud
galaxies that have comparable stellar mass, and redshift estimates. In practice, we take redshift
bin dz = 0.1, and mass bin dM = 0.2. Fig. 6.9 shows the median value of ∆ρ for the X-ray AGN
situated in given stellar mass range.
The environment of X-ray AGN is quite different from the environment of normal galaxies.
The X-ray AGN are preferentially situated in large scale underdensities ∆ρ450 ∼ −0.4, while they
seem quite insensitive to the small scale environment.
6.5
S  
In this paper we have proceeded to optically identify a sample of ∼ 1000 point-like X-ray sources
in the XMM-LSS field, leading to a fraction of X-ray sources having an optical counterpart of
∼ 80%. In order to reject the Type-1 AGN for which we cannot retrieve reliable photometric
142
Host galaxies and environment of active galactic nuclei
Figure 6.9: We have computed the overdensity parameter for the normal galaxies and for the X-ray selected
AGN. This figure shows the differences between these two populations: the X-ray selected AGN lay in large
scale (450 kpc) underdense environments.
redshifts estimates, we have followed, in detail, the method described in Tasse et al. (2007a). We
estimate that the remaining contamination by Type-1 AGN is on the level of ∼ 2%. In order to
correct for the extinction by dust in the line of sight, we have estimated the hydrogen column
density. A significant fraction of our selected sample (& 50%) shows such absorption in the X-ray,
with column densities nH > 1022 cm−2 (Fig. 6.4). These estimates are in quite good agreement
with results from previous surveys (Tajer et al. 2007). Based on these estimates, we have corrected
for the extinction, and estimate the intrinsic X-ray luminosities. The main results from this work
are as follows:
(i) The X-ray luminosity function of the X-ray selected AGN sample in the redshift ranges 0.1 <
z < 0.5 and 0.5 < z < 1.0 (Fig. 6.6) shows good agreement with previous measurements
(Steffen et al. 2003), with a strong comoving number density evolution at L[2−10] > 1043
erg.s−1 .
(ii) The fraction fX of galaxies that are X-ray luminous with LX > 1043 erg.s−1 have a strong
stellar mass dependence with fX ∝ M 1.5 , which is similar to the slope found for the emission line AGN (Best et al. 2005). By using an X-ray versus [OIII] luminosity relationship
(Heckman et al. 2005), we found that the comoving number density of X-ray selected and
emission line selected AGN (Best et al. 2005) differ by ∼ 1 dex at low redshift.
(iii) Compared with normal galaxies of the same mass, X-ray selected AGN show an infrared
excess in the IRAC 3.6, 4.5, 5.8 and 8.0 µm bands and over the full mass range (Fig. 6.8).
(iv) Compared with normal galaxies of the same mass, X-ray selected AGN are preferentially
found in underdense large-scale (450 kpc) environments over the full stellar mass range.
Internal and environmental properties of X-ray selected AGN.
143
Their small 75 kpc overdensities is similar to the overdensities found arround normal galaxies.
Many authors have suggested that there are indeed two different modes of accretion: the Quasar
mode is radiatively efficient, while on the other hand the Radio mode is radiatively inefficient (see
Sec. 6.1 for a discussion). In Tasse et al. (2007b), based on a sample of radio selected AGN, we
have argued that the accretion mode in the most massive galaxies has low efficiency. These sources
may have their AGN activity triggered by the cooling of the hot gas that is observed in their atmosphere (Mathews & Brighenti 2003; Best et al. 2005). Conversely, the lower stellar mass systems
(M < 1010.5−11.0 M⊙ ) show signs of actively accreting black holes. We have argued that their AGN
activity is triggered by major galaxy mergers and interaction in underdense environments. In the
following, we argue that X-ray selected AGN may correspond to the radiatively efficient Quasar
mode.
Result (ii) above shows that, as expected, the fraction of galaxies that are X-ray AGN is a
strong function of stellar mass (Fig. 6.7). However, using an L[OIII] − LX relationship (Heckman
et al. 2005), the number densities of X-ray selected and emission line selected AGN show strong
differences. We have argued in Sec. 6.4.3 that this effect is indeed expected as many Type-2
emission line AGN are not seen to produce significant X-ray flux, even in the hard [2-10] keV Xray bands (Heckman et al. 2005). This is often interpreted as sources that are heavily absorbed and
even compton thick (Levenson et al. 2002). Although we have corrected for the intrinsic absorption
in each individual source, this suggests that there are many obscured X-ray sources, that we do not
detect. However, the slope of the relation between stellar mass and fraction of X-ray selected
AGN ( fX ∝ M 1.5 ) is in relatively good agreement with the relation between the fraction of galaxies
that are classified as AGN using emission line criteria, while it disagrees with the fraction fRad
of radio loud AGN versus stellar mass relation fRad ∝ M 2.5 . This suggests that although we are
missing a significant fraction of the X-ray luminous AGN population, we are indeed selecting the
same AGN galaxy population of emission line AGN that have been recognised as AGN which
have radiatively efficient accretion (“Quasar mode”, Heckman et al. 2004; Best et al. 2005). This
picture is supported by the result (iii) on the infrared properties of X-ray selected AGN: these
objects have a hot dust component at wavelength as short as 3.6 µm (observer frame), which
are often interpreted as being due to an actively accreting black hole where UV light heats the
surrounding dust at temperatures of 500 − 1000 K (Seymour et al. 2007). Result (iv) suggests that
these AGN are preferentially located in large 450 kpc scale underdensities at levels of ∼ −0.5.
Consistently, luminous L[OIII] > 107 L⊙ emission line AGN are preferentially found in underdense
environment (Kauffmann et al. 2004; Best et al. 2005).
The internal and environmental properties of this X-ray selected AGN population are very
similar to the characteristics of the low stellar mass radio selected AGN population. Both of these
classes of AGN have a rather flat fraction mass relation ( f ∝ M 1.5 ), an infrared excess, and they lie
in large scale underdense environments. These factors suggest that X-ray, optical, and low-mass
radio AGN are indeed similar populations, which are dominated by quasar mode AGN. However,
environmental differences are found at the smaller scale: contrary to the radio AGN, the X-ray
selected AGN seem quite insensitive to their small 75 kpc scale overdensities.
It has often been proposed that luminous AGN activity is triggered by the galaxy mergers and
interactions, and this process has been suggested to occur more frequently in underdense environments (eg. Gómez et al. 2003; Best 2004). Galaxy mergers and interactions, as the dominant
144
Host galaxies and environment of active galactic nuclei
triggering processes for these AGN, provide a natural explanation to the underdensities found
around X-ray selected, and low-mass radio-selected AGN. The differences found in the small 75
kpc scale environment might be caused by various effects. If the AGN are triggered by a major
merger, there might be an observational sequence that AGN follow during their lifetime: while
the radio emission is seen to be associated with small scale overdensities, it might be that during
the X-ray emitting phase, the two interacting galaxies have already merged into a single system.
Alternatively, Taniguchi (1999) suggests that minor mergers that are produced by the interaction
between a given galaxy and a low mass satellite galaxy can play an important role in triggering
AGN activity. In such cases our dataset would certainly not allow us to detect a galaxy pair as a
small 75 kpc overdensity. It could be that the intergalactic medium gas state is different in these
underdense regions, and actually favours such scenarios.
A
The optical images were obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA,
at the CFHT which is operated by the National Research Council (NRC) of Canada, the Institut National
des Sciences de l’Univers of the Centre National de la Recherche Scientifique (CNRS) of France and the
University of Hawaii. This work is based on data products produced at TERAPIX and at the Canadian
Astronomy Data Centre as part of the CFHTLS, a collaborative project of NRC and CNRS.
R
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A
A
T
146
Table A1: Properties of the selected X-ray sources.
Name
X-ray
DEC
(J2000)
−04 29 49.39
−04 28 02.55
−04 24 47.10
−04 08 00.60
−04 03 03.99
−04 03 00.08
−04 56 48.34
−04 10 06.14
−04 39 00.03
−04 16 20.98
−04 37 27.99
−04 37 25.61
−04 49 34.80
−05 19 39.41
−04 45 57.38
−05 29 26.75
−03 48 41.91
−05 39 33.64
−04 41 30.36
−04 20 32.88
−04 56 14.43
−04 11 50.13
−04 32 41.75
−03 54 14.10
−04 26 00.53
−03 56 01.69
−05 29 16.17
−04 13 45.89
−04 19 55.89
−05 25 09.44
−04 31 01.99
−04 12 51.26
−03 46 13.88
−04 16 56.34
−04 25 17.51
−04 09 28.72
−04 18 29.93
−04 18 28.78
−05 20 37.46
−04 13 43.17
−05 00 14.52
−05 00 16.43
−04 59 17.67
−04 55 07.10
−04 07 55.44
−04 48 42.01
−05 09 42.54
−05 09 44.42
−04 41 36.29
−05 07 12.51
−04 14 57.64
−03 55 46.13
−04 37 41.94
−05 08 17.13
−04 32 48.39
log(FS )
log(FH )
−14.18
−13.85
−13.29
.
.
.
−13.81
.
.
−14.70
−14.19
.
−13.55
.
−14.35
−14.37
.
−14.08
.
−14.77
.
−14.06
−14.58
.
−14.30
−13.53
−14.23
−14.75
.
−14.75
−13.77
.
.
.
.
.
−14.11
−14.07
−13.87
−14.30
−14.08
−13.85
−14.09
−14.22
.
−14.36
−13.88
−12.92
.
−14.94
−13.97
−14.10
−14.51
−14.58
.
−13.50
−13.48
−13.04
−13.84
−13.82
−13.80
−13.49
−13.77
−13.64
−13.83
−13.23
−13.38
−13.26
−13.18
−14.05
−13.96
−13.74
−13.12
−13.89
−13.79
−13.84
−13.74
−13.81
−13.78
−13.71
−13.24
−13.74
−14.02
−13.67
−13.94
−13.67
−13.80
−13.81
−13.67
−14.01
−13.62
−13.82
−13.37
−13.57
−13.68
−13.55
−13.40
−13.43
−13.96
−13.69
−13.90
−13.43
−12.55
−13.81
−14.22
−13.28
−13.54
−13.87
−13.81
−13.77
RA
(J2000)
02 19 15.83
02 19 41.78
02 19 52.37
02 19 59.36
02 20 15.73
02 20 16.16
02 20 16.84
02 20 20.10
02 20 22.97
02 20 26.26
02 20 27.18
02 20 27.18
02 20 27.65
02 20 29.86
02 20 32.69
02 20 39.66
02 20 41.27
02 20 43.33
02 20 48.24
02 20 52.34
02 20 53.00
02 20 57.96
02 20 58.19
02 21 02.12
02 21 02.27
02 21 05.15
02 21 05.26
02 21 16.02
02 21 16.21
02 21 17.03
02 21 17.96
02 21 19.06
02 21 21.38
02 21 22.68
02 21 24.49
02 21 24.81
02 21 36.57
02 21 36.57
02 21 42.63
02 21 43.04
02 21 47.10
02 21 47.10
02 21 49.87
02 21 53.81
02 21 56.50
02 22 02.22
02 22 02.75
02 22 02.75
02 22 06.73
02 22 15.59
02 22 18.18
02 22 20.17
02 22 25.31
02 22 29.60
02 22 31.01
Optical
DEC
u
g
(J2000)
(AB)
(AB)
−04 29 50.11 23.54
23.11
−04 28 06.34 25.61
25.08
−04 24 48.75 22.57
21.59
−04 08 02.06 24.18
24.15
−04 03 07.64 24.72
24.97
−04 02 59.49 24.30
23.00
−04 56 46.16 23.63
22.70
−04 10 06.21 24.73
22.85
−04 39 03.00 25.34
24.72
−04 16 23.50 21.97
20.62
−04 37 25.22 24.33
23.87
−04 37 25.22 24.33
23.87
−04 49 33.40 22.63
22.62
−05 19 39.55 22.84
21.84
−04 45 58.06 22.85
22.45
−05 29 28.42 21.21
20.40
−03 48 41.77 22.62
21.07
−05 39 34.30 > 24.90 22.19
−04 41 30.42 > 24.70 26.39
−04 20 32.93 23.45
22.53
−04 56 16.38 26.62
24.17
−04 11 52.20 25.15
24.26
−04 32 43.34 23.96
22.72
−03 54 13.40 22.46
21.34
−04 26 01.13 23.05
22.51
−03 56 01.07 21.36
20.37
−05 29 18.12 24.44
24.03
−04 13 44.22 22.86
22.02
−04 19 54.92 23.93
23.31
−05 25 09.51 23.86
22.18
−04 31 01.58 21.39
21.78
−04 12 45.92 23.84
23.97
−03 46 13.73 24.11
22.80
−04 16 53.12 24.18
23.97
−04 25 17.66 21.82
20.25
−04 09 30.80 24.57
24.49
−04 18 28.67 26.02
25.62
−04 18 28.67 26.02
25.62
−05 20 37.67 21.88
21.25
−04 13 43.65 24.04
24.06
−05 00 16.12 24.82
23.75
−05 00 16.12 24.82
23.75
−04 59 18.61 24.13
23.59
−04 55 10.17 23.84
23.52
−04 07 57.82 21.00
20.66
−04 48 41.66 22.22
20.66
−05 09 44.39 20.30
19.77
−05 09 44.39 20.30
19.77
−04 41 38.48 22.31
20.67
−05 07 11.23 > 24.70 24.41
−04 14 57.82 24.37
23.16
−03 55 47.66 23.60
23.69
−04 37 44.25 23.58
22.40
−05 08 18.14 21.91
20.79
−04 32 48.54 23.44
22.83
IR
r
(AB)
22.28
24.24
20.51
23.49
23.34
21.53
21.44
21.63
> 25.00
19.31
23.38
23.38
> 25.00
20.75
21.97
19.54
19.64
20.75
24.94
21.47
24.16
23.21
21.80
20.67
21.79
19.49
23.34
21.13
22.53
20.93
20.90
23.18
> 24.90
24.07
19.09
23.52
23.92
23.92
20.83
23.37
22.83
22.83
23.04
23.86
20.35
19.84
19.03
19.03
19.76
24.15
22.60
23.09
21.46
20.07
> 24.90
i
(AB)
22.07
22.94
19.79
22.67
22.46
20.41
20.77
20.76
22.49
18.68
22.44
22.44
21.55
20.25
21.24
19.06
18.82
20.00
23.77
20.60
23.02
21.98
20.73
20.37
21.14
18.98
22.60
20.07
21.94
20.39
20.64
22.43
20.38
23.25
18.57
22.81
23.20
23.20
20.34
21.86
22.62
22.62
22.32
23.12
19.40
19.40
18.62
18.62
19.25
22.81
22.18
22.19
20.56
19.48
21.07
z
(AB)
> 23.60
21.91
19.39
22.21
21.84
20.05
20.44
20.42
21.90
18.35
22.15
22.15
21.49
19.85
20.80
18.76
18.52
19.68
23.16
20.20
23.23
21.40
20.23
20.16
20.70
18.66
20.88
19.59
21.90
20.15
20.39
21.52
19.91
23.45
18.20
22.37
23.39
23.39
20.15
21.80
24.20
24.20
22.19
23.05
20.53
19.25
18.25
18.25
18.90
21.52
22.08
21.71
20.29
19.34
20.75
3.6µm
(AB)
19.37
> 21.03
18.22
> 21.03
13.78
13.78
19.64
18.53
0.00
18.17
0.00
> 21.03
19.58
19.59
19.17
18.19
18.03
18.16
19.89
18.73
19.54
19.35
19.22
20.78
19.15
17.69
20.00
18.60
> 21.03
19.54
19.07
0.00
15.75
0.00
17.62
19.94
> 21.03
> 21.03
18.72
19.51
19.59
19.59
18.70
20.34
18.37
19.32
17.65
17.65
> 21.03
> 21.03
> 21.03
21.12
18.41
18.09
19.23
4.5µm
(AB)
19.06
> 20.96
18.17
> 20.96
14.29
14.29
19.67
18.45
0.00
18.25
0.00
> 20.96
18.99
19.73
19.41
18.15
18.27
18.00
19.63
19.01
19.36
19.53
19.65
21.00
19.32
17.54
20.02
18.89
> 20.96
19.64
19.06
0.00
16.24
0.00
17.33
19.60
> 20.96
> 20.96
18.72
19.71
19.30
19.30
18.17
20.09
17.68
19.76
17.42
17.42
> 20.96
> 20.96
> 20.96
21.20
18.37
17.88
19.23
5.8µm
(AB)
18.69
> 19.84
17.84
> 19.84
14.70
14.70
19.34
18.14
0.00
18.53
0.00
> 19.84
18.69
> 19.84
19.82
18.34
18.30
17.83
> 19.84
19.07
19.11
> 19.84
19.73
> 19.84
> 19.84
17.43
> 19.84
18.96
> 19.84
19.66
18.71
0.00
16.64
0.00
16.96
> 19.84
> 19.84
> 19.84
18.44
19.39
19.78
19.78
17.81
19.72
16.88
> 19.84
17.21
17.21
> 19.84
> 19.84
> 19.84
> 19.84
18.12
17.59
18.98
8.0µm
(AB)
18.14
> 19.53
17.80
> 19.53
15.33
15.33
19.50
17.75
0.00
18.30
0.00
> 19.53
18.03
> 19.53
> 19.53
17.72
18.36
17.63
> 19.53
18.75
19.31
> 19.53
19.86
> 19.53
19.17
17.15
> 19.53
18.82
> 19.53
19.45
18.38
0.00
17.25
0.00
16.38
19.24
> 19.53
> 19.53
18.33
> 19.53
18.98
18.98
17.30
> 19.53
16.18
> 19.53
16.73
16.73
> 19.53
> 19.53
> 19.53
> 19.53
17.64
16.82
18.58
z
0.42
1.14
0.54
0.94
0.87
0.70
0.48
0.57
0.96
0.46
0.77
0.77
0.86
0.42
0.93
0.28
0.56
0.50
0.93
0.59
0.82
0.96
0.73
0.13
0.68
0.26
1.18
0.79
0.62
0.22
0.67
1.12
0.54
0.87
0.18
0.93
0.61
0.61
0.71
0.82
0.35
0.35
0.71
0.43
0.73
0.19
0.28
0.28
0.11
1.19
0.11
0.98
0.60
0.60
0.68
log(M)
[M⊙ ]
9.32
10.49
10.97
10.18
10.75
10.95
10.57
10.49
10.20
11.35
10.26
10.26
9.82
10.70
10.85
10.74
11.15
10.77
9.82
10.85
9.20
10.40
11.15
9.18
10.59
10.71
10.81
11.43
9.85
10.13
10.15
11.17
11.10
9.12
10.74
10.27
9.23
9.23
10.36
9.97
8.75
8.75
9.80
7.26
10.55
9.92
10.78
10.78
9.97
10.62
8.52
10.00
10.78
11.01
10.61
Phys. prop.
log(S )
nH
[yr−1 ]
[cm−2 ]
−8.92
22.3
−∞
22.3
−10.25
21.6
−9.31
22.1
−10.85
22.1
−∞
22.1
−10.85
21.7
−∞
22.0
−∞
22.5
−10.85
22.5
−9.59
22.8
−9.59
22.7
−8.93
21.9
−10.12
22.6
−9.41
22.0
−9.99
21.8
−∞
22.1
−∞
22.6
−∞
22.0
−10.49
22.7
−8.93
22.1
−∞
22.1
−10.85
22.6
−10.09
21.7
−9.51
22.4
−10.23
21.5
−∞
22.5
−10.38
22.6
−9.17
22.3
−∞
22.3
−9.16
< 21.0
−10.38
22.3
−∞
21.9
−8.63
22.4
−∞
21.1
−9.67
22.5
−∞
21.8
−∞
22.5
−8.66
21.9
−8.90
22.5
−8.91
22.0
−8.91
21.9
−9.01
22.5
−8.74
21.6
−8.93
22.3
−∞
21.8
−9.75
21.9
−9.75
21.7
−∞
21.6
−∞
22.8
−9.87
22.0
−8.92
22.5
−10.25
22.4
−9.43
22.5
−9.89
22.1
L[2-10]
[erg.s−1 ]
43.3
44.4
44.0
43.8
43.8
43.6
43.5
43.4
44.1
43.1
44.3
44.1
44.3
43.7
43.6
42.4
43.4
43.9
43.8
43.4
43.7
44.0
43.6
41.9
43.6
43.1
44.2
43.5
43.6
42.3
43.6
44.1
43.3
43.9
41.9
44.1
43.4
43.9
43.8
43.9
43.1
43.2
44.0
42.9
43.7
42.1
43.0
43.9
41.7
43.7
42.2
44.2
43.3
43.4
43.6
Host galaxies and environment of active galactic nuclei
X888
X885
X880
X909
X899
X898
X826
X914
X789
X929
X788
X894
X806
X976
X802
X990
X940
X1000
X793
X934
X825
X921
X857
X943
X843
X946
X989
X925
X837
X983
X851
X923
X939
X930
X841
X913
X835
X877
X972
X872
X521
X956
X520
X518
X868
X508
X655
X964
X502
X650
X876
X717
X355
X653
X345
RA
(J2000)
02 19 15.85
02 19 41.80
02 19 52.30
02 19 59.19
02 20 15.59
02 20 16.18
02 20 16.74
02 20 20.06
02 20 23.28
02 20 26.37
02 20 27.43
02 20 27.30
02 20 27.57
02 20 29.63
02 20 32.58
02 20 39.44
02 20 41.36
02 20 43.14
02 20 48.29
02 20 52.13
02 20 52.81
02 20 58.19
02 20 58.23
02 21 01.78
02 21 02.41
02 21 05.21
02 21 05.48
02 21 16.04
02 21 15.95
02 21 17.00
02 21 17.90
02 21 18.98
02 21 21.35
02 21 22.95
02 21 24.23
02 21 24.64
02 21 36.54
02 21 36.38
02 21 42.68
02 21 42.82
02 21 46.98
02 21 46.96
02 21 49.86
02 21 53.62
02 21 56.61
02 22 02.31
02 22 02.70
02 22 02.67
02 22 06.81
02 22 15.43
02 22 18.28
02 22 20.09
02 22 25.52
02 22 29.55
02 22 31.06
X322
X354
X715
X637
X647
X320
X712
X474
X314
X119
X710
X152
X645
X498
X635
X470
X340
X471
X135
X144
X134
X117
X318
X624
X658
X472
X630
X296
X494
X668
X301
X272
X280
X297
X598
X306
X626
X274
X283
X728
X305
X273
X292
X730
X277
X481
X729
X282
X597
X106
X622
X303
X116
X604
X105
X603
X571
RA
(J2000)
02 22 31.52
02 22 34.42
02 22 36.49
02 22 38.20
02 22 40.11
02 22 40.89
02 22 41.00
02 22 44.79
02 22 50.26
02 22 50.73
02 22 51.37
02 22 51.63
02 22 52.10
02 22 58.78
02 22 58.80
02 22 59.93
02 23 01.93
02 23 02.28
02 23 05.92
02 23 06.82
02 23 07.59
02 23 09.94
02 23 15.39
02 23 18.30
02 23 18.09
02 23 19.54
02 23 19.60
02 23 21.20
02 23 21.90
02 23 22.17
02 23 35.26
02 23 39.34
02 23 40.33
02 23 46.97
02 23 47.30
02 23 49.33
02 23 50.18
02 23 51.18
02 23 51.83
02 23 52.83
02 23 54.36
02 23 55.86
02 23 56.62
02 23 59.72
02 24 03.84
02 24 03.97
02 24 03.91
02 24 05.34
02 24 08.54
02 24 15.56
02 24 18.23
02 24 18.41
02 24 20.94
02 24 26.22
02 24 28.80
02 24 28.73
02 24 30.59
X-ray
DEC
(J2000)
−04 26 43.44
−04 37 08.52
−03 53 43.79
−05 00 57.62
−05 06 56.33
−04 26 15.12
−03 50 37.63
−04 47 55.52
−04 22 54.15
−04 00 31.69
−03 48 43.35
−04 15 39.92
−05 06 21.67
−04 58 51.86
−04 58 52.26
−04 46 25.22
−04 32 04.64
−04 46 51.67
−04 08 35.99
−04 12 53.75
−04 08 18.38
−04 23 04.30
−04 25 58.45
−05 12 07.51
−05 12 09.09
−04 47 31.49
−05 14 18.33
−04 33 04.79
−04 57 38.97
−05 16 28.74
−04 36 12.29
−04 20 00.11
−04 24 20.20
−04 33 45.69
−05 00 27.08
−04 40 09.74
−05 13 11.52
−04 20 54.33
−04 24 36.27
−03 56 41.63
−04 39 16.01
−04 20 17.90
−04 31 16.73
−03 57 48.69
−04 21 58.29
−04 51 20.43
−03 57 26.07
−04 24 23.83
−04 58 55.62
−04 14 17.40
−05 10 34.24
−04 37 07.97
−04 19 56.72
−05 02 31.99
−04 14 15.13
−05 02 28.36
−05 08 38.91
log(FS )
log(FH )
−14.48
−14.05
−14.09
−14.28
.
−14.70
−13.43
.
−13.91
−13.97
−13.94
.
−14.64
−13.31
−13.31
.
−14.42
−14.40
−14.71
.
−14.25
−13.99
−14.37
−13.63
−13.60
−13.78
.
.
−14.62
−14.67
−14.38
.
−14.02
−14.17
−14.24
−14.50
−13.80
−14.45
.
−13.93
.
−14.37
−14.03
−14.18
−15.01
−13.86
−14.37
−14.73
−14.70
−13.86
−13.76
−14.25
−14.44
.
−14.22
−14.54
−13.93
−13.92
−13.74
−13.59
−13.38
−14.19
−14.13
−13.27
−13.96
−13.65
−13.54
−13.50
−13.88
−14.15
−13.29
−13.28
−14.05
−13.98
−13.83
−13.98
−13.89
−14.12
−13.77
−13.81
−13.34
−13.42
−13.45
−13.93
−13.68
−13.26
−13.47
−13.79
−13.71
−13.70
−13.86
−13.90
−13.80
−13.56
−13.62
−13.90
−13.54
−13.92
−13.70
−13.90
−13.73
−13.83
−13.61
−13.48
−14.10
−13.63
−13.70
−13.58
−13.92
−13.81
−14.02
−14.13
−13.90
−13.54
RA
(J2000)
02 22 31.39
02 22 34.48
02 22 36.60
02 22 38.18
02 22 39.82
02 22 40.84
02 22 41.01
02 22 45.01
02 22 50.31
02 22 50.88
02 22 51.45
02 22 51.57
02 22 52.14
02 22 58.88
02 22 58.88
02 22 59.88
02 23 02.06
02 23 02.26
02 23 05.91
02 23 06.84
02 23 07.60
02 23 10.02
02 23 15.33
02 23 18.34
02 23 18.34
02 23 19.60
02 23 19.72
02 23 21.16
02 23 21.99
02 23 22.43
02 23 35.57
02 23 39.27
02 23 40.38
02 23 47.03
02 23 47.41
02 23 49.50
02 23 50.32
02 23 51.24
02 23 51.79
02 23 52.84
02 23 54.21
02 23 55.95
02 23 56.67
02 23 59.68
02 24 03.72
02 24 04.05
02 24 04.09
02 24 05.49
02 24 08.48
02 24 15.76
02 24 18.39
02 24 18.39
02 24 20.85
02 24 26.26
02 24 28.77
02 24 28.99
02 24 30.56
DEC
(J2000)
−04 26 43.37
−04 37 09.76
−03 53 43.81
−05 00 59.34
−05 06 57.99
−04 26 14.73
−03 50 36.93
−04 47 51.56
−04 22 53.87
−04 00 32.20
−03 48 44.35
−04 15 41.15
−05 06 23.31
−04 58 52.35
−04 58 52.35
−04 46 26.54
−04 32 05.15
−04 46 52.03
−04 08 35.45
−04 12 53.67
−04 08 18.31
−04 23 04.03
−04 25 58.49
−05 12 08.46
−05 12 08.46
−04 47 30.89
−05 14 18.86
−04 33 04.19
−04 57 38.42
−05 16 27.09
−04 36 13.45
−04 20 05.08
−04 24 20.02
−04 33 48.00
−05 00 28.72
−04 40 11.00
−05 13 10.91
−04 20 53.33
−04 24 35.77
−03 56 42.44
−04 39 20.17
−04 20 18.41
−04 31 18.00
−03 57 47.28
−04 21 56.97
−04 51 18.51
−03 57 28.13
−04 24 23.73
−04 58 56.65
−04 14 16.66
−05 10 34.83
−04 37 06.08
−04 19 55.88
−05 02 31.80
−04 14 13.94
−05 02 24.14
−05 08 41.69
Optical
u
g
(AB)
(AB)
22.53
21.13
21.79
20.60
24.56
24.37
23.48
23.12
21.42
20.55
24.86
25.85
21.65
20.94
22.41
21.61
23.75
22.76
23.56
22.89
24.90
24.60
24.93
23.96
21.71
21.11
19.90
19.73
19.90
19.73
22.69
21.64
22.04
21.21
24.40
23.50
25.19
24.38
25.53
25.46
20.55
20.01
22.02
21.03
20.19
19.16
21.64
20.41
21.64
20.41
22.10
20.45
28.34
24.87
23.18
22.76
22.29
21.66
26.66
24.90
22.66
22.17
22.79
21.66
24.96
24.66
21.25
20.56
22.73
22.28
23.58
22.56
22.43
21.97
20.87
19.97
23.64
22.05
24.01
22.77
23.87
23.03
27.68
25.25
23.69
23.27
26.80
25.17
24.80
24.64
22.59
22.25
23.64
23.31
23.17
22.19
22.69
21.97
22.46
22.13
21.11
20.69
22.55
21.80
25.18
24.61
20.83
19.75
20.43
19.44
23.60
23.07
21.66
20.48
IR
r
(AB)
20.52
19.88
22.99
> 25.00
> 25.00
24.44
19.81
20.57
21.88
22.13
23.86
23.23
20.57
19.36
19.36
20.40
20.34
22.44
23.98
24.66
19.18
20.42
18.56
19.15
19.15
19.30
23.68
22.54
20.79
23.36
21.62
20.42
24.65
19.76
21.38
21.64
21.41
19.50
20.75
21.66
22.21
24.31
22.63
23.90
23.64
21.44
22.70
21.25
21.27
21.76
20.11
20.91
23.58
19.04
18.85
22.54
19.37
i
(AB)
19.49
19.39
22.33
21.06
19.33
23.98
19.27
19.84
20.96
21.35
23.14
22.25
19.86
19.10
19.10
19.56
19.78
21.28
22.81
23.10
18.85
20.04
18.21
18.57
18.57
18.69
22.74
21.72
19.65
22.10
21.28
19.80
23.40
19.23
21.01
20.69
21.00
19.22
20.01
21.10
21.33
23.56
22.13
22.81
22.66
21.14
21.57
20.47
20.53
21.23
19.78
20.04
22.38
18.64
18.49
22.02
18.86
z
(AB)
19.28
19.18
21.31
20.46
19.08
23.59
18.91
19.44
20.51
20.69
22.57
21.71
19.67
18.86
18.86
19.26
19.57
20.93
22.38
22.72
18.54
19.82
18.06
18.26
18.26
18.31
21.83
21.44
19.27
21.51
21.00
19.45
22.29
18.93
21.22
20.29
20.69
19.21
19.67
20.57
20.89
23.24
22.09
22.24
21.86
20.70
20.61
20.05
20.30
21.20
19.44
19.72
21.75
18.35
18.37
21.48
18.50
3.6µm
4.5µm
(AB)
(AB)
16.32
16.82
19.29
19.40
19.16
19.11
18.40
18.16
19.19
19.31
20.19
20.29
18.68
18.69
18.54
18.94
18.76
18.74
18.52
18.56
19.66
19.52
20.57
20.60
18.53
18.81
17.77
17.67
17.77
17.67
18.24
18.46
18.04
17.98
19.89
20.33
19.38
19.24
20.30
20.50
18.16
18.11
19.83
19.89
18.28
18.25
17.88
17.88
17.88
17.88
17.09
16.86
19.70
19.80
20.41
20.74
17.57
17.13
19.70
19.98
19.54
19.34
19.08
19.24
20.73
20.53
17.96
17.95
20.26
20.33
19.01
19.16
19.84
19.47
19.24
19.33
19.01
18.97
18.13
17.66
0.00
0.00
20.22
20.10
> 21.03 > 20.96
19.98
20.15
20.10
20.58
18.62
18.43
18.34
18.34
18.14
18.22
19.36
19.53
19.47
18.98
18.73
18.54
18.51
18.76
19.88
20.18
18.27
18.18
18.59
18.73
0.00
0.00
18.54
18.70
5.8µm
(AB)
17.28
> 19.84
19.07
17.82
> 19.84
> 19.84
18.98
19.49
18.47
18.29
19.59
> 19.84
18.65
17.40
17.40
18.14
17.79
> 19.84
19.63
> 19.84
18.06
> 19.84
18.11
18.02
18.02
16.67
19.75
> 19.84
16.75
> 19.84
19.05
19.52
> 19.84
17.81
> 19.84
19.09
19.45
19.58
18.74
17.27
0.00
> 19.84
> 19.84
> 19.84
> 19.84
18.26
18.34
18.12
19.28
18.36
18.48
18.71
19.85
18.19
19.23
0.00
19.02
8.0µm
(AB)
17.94
19.06
19.08
17.11
18.54
> 19.53
18.47
19.21
18.11
18.14
> 19.53
> 19.53
18.65
16.99
16.99
17.98
17.49
> 19.53
> 19.53
> 19.53
17.47
18.43
17.13
17.66
17.66
16.29
19.69
> 19.53
15.95
> 19.53
19.08
18.98
> 19.53
17.37
> 19.53
18.64
18.54
17.55
18.24
16.81
0.00
> 19.53
> 19.53
> 19.53
> 19.53
18.02
18.01
17.68
19.31
17.96
17.93
18.50
19.55
16.88
17.44
0.00
18.59
z
0.72
0.16
1.14
0.87
0.20
1.01
0.42
0.57
0.68
1.02
0.97
0.84
0.71
0.71
0.71
0.56
0.26
0.72
0.83
0.88
0.39
0.13
0.14
0.43
0.43
0.46
1.11
0.85
0.73
0.79
0.66
0.45
1.17
0.56
0.60
0.68
0.67
0.14
0.47
0.46
0.81
0.56
0.64
0.94
1.05
0.32
1.11
0.58
0.66
0.71
0.67
0.67
0.98
0.11
0.13
0.99
0.45
log(M)
[M⊙ ]
11.19
10.09
10.68
11.14
10.33
10.07
11.08
10.96
10.80
10.87
10.23
10.53
10.88
10.52
10.52
11.23
10.38
10.88
10.08
9.69
10.85
9.54
10.31
11.32
11.32
11.32
10.51
10.25
11.47
10.88
9.92
10.89
10.11
11.17
9.81
10.90
10.14
9.65
10.78
10.38
10.75
9.44
9.67
10.11
10.25
9.84
10.82
10.79
10.83
9.92
10.54
11.00
10.28
10.07
10.12
10.65
11.07
Phys. prop.
log(S )
nH
[yr−1 ]
[cm−2 ]
−9.67
22.4
−10.35
21.5
−∞
22.5
−10.73
22.8
−10.09
< 21.0
−9.59
22.5
−10.12
21.3
−10.09
21.6
−10.25
21.7
−9.87
22.4
−9.67
22.3
−10.09
22.0
−9.26
22.3
−8.63
< 21.0
−8.63
< 21.0
−10.85
21.1
−10.23
21.8
−10.85
22.4
−9.67
22.6
−8.90
22.0
−9.48
21.1
−9.90
21.3
−9.81
21.9
−10.85
21.7
−10.85
21.4
−10.49
21.8
−∞
22.0
−9.15
22.4
−10.38
23.1
−∞
23.0
−8.66
22.4
−10.85
22.1
−8.90
22.2
−9.59
21.8
−8.92
21.9
−10.25
22.5
−8.74
21.7
−9.71
22.2
−∞
21.7
−10.49
21.9
−9.89
21.8
−10.09
22.4
−8.83
21.2
−∞
22.3
−8.90
23.2
−9.60
21.5
−8.90
23.0
−10.25
22.4
−9.59
22.8
−8.62
21.4
−8.66
21.5
−9.89
21.9
−∞
22.6
−10.46
21.1
−9.81
< 21.0
−9.41
22.6
−10.09
21.9
L[2-10]
[erg.s−1 ]
43.5
42.1
44.3
44.3
41.9
43.6
43.5
43.2
43.7
44.2
44.2
43.7
43.2
44.1
44.1
43.1
42.4
43.6
43.6
43.7
42.6
41.9
41.9
43.5
43.4
43.5
43.9
43.9
44.2
44.1
43.5
43.2
44.2
43.3
43.3
43.5
43.8
42.1
43.0
43.4
43.6
43.4
43.4
44.0
44.1
42.9
44.4
43.1
43.7
43.7
43.7
43.4
43.9
41.5
41.5
43.9
43.4
References
Name
147
X-ray
DEC
(J2000)
−05 08 39.59
−05 09 28.09
−05 09 34.04
−05 09 33.82
−04 07 52.92
−04 23 59.07
−04 24 02.12
−03 59 43.62
−05 06 10.59
−04 18 01.27
−04 02 59.43
−04 12 09.96
−04 18 09.78
−05 08 26.38
−04 00 39.17
−04 00 40.70
−05 09 41.26
−04 27 52.29
−04 42 43.03
−04 26 49.04
−04 19 50.73
−05 09 43.69
−05 10 49.01
−04 43 42.45
−05 19 59.73
−05 03 15.23
−05 12 33.97
−04 41 39.34
−05 00 18.73
−04 49 19.44
−03 59 35.54
−04 53 55.05
−04 22 31.54
−03 58 31.36
−05 17 23.52
−04 27 36.69
−04 12 13.07
−04 57 10.36
−04 57 10.21
−04 53 12.69
−04 46 34.60
−04 46 37.78
−03 45 37.52
−04 29 32.90
−03 50 10.19
−05 03 58.34
−04 16 24.95
−04 16 25.84
−04 29 21.11
−04 12 39.02
−04 05 35.58
−04 08 20.98
−04 23 21.79
−04 11 08.97
−04 07 49.94
−05 04 57.78
−04 10 46.09
log(FS )
log(FH )
−13.86
.
−14.14
−14.12
.
−13.59
−13.80
.
.
−13.74
.
−13.96
−14.15
−14.36
−14.38
−14.40
.
.
−14.93
−14.45
.
−14.33
−13.91
.
−14.55
−14.09
−14.46
−14.37
.
−14.49
−14.47
−14.12
−14.45
.
−14.10
.
.
−13.64
−14.04
−14.02
−13.91
−14.00
−13.74
.
.
.
−13.84
.
−14.48
−13.91
−14.26
−14.93
−13.96
.
−13.67
.
.
−13.69
−13.58
−13.50
−13.31
−14.18
−13.30
−13.63
−13.86
−14.00
−13.66
−13.57
−13.78
−13.89
−14.13
−13.51
−13.77
−13.89
−14.06
−13.76
−13.56
−13.90
−13.81
−13.74
−13.78
−13.64
−13.69
−14.06
−13.83
−13.87
−14.03
−13.61
−13.64
−13.81
−13.61
−13.63
−13.86
−13.46
−13.37
−11.96
−13.74
−13.44
−13.55
−13.42
−14.13
−13.76
−13.62
−13.26
−13.24
−13.89
−13.65
−13.99
−13.96
−13.33
−13.63
−13.29
−13.92
−14.07
RA
(J2000)
02 24 30.56
02 24 32.81
02 24 32.81
02 24 32.81
02 24 33.23
02 24 39.72
02 24 39.72
02 24 39.97
02 24 48.74
02 24 49.23
02 24 51.97
02 24 52.36
02 24 53.30
02 24 54.96
02 25 00.32
02 25 00.61
02 25 11.31
02 25 18.25
02 25 20.43
02 25 22.86
02 25 24.55
02 25 26.84
02 25 30.85
02 25 32.01
02 25 35.02
02 25 36.12
02 25 44.79
02 25 50.90
02 25 53.14
02 25 54.18
02 26 14.53
02 26 15.10
02 26 15.43
02 26 16.86
02 26 18.70
02 26 26.41
02 26 26.59
02 26 27.41
02 26 27.41
02 26 28.09
02 26 33.29
02 26 33.29
02 26 37.86
02 26 38.83
02 26 42.24
02 26 43.14
02 26 43.93
02 26 43.93
02 26 49.35
02 26 49.82
02 26 53.52
02 26 56.35
02 26 58.92
02 26 59.78
02 27 01.43
02 27 02.23
02 27 04.37
Optical
DEC
u
g
(J2000)
(AB)
(AB)
−05 08 41.69 21.66
20.48
−05 09 33.40 24.66
23.93
−05 09 33.40 24.66
23.93
−05 09 33.40 24.66
23.93
−04 07 54.83 22.83
22.00
−04 24 01.47 21.11
20.49
−04 24 01.47 21.11
20.49
−03 59 42.13 24.97
24.98
−05 06 14.91 25.93
23.83
−04 18 02.01 22.00
21.56
−04 02 59.88 21.66
20.32
−04 12 11.97 23.54
23.20
−04 18 07.92 22.94
22.52
−05 08 25.75 22.43
21.81
−04 00 37.98 23.39
22.74
−04 00 38.18 22.53
21.60
−05 09 40.87 26.33
24.98
−04 27 50.31 23.84
22.84
−04 42 38.86 22.40
21.74
−04 26 47.78 24.03
23.05
−04 19 50.76 22.80
22.34
−05 09 45.20 23.48
22.86
−05 10 50.19 23.22
22.21
−04 43 46.54 23.02
21.44
−05 19 57.49 22.28
21.12
−05 03 14.12 25.40
22.79
−05 12 34.52 23.96
23.41
−04 41 40.96 24.56
23.66
−05 00 18.55 24.02
24.28
−04 49 21.05 24.00
23.60
−03 59 38.34 24.72
22.42
−04 53 55.82 23.67
22.89
−04 22 32.45 23.71
22.80
−03 58 30.38 20.22
19.13
−05 17 25.47 23.20
22.58
−04 27 36.83 23.12
23.02
−04 12 12.75 23.73
22.77
−04 57 10.81 > 25.00 21.96
−04 57 10.81 > 25.00 21.96
−04 53 12.93 > 25.00 21.63
−04 46 37.08 26.69
24.56
−04 46 37.08 26.69
24.56
−03 45 40.18 25.37
25.07
−04 29 33.30 24.78
24.15
−03 50 10.50 21.43
20.10
−05 03 57.50 23.78
23.39
−04 16 27.07 21.82
20.11
−04 16 27.07 21.82
20.11
−04 29 22.04 23.88
22.52
−04 12 40.58 22.11
20.99
−04 05 37.49 24.41
23.58
−04 08 17.95 22.78
22.00
−04 23 20.87 24.99
24.31
−04 11 08.61 22.32
21.58
−04 07 51.11 20.28
19.36
−05 04 57.37 20.70
19.68
−04 10 43.83 24.00
22.99
IR
r
(AB)
19.37
23.00
23.00
23.00
21.48
19.87
19.87
24.15
22.47
21.05
19.51
22.82
21.94
21.27
21.60
21.06
23.56
21.74
20.90
22.06
21.80
22.28
21.51
20.15
20.16
21.36
22.78
22.87
23.73
22.79
20.92
22.22
21.86
18.51
21.58
22.69
21.71
22.00
22.00
21.45
22.99
22.99
23.85
23.28
19.08
22.69
19.06
19.06
21.35
19.77
22.85
20.98
23.18
20.98
18.62
19.01
22.18
i
(AB)
18.86
22.03
22.03
22.03
21.24
19.38
19.38
23.65
21.35
20.50
19.01
22.04
21.41
20.64
21.28
20.70
22.32
20.71
20.55
21.02
21.47
21.90
20.71
19.57
19.72
20.15
21.73
21.67
22.95
22.00
19.84
21.44
20.75
18.15
20.86
21.99
20.67
21.43
21.43
20.79
21.64
21.64
22.88
22.03
18.60
21.92
18.52
18.52
20.43
19.18
22.37
20.46
22.32
20.47
18.15
18.64
21.38
z
(AB)
18.50
21.34
21.34
21.34
21.23
19.11
19.11
23.21
21.12
20.24
18.71
21.72
21.12
20.26
21.19
20.56
21.68
20.34
20.46
20.42
21.35
21.66
20.46
19.17
19.39
19.74
21.06
21.06
21.79
21.76
19.46
20.83
20.23
18.04
20.54
21.82
20.28
21.48
21.48
20.64
20.95
20.95
22.05
21.45
18.34
21.66
18.28
18.28
20.04
18.86
22.04
20.10
21.61
20.28
17.95
18.39
21.01
3.6µm
4.5µm
(AB)
(AB)
18.54
18.70
0.00
0.00
19.46
19.36
19.46
19.36
> 21.03 > 20.96
17.96
17.90
17.96
17.90
21.00
20.96
19.54
20.03
19.34
19.33
18.04
18.15
20.86
21.04
> 21.03 > 20.96
19.03
19.24
19.18
18.68
20.63
20.70
19.66
19.78
18.72
18.95
20.32
20.81
18.28
18.24
21.22
21.23
18.91
18.78
18.48
18.55
18.70
18.74
18.59
18.47
18.21
18.24
19.22
19.43
18.91
18.92
19.60
19.68
19.69
19.62
18.39
18.74
19.40
19.64
18.76
18.43
0.00
0.00
19.19
19.48
19.98
20.04
18.21
18.17
16.64
17.12
16.64
17.12
19.19
19.22
19.25
19.49
19.25
19.49
NC
NC
19.20
19.31
NC
NC
20.28
20.72
17.38
17.35
17.38
17.35
18.70
18.99
18.38
18.49
19.75
19.71
18.71
18.89
18.64
18.54
18.90
18.62
NC
NC
18.59
18.50
NC
NC
5.8µm
(AB)
19.02
0.00
19.24
19.24
> 19.84
17.71
17.71
> 19.84
> 19.84
18.91
18.14
> 19.84
> 19.84
19.12
18.18
> 19.84
> 19.84
18.95
> 19.84
18.09
> 19.84
18.82
18.18
19.12
18.40
18.02
19.60
18.71
> 19.84
19.49
18.71
19.01
18.02
0.00
> 19.84
> 19.84
17.72
17.36
17.36
19.23
19.93
19.93
NC
18.94
NC
> 19.84
17.39
17.39
18.81
18.51
> 19.84
19.03
18.36
18.10
NC
18.91
NC
8.0µm
(AB)
18.59
0.00
18.89
18.89
> 19.53
17.38
17.38
> 19.53
19.77
19.15
16.78
> 19.53
> 19.53
18.98
17.76
> 19.53
> 19.53
18.40
> 19.53
17.73
> 19.53
> 19.53
18.07
18.73
17.65
17.68
> 19.53
18.40
19.86
19.37
19.39
19.51
17.25
0.00
> 19.53
> 19.53
17.06
17.25
17.25
> 19.53
19.32
19.32
NC
18.55
NC
> 19.53
17.01
17.01
19.15
18.42
> 19.53
18.50
17.94
17.83
NC
17.02
NC
z
0.45
1.02
1.02
1.02
0.20
0.65
0.65
0.97
0.71
0.70
0.11
0.82
0.68
0.72
0.44
0.13
0.83
0.67
0.26
0.83
0.62
0.66
0.66
0.45
0.14
0.64
1.03
0.99
1.16
0.66
0.64
0.73
0.79
0.15
0.56
0.90
0.68
0.91
0.91
0.85
0.82
0.82
1.05
0.96
0.18
0.70
0.20
0.20
0.59
0.45
0.20
0.46
0.92
0.62
0.20
0.12
0.65
log(M)
[M⊙ ]
11.07
10.43
10.43
10.43
9.09
11.00
11.00
10.09
10.33
10.44
10.00
10.20
10.11
10.61
9.84
9.18
10.68
11.02
9.88
11.24
9.78
9.75
10.88
10.98
9.96
11.18
10.47
10.50
10.24
10.33
11.25
10.81
11.26
10.39
10.73
9.92
11.06
9.80
9.80
10.30
11.07
11.07
10.25
10.38
10.60
10.29
10.78
10.78
10.96
11.10
9.11
10.67
10.92
10.49
10.75
10.10
10.43
Phys. prop.
log(S )
nH
[yr−1 ]
[cm−2 ]
−10.09
21.3
−8.90
22.6
−8.90
22.6
−8.90
22.8
−9.56
< 21.0
−9.26
21.8
−9.26
21.4
−9.51
22.1
−∞
21.5
−8.83
< 21.0
−∞
22.0
−9.26
21.6
−9.15
21.8
−9.26
21.7
−9.44
22.5
−9.75
22.0
−∞
21.9
−10.85
21.2
−9.73
22.6
−10.85
22.8
−8.66
21.8
−8.74
22.3
−9.70
21.5
−10.85
21.9
−∞
22.3
−∞
22.0
−8.90
22.3
−8.90
22.5
−8.91
22.2
−9.73
22.2
−∞
22.7
−9.87
22.3
−10.85
22.5
−9.81
22.0
−9.99
22.1
−8.61
22.1
−10.85
22.6
−8.71
21.9
−8.71
23.5
−9.10
21.9
−∞
22.3
−∞
22.3
−∞
22.1
−∞
< 21.0
−∞
21.7
−9.51
22.4
−∞
22.0
−∞
22.4
−10.85
22.3
−10.49
21.6
−10.09
21.5
−9.99
22.6
−10.85
22.6
−9.26
22.3
−9.99
21.7
−10.09
21.4
−9.89
< 21.0
L[2-10]
[erg.s−1 ]
43.2
44.2
44.3
44.5
41.9
44.0
43.6
43.8
43.4
43.7
41.9
43.7
43.4
43.3
43.4
41.9
43.7
43.2
42.6
44.0
43.3
43.5
43.6
43.1
42.1
43.6
43.7
43.9
44.0
43.3
43.7
43.8
43.7
42.2
43.5
43.8
43.9
44.3
45.9
43.8
44.1
44.0
44.4
43.6
42.2
43.8
42.8
42.9
43.3
43.2
42.1
43.0
44.3
43.6
42.8
41.6
43.2
Host galaxies and environment of active galactic nuclei
X618
X573
X574
X620
X87
X235
X279
X70
X563
X110
X74
X101
X111
X569
X751
X71
X575
X245
X403
X244
X231
X576
X580
X404
X595
X557
X585
X401
X438
X424
X53
X430
X211
X51
X704
X215
X64
X431
X683
X428
X372
X415
X752
X219
X755
X676
X44
X67
X218
X33
X10
X18
X213
X29
X15
X691
X28
RA
(J2000)
02 24 30.51
02 24 32.72
02 24 32.81
02 24 32.75
02 24 33.54
02 24 39.72
02 24 39.58
02 24 40.10
02 24 48.66
02 24 49.21
02 24 52.07
02 24 52.06
02 24 53.12
02 24 54.91
02 25 00.45
02 25 00.50
02 25 11.24
02 25 17.93
02 25 20.32
02 25 22.89
02 25 24.21
02 25 26.86
02 25 30.83
02 25 31.92
02 25 35.05
02 25 36.10
02 25 44.77
02 25 50.99
02 25 53.26
02 25 54.23
02 26 14.36
02 26 15.11
02 26 15.14
02 26 17.28
02 26 18.58
02 26 26.55
02 26 26.45
02 26 27.34
02 26 27.49
02 26 28.11
02 26 33.34
02 26 33.35
02 26 37.86
02 26 38.82
02 26 42.09
02 26 43.45
02 26 43.84
02 26 43.93
02 26 49.31
02 26 49.83
02 26 53.46
02 26 56.14
02 26 58.76
02 26 59.72
02 27 01.40
02 27 02.46
02 27 04.31
148
Name
X45
X19
X530
X693
X196
X395
X777
X4
X204
X761
X166
X41
X543
X544
X531
X776
X784
X532
X773
X17
X174
X37
X165
X169
X781
X552
X771
X527
X539
X179
RA
(J2000)
02 27 10.12
02 27 13.18
02 27 13.49
02 27 13.49
02 27 26.27
02 27 36.23
02 27 36.18
02 27 37.04
02 27 38.03
02 27 39.96
02 27 40.66
02 27 49.24
02 27 53.18
02 27 54.03
02 27 54.44
02 27 54.59
02 27 56.20
02 27 56.90
02 28 02.15
02 28 02.05
02 28 02.31
02 28 04.22
02 28 04.77
02 28 08.22
02 28 08.88
02 28 12.70
02 28 12.99
02 28 27.75
02 28 43.35
02 28 46.93
X-ray
DEC
(J2000)
−04 16 49.94
−04 09 12.33
−05 06 51.21
−05 06 52.82
−04 33 27.02
−04 58 07.16
−03 56 49.75
−04 01 01.17
−04 38 06.02
−03 42 26.07
−04 18 57.90
−04 14 48.19
−05 12 29.06
−05 12 43.25
−05 06 58.27
−03 55 40.84
−04 00 17.63
−05 07 34.97
−03 52 50.21
−04 08 09.86
−04 25 49.23
−04 12 39.50
−04 18 18.18
−04 20 47.61
−03 58 43.74
−05 19 04.51
−03 51 15.87
−05 02 36.42
−05 10 11.61
−04 28 00.53
log(FS )
log(FH )
−13.93
−13.98
−14.54
−14.32
−14.27
−14.34
−14.52
.
.
−13.91
−14.21
−14.28
−14.66
−14.76
−14.05
−14.60
−14.08
−14.22
−14.26
−14.07
−14.65
.
.
.
−14.14
.
−14.44
−13.96
−14.18
.
−13.69
−13.68
−13.55
−13.78
−13.74
−14.07
−13.49
−13.86
−14.03
−13.57
−13.60
−13.94
−13.76
−13.69
−13.77
−13.70
−13.76
−13.64
−13.85
−13.88
−13.68
−13.94
−13.39
−13.79
−13.94
−13.82
−13.93
−13.72
−13.48
−13.73
RA
(J2000)
02 27 10.11
02 27 13.21
02 27 13.44
02 27 13.44
02 27 26.31
02 27 36.06
02 27 36.24
02 27 36.86
02 27 38.24
02 27 39.80
02 27 40.58
02 27 49.34
02 27 53.26
02 27 53.98
02 27 54.47
02 27 54.52
02 27 56.27
02 27 56.89
02 28 02.15
02 28 02.43
02 28 02.46
02 28 04.31
02 28 04.67
02 28 08.20
02 28 08.89
02 28 12.75
02 28 12.76
02 28 27.88
02 28 43.29
02 28 47.02
Optical
DEC
u
g
(J2000)
(AB)
(AB)
−04 16 48.42 22.10
21.20
−04 09 12.45 25.20
24.14
−05 06 51.76 > 25.00 24.00
−05 06 51.76 > 25.00 24.00
−04 33 28.00 22.18
20.60
−04 58 07.50
NC
23.90
−03 56 52.31
NC
22.42
−04 01 02.60
NC
23.21
−04 38 04.90
NC
21.80
−03 42 27.18
NC
22.39
−04 18 58.24
NC
24.31
−04 14 45.45
NC
24.41
−05 12 29.71
NC
22.68
−05 12 43.48
NC
21.73
−05 06 58.93
NC
20.96
−03 55 39.19
NC
23.42
−04 00 18.03
NC
24.26
−05 07 34.86
NC
20.82
−03 52 47.45
NC
20.86
−04 08 09.38
NC
22.46
−04 25 47.00
NC
22.77
−04 12 39.36
NC
20.26
−04 18 15.02
NC
24.14
−04 20 42.70
NC
22.02
−03 58 45.14
NC
22.19
−05 19 01.98
NC
24.19
−03 51 18.12
NC
19.65
−05 02 40.41
NC
22.93
−05 10 11.51
NC
19.93
−04 28 00.04
NC
24.42
IR
r
(AB)
20.39
23.37
23.52
23.52
19.70
22.75
21.13
22.46
20.26
22.00
24.50
23.54
21.76
20.48
20.30
22.82
23.13
19.90
20.58
22.28
21.55
19.24
23.20
21.14
21.80
23.82
18.90
22.45
18.91
23.22
i
(AB)
20.04
22.46
22.25
22.25
19.23
22.19
20.46
22.08
19.43
21.31
23.52
22.66
20.78
19.90
19.80
21.93
21.92
19.43
20.06
21.57
20.95
18.75
22.97
20.66
21.44
22.90
18.46
21.88
18.48
22.17
z
(AB)
19.84
22.24
21.84
21.84
19.01
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
3.6µm
4.5µm
(AB)
(AB)
19.44
19.45
NC
NC
18.89
18.71
18.89
18.71
18.76
18.86
19.53
19.14
NC
NC
NC
NC
18.18
18.43
NC
NC
NC
NC
NC
NC
18.43
18.41
18.67
18.70
18.81
18.81
NC
NC
NC
NC
18.17
18.30
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
> 21.03 > 20.96
NC
NC
> 21.03 > 20.96
NC
NC
NC
NC
5.8µm
(AB)
> 19.84
NC
18.93
18.93
18.86
18.81
NC
NC
18.47
NC
NC
NC
18.20
18.57
18.69
NC
NC
18.31
NC
NC
NC
NC
NC
NC
NC
> 19.84
NC
> 19.84
NC
NC
8.0µm
(AB)
18.62
NC
18.66
18.66
18.75
18.50
NC
NC
18.56
NC
NC
NC
17.90
18.58
18.51
NC
NC
17.86
NC
NC
NC
NC
NC
NC
NC
> 19.53
NC
> 19.53
NC
NC
z
0.24
0.64
0.94
0.94
0.14
0.58
0.62
0.11
0.47
0.76
0.89
1.19
0.91
0.44
0.64
1.00
0.92
0.53
0.78
0.87
0.48
0.63
0.46
0.56
0.69
0.83
0.59
1.10
0.12
0.71
log(M)
[M⊙ ]
10.10
9.96
10.02
10.02
10.12
9.63
10.31
8.69
11.17
9.99
9.19
10.62
11.40
10.77
10.65
10.45
10.34
10.90
10.38
10.10
10.28
10.81
9.09
10.39
9.66
9.32
11.10
11.08
10.35
9.84
Phys. prop.
log(S )
nH
[yr−1 ]
[cm−2 ]
−9.80
21.4
−9.89
21.8
−8.91
23.0
−8.91
22.5
−∞
21.9
−9.58
21.7
−8.90
22.8
−10.46
21.5
−∞
21.2
−8.61
22.0
−9.04
22.5
−∞
22.3
−10.38
22.9
−10.49
22.7
−9.41
21.8
−9.38
22.9
−∞
22.0
−9.43
22.3
−8.62
22.2
−9.14
21.6
−10.35
22.6
−8.92
21.7
−9.01
22.5
−9.35
22.0
−8.90
21.6
−8.93
22.1
−9.32
22.2
−9.59
21.9
−∞
22.1
−∞
22.2
L[2-10]
[erg.s−1 ]
42.6
43.6
44.2
43.9
42.0
43.1
43.8
41.6
42.9
43.9
44.0
44.0
43.9
43.2
43.5
44.1
43.9
43.4
43.6
43.7
43.3
43.3
43.6
43.3
43.4
43.7
43.3
44.1
42.1
43.7
149
Description of columns:
1. X-ray source Name.
2. X-ray source right assenscion.
3. X-ray source declination.
4. X-ray source flux in the soft [0.5-2] keV band in erg.s−1 .cm−2 .
5. X-ray source flux in the hard [2-10] keV band in erg.s−1 .cm−2 .
6. Optical counterpart right Ascension (J2000).
7. Optical counterpart declination (J2000).
8. u-band magnitude. Note ’NC’ means the field has not been observed in that band.
9. g-band magnitude. Note ’NC’ means the field has not been observed in that band.
10. r-band magnitude.
11. i-band magnitude.
12. z-band magnitude. Note ’NC’ means the field has not been observed in that band.
13. 3.6µm magnitude (AB). Tag ’NC’ (standing for ’Not Covered’) has been used when the location of the object is not overlapping with the SWIRE field. Tag ’NI’ stands for ’Not investigated’, and
is used when the object has no optical counterpart, so we did not investigate either if the radio source has an IRAC counterpart. We have used the same notation for the following columns.
14. 4.5µm magnitude (AB).
15. 5.8µm magnitude (AB).
16. 8.0µm magnitude (AB).
17. Photometric redshift as estimated by ZPEG.
18. Stellar mass as estimated by ZPEG in logarithm base-10 scale.
19. Specific SFR as estimated by ZPEG in logarithm base-10 scale.
20. Estimated hydrogen column density in cm−2 .
21. Estimated rest-frame intrinsic X-ray luminosity in the [2-10] keV band.
References
Name
150
Host galaxies and environment of active galactic nuclei
CHAPTER 7
Summary and discussion
As discussed in the introduction of this thesis, unified schemes are very successful in explaining
properties of many classes of AGN. However, many authors have argued that it does not properly
describe the low-power radio-loud AGN. These AGN often lack the observed features of the unified
scheme: they do not show evidence for luminous emission lines (Jackson & Rawlings 1997),
infrared emission from dusty torus (Whysong & Antonucci 2004; Ogle et al. 2006) and accretion
related X-ray emission (Hardcastle et al. 2006; Evans et al. 2006). It has been proposed that the
super-massive black hole in these systems may be accreting with a radiatively inefficient accretion
(“Radio mode”), as opposed to the emission line selected AGN, which are recognised as being
associated with radiatively efficient accretion (“Quasar mode”, Heckman et al. 2004; Best et al.
2005). Interestingly, Best et al. (2005) have shown that AGN as selected using criteria on emission
line luminosity or on radio power are statistically independent, suggesting these classes of AGN are
triggered by different mechanisms. These two accretion modes might be driven by the temperature
of the gas reaching the super massive black hole (see Hardcastle et al. 2007, for a discussion).
In this framework, the accretion of cold gas produces a radiatively efficient accretion disk, while
hot gas accretion drive an advective accretion, having low radiative efficiency. It has also been
proposed that the type of triggering process determines the temperature of the gas reaching the
black hole: “wet” galaxy mergers bring the cold gas to the central super massive black hole, thereby
triggering quasar mode AGN, while the intergalactic medium (IGM) hot gas cooling triggers a low
efficiency hot gas accretion (radio mode).
7.1
S
In order to test this scheme, we have studied in this thesis the properties of AGN populations
in the XMM-LSS field. Specifically, we have selected two samples of AGN based on (i) radio
luminosity and (ii) X-ray luminosity. To do this, we have carried out a deep low-frequency radio
survey with the Very Large Array at 74 and 325 MHz, covering 132 and 15.3 degree2 , leading
to the detection of 1500 radio sources. To increase our sample size, we have also observed the
XMM-LSS field with the Giant Meterwave Radio Telescope at 240 and 610 MHz. From our
radio sources catalog and from the X-ray catalogs of Pacaud et al. (2006), we have identified the
radio and point-like X-ray sources with their optical and infrared counterparts in the CFHTLS-W1
152
Host galaxies and environment of active galactic nuclei
and SWIRE surveys. Using the ZPEG stellar synthesis code, we have estimated stellar masses,
star formation rates, and redshifts for the normal galaxies, and for the AGN. In order to obtain
samples of AGN having reliable photometric redshifts we have developed a method for rejecting
the broad line Type-1 AGN. In addition, we have constructed an overdensity parameter based on
the photometric redshifts probability function, that gives the significance of the density around a
galaxy.
We have classified the radio and X-ray selected AGN in three classes based on their generic
properties. The results are as follows:
(I) Radio selected AGN with M & 1010.5−10.8 M⊙ : The fraction, fR , of radio-loud AGN is a
strong function of the galaxies stellar mass following the fR ∝ M 2.5 relationship as found
at z . 0.3 (Best et al. 2005). These AGN do not show signs of infrared excess. They
are preferentially found in poor cluster environment, while no signs of small 75 kpc scale
overdensity is detected around them.
(II) Radio selected AGN with M . 1010.5−10.8 M⊙ : The fraction of radio-loud AGN has a flatter
fR − M relation as compared to the M & 1010.5−10.8 M⊙ galaxies. These galaxies show a hot
infrared excess at wavelength as short as 3.6 µm (observer frame). Their environment is very
different from the environment of the higher stellar masses objects, in that they lie in large
scale underdensities, while their small 75 kpc scale overdensity is higher.
(III) X-ray selected AGN: The fraction of X-ray selected AGN with L[2−10]keV > 1043 erg.s−1 is a
strong function of the stellar mass. The slope of that function is in good agreement with the
same relation for the emission line selected AGN, while it disagrees with the fraction-mass
relation for the radio selected AGN. Over all the probed stellar mass range X-ray selected
AGN show an infrared excess in the near infrared. These AGN are preferentially found in
environment underdense on large scales.
7.2
D
We argue in this section that the properties of AGN in the XMM-LSS field support the picture
in which there are indeed two very distinct populations of AGN (“Quasar mode” versus “Radio
mode”, Best et al. 2005; Hardcastle et al. 2007). We further discuss the relations that might link
the triggering processes to these accretion modes.
Our radio selected AGN (I) with M & 1010.5−11 M⊙ seem to be radio mode AGN, as no hot
infrared emission is observed for these objects. Best et al. (2005) has argued that the fraction fR of
radio-loud galaxies versus their stellar mass relation ( fR ∝ M 2.5 ) is generated by the dependency
of the IGM gas cooling rate on the stellar mass (Mathews & Brighenti 2003). The mass fraction
relation found for the radio selected AGN (I) in our sample agrees with fR ∝ M 2.5 found at z . 0.3
in the SDSS (Best et al. 2005). These AGN that are in their radio mode, are not sensitive to their
local (75 kpc) density, but their large (450 kpc) scale environments are denser than the average. In
other words, in such stellar mass range, increasing the density of the surrounding environment on
large scales increases the probability that a galaxy is a radio-loud AGN. Altogether, this supports
the picture in which their AGN activity is triggered by the IGM hot gas cooling in their atmosphere.
References
153
Interestingly, populations (II) and (III) have similar internal and environmental properties,
while they are both very different from population (I). The slope of their AGN fraction versus
mass relation is similar to the one for emission line selected AGN that are recognised as being
AGN in their highly accreting quasar mode (Heckman et al. 2004; Best et al. 2005; Heckman &
Kauffmann 2006). In addition, both the (II) and (III) population show infrared emission from hot
dust at 3.6 µm, indicating that black holes are accreting in a radiatively efficient quasar mode.
Furthermore, their environments are similar: in a given stellar mass bin, they are found to be preferentially located in large 450 kpc scale underdense environment, with their small 75 kpc scale
overdensity being higher on average, which suggests that these populations are indeed the same.
Gas-rich galaxy mergers and interaction have often been proposed as mechanisms for triggering
the black hole activity (Springel et al. 2005), while this process has been suggested to occur more
frequently in underdense environment (Gómez et al. 2003; Best 2004). It may therefore be that the
activity of those AGN is triggered by the galaxy mergers and interactions that feed the black hole
with cold gas. Alternatively the AGN may be fuelled by cold gas that is infalling from the IGM
and also forming stars in the host galaxy. In order to have such cold gas infall, the gas cooling
time must be lower than the dynamical infall time, and that is more likely in underdense regions:
in the context of large scale structure formation, overdensity correspond the shock-heated, high
temperature IGM, while the temperature of the IGM gas in underdense regions is lower.
Altogether, our results are consistent with the picture in which there are two types of accretion,
with the first being radiatively efficient “Quasar mode” and the second being radiatively inefficient
“Radio mode” (Best et al. 2005; Heckman & Kauffmann 2006; Hardcastle et al. 2007). Our most
important result is that those two classes of AGN seem to lie in very different ∼ 450 kpc scale
environments, suggesting that the nature of the triggering mechanisms might be connected to the
rise of these two accretion modes. Hardcastle et al. (2007) discussed a physical picture in which
the accretion mode is determined by the temperature of the gas reaching the black hole, with that
temperature being connected with both the nature of the triggering process and the environment.
In such scenario, for the radio mode accretion, the IGM hot gas cools and reaches the black hole
at too high temperature to form a radiatively efficient accretion disk, so it rather accretes spherically. In contrast, the gas-rich galaxy mergers or the cold IGM gas in underdense regions might
preferentially bring cold gas to the central black hole, giving rise to a disk-like radiatively efficient
accretion (quasar mode).
In this picture these competing triggering processes depend on the large scale environment.
It might be that there is quite a direct link between the large scale structure formation and the
observed evolution of the AGN luminosity functions.
R
Best, P. N. 2004, MNRAS, 351, 70
Best, P. N., Kauffmann, G., Heckman, T. M., et al. 2005, MNRAS, 362, 25
Evans, D. A., Worrall, D. M., Hardcastle, M. J., Kraft, R. P., & Birkinshaw, M. 2006, ApJ, 642, 96
Gómez, P. L., Nichol, R. C., Miller, C. J., et al. 2003, ApJ, 584, 210
Hardcastle, M. J., Evans, D. A., & Croston, J. H. 2006, MNRAS, 370, 1893
Hardcastle, M. J., Evans, D. A., & Croston, J. H. 2007, MNRAS, 376, 1849
154
Host galaxies and environment of active galactic nuclei
Heckman, T. M. & Kauffmann, G. 2006, New Astronomy Review, 50, 677
Heckman, T. M., Kauffmann, G., Brinchmann, J., et al. 2004, ApJ, 613, 109
Jackson, N. & Rawlings, S. 1997, MNRAS, 286, 241
Mathews, W. G. & Brighenti, F. 2003, ARA&A, 41, 191
Ogle, P., Whysong, D., & Antonucci, R. 2006, ApJ, 647, 161
Pacaud, F., Pierre, M., Refregier, A., et al. 2006, MNRAS, 372, 578
Springel, V., Di Matteo, T., & Hernquist, L. 2005, ApJ, 620, L79
Whysong, D. & Antonucci, R. 2004, ApJ, 602, 116
Nederlandse Samenvatting
155
Nederlandse samenvatting
De kernen van actieve sterrenstelsels (AGN) vormen ongetwijfeld één van de interessantste
onderzoeksgebieden binnen de sterrenkunde (zie Figuur 8.1). In de gangbare AGN theorie wordt
hier energie op de meest efficiënte manier opgewekt, via materie die zich op een super zwaar zwart
gat (van zo’n 106−9 zonsmassa’s) stort. Ofschoon we nog niet de details van dit proces kennen,
weten we wel dat hierbij veel licht van korte golflengten wordt geproduceerd, en ook bundels van
relativistische deeltjes. Rondom de schijf van waaruit massa op het punt staat naar het zwarte gat
te vallen (de zogenaamde accretieschijf) zit nog een dikke ring van stof die dit centrale deel van de
AGN verhult. Al deze onderdelen samen vormen wat bekend staat als de “overkoepelende theorie”
van actieve sterrenstelsels (zie Figuur 8.2).
Figuur 8.1: Het sterrenstelsel NGC 7742, dat geclassificeerd is als een Seyfert 1-type sterrenstelsel,
vertoont tekenen van activiteit in zijn centrale deel.
Men denkt dat hier energie wordt opgewekt doordat
massa op een super zwaar zwart gat valt.
Figuur 8.2: Een afbeelding van hoe de kern van
zo’n sterrenstelsel er van dichtbij uit zou kunnen zien volgens de overkoepelende theorie van
AGN. Rondom het zwarte gat in het centrum van
het sterrenstelsel bevindt zich een dikke stofring
die dit centrale deel verhult. Ioniserende straling
en relativistische deeltjes bewegen zich loodrecht
op deze ring weg van de kern.
De overkoepelende theorie van AGN kan echter niet verklaren waarom het vermogen op radio
golflengten van sommige radio-luide AGN niet al te groot is. Ook ontbreken heldere emissielijnen
in het spectrum van deze sterrenstelsels, terwijl de overkoepelende theorie die juist wel voorspelt.
Bij deze sterrenstelsels wordt meestal ook geen warmtestraling waargenomen die afkomstig is van
de stofring rond het centrum van de AGN, en ook niet de Röntgenstraling die afkomstig is van
de accretieschijf. Sommige wetenschappers hebben daarom voorgesteld dat er twee klassen van
AGN zijn: één waarbij straling op een efficiënte manier door de accretieschijf wordt uitgezonden
(de zogenaamde “quasar groep”), en één waarbij straling niet efficiënt kan worden uitgezonden
(de “radio groep”). Die laatste groep past nog niet binnen het kader van de overkoepelende AGN
156
Host galaxies and environment of active galactic nuclei
Figuur 8.3: Linker 2×2 plaatjes: Vaak zijn heldere AGN in het nabije deel van het heelal geassocieerd met
botsende sterrenstelsels. Door zo’n botsing komt koud gas in het centrum van een sterrenstelsel terecht,
waar het kan worden opgeslokt door het super zware zwarte gat in het centrum van dat stelsel. Hierdoor
zal de AGN gaan stralen. In het rechter paneel laat de linker kolom van 3 plaatjes heet gas zien dat in de
buurt van 3 sterrenstelsels koelt en Röntgenstraling uitzendt, terwijl de rechter kolom de sterrenstelsels zelf
laat zien in het zichtbare deel van het spectrum. Volgens veel onderzoekers is het koelen van heet gas in de
buitenste delen van een sterrenstelsel een alternatieve manier om een AGN aan de praat te krijgen.
theorie. Volgens deze wetenschappers ligt de oorzaak voor deze tweedeling bij de temperatuur
van de materie die richting het zwarte gat beweegt. Hun idee is dat als de materie koud genoeg
is, deze materie een accretieschijf kan vormen rond het zwarte gat, van waaruit straling makkelijk
kan worden weggetransporteerd (de quasar groep). Heet gas kan geen accretieschijf vormen, en
daardoor zal straling niet op een efficiënte manier uitgezonden kunnen worden (de radio groep).
De temperatuur van het gas zou inderdaad bepaald kunnen worden door de manier waarop gas
in centrale deel van het sterrenstelsel, waar het super zware zwarte gat zich bevindt, terecht komt.
Voor heldere AGN is er behoorlijk duidelijk bewijs dat botsingen en interacties van sterrenstelsels
een rol spelen. Quasars die zijn geselecteerd op grond van hun eigenschappen in het zichtbare
of infrarode deel van het spectrum worden bijvoorbeeld vaker aangetroffen in vervormde sterrenstelsels (Zie Figuur 8.3). Het idee hierbij is dat door een botsing van sterrenstelsels koud gas in
de buurt van het zwarte gat komt, en daarbij kan gemakkelijk energie worden uitgestraald. Heet
gas dat van nature voorkomt tussen sterrenstelsels (het IGM) koelt af in de buitendelen van zware
elliptische sterrenstelsels (Zie Figuur 8.3), en dit is volgens sommigen een andere manier om een
normaal sterrenstelsel te veranderen in een actief sterrenstelsel. Recent onderzoek toont aan dat
AGN met een laag radio vermogen, en dan vooral van de “radio groep”, door dit mechanisme ontstaan. Het koelen van heet gas produceert dan heet gas dat maar heel lastig straling kan uitzenden
als het naar het zwarte gat beweegt.
Nederlandse Samenvatting
157
Figuur 8.4: Een aantal telescopen die voor het onderzoek in dit proefschrift zijn gebruikt. Van links naar
rechts, en van boven naar beneden: de Very Large Array en Giant Meterwave Radio Telescope radio interferometers, die zich respectievelijk in New Mexico (VS) en Pune (India) bevinden, de Spitzer infrarood
satelliet, de Canada France Hawaii Telescope, en de XMM-Newton Röntgensatelliet.
Dit proefschrift
Doel van dit proefschrift is om te onderzoeken of de theorie klopt die hierboven staat beschreven, waarin het actief worden van een sterrenstelsel afhangt van de eigenschappen van het gas dat
naar het zwarte gat valt, en dat op die manier de waarneembare eigenschappen van de AGN bepaalt. Dit zijn daarbij de belangrijkste vragen. Waar bevinden de verschillende typen AGN zich ten
opzichte van de grote-schaal verdeling van andere sterrenstelsels? Waardoor wordt de kern van een
sterrenstelsel een AGN? Bestaat er een verband tussen de manier waarop een AGN actief wordt en
de toestand waarin gas het zwarte gat bereikt (dus “quasar groep” tegenover “radio groep”)? Hoe
veranderen deze eigenschappen gedurende de geschiedenis van het heelal?
Een goede manier om deze vragen te beantwoorden is door de statistische eigenschappen van
een groot aantal AGN te bestuderen. In dit proefschrift selecteren we 2 groepen AGN in de XMMLarge Scale Structure survey op grond van (i) hun helderheid op radio golflengten (hoofdstuk
2 t/m 5) en (ii) hun helderheid op Röntgen golflengten (hoofdstuk 6). Ons idee daarbij is dat
de eerste groep AGN gedomineerd wordt door AGN uit de “radio groep”, en de tweede door
AGN uit de “quasar groep”. Van elke AGN in deze dataset bepalen we de interne en omgevings
eigenschappen, zoals totale massa aan sterren, roodverschuiving, hoeveelheid sterren die worden
gevormd in het sterrenstelsel, teveel aan infrarode emissie, en aantal naburige sterrenstelsels. Door
de interne en omgevings eigenschappen te bestuderen van de stelsels die we hebben geselecteerd op
grond van hun eigenschappen op radio en Röntgen golflengtes kunnen we de vragen die hierboven
158
Host galaxies and environment of active galactic nuclei
zijn gesteld proberen te beantwoorden. Hierna geef ik een gedetailleerdere beschrijving van de
verschillende hoofdstukken.
In hoofdstuk 2 beschrijven we een lage-frequentie radio survey van het XMM-LSS veld die
we hebben uitgevoerd met de Very Large Array (VLA, figuur 8.4) op frequenties van 74 en 325
MHz. Deze data beslaan 132 en 15.3 vierkante graden aan de hemel respectievelijk. Vanwege
de storende invloeden van de ionosfeer en vanwege het grote oppervlak aan de hemel dat we
waarnemen, hebben we extra aandacht besteed aan de calibratie van onze data.
Om het aantal radiobronnen dat we kunnen gebruiken te vergroten, en om ook de radio spectra
van deze bronnen te kunnen bepalen, gebruiken we in hoofdstuk 3 het grote antenne-oppervlak
van de Giant Meterwave Radio Telescope (GMRT, figuur 8.4) om het XMM-LSS veld op 240 en
610 MHz waar te nemen.
We identificeren de radiobronnen die we op frequenties van 74, 240, 325 en 610 MHz hebben
waargenomen met sterrenstelsels in het zichtbare deel van het spectrum in hoofdstuk 4. Hierbij
maken we gebruik van een catalogus van bronnen in het optische deel van het spectrum, en van
afbeeldingen van de hemel. We schatten dat ∼ 75% van de radio bronnen een optische tegenhanger
hebben, en we bepalen fotometrische roodverschuivingen voor de 3 miljoen sterrenstelsels in het
onderzochte gebied aan de hemel, inclusief de gaststelsels waarin zich radiobronnen bevinden. We
ontwikkelen een methode om sterrenstelsels met een verkeerde fotometrische roodverschuiving
te verwijderen uit onze bronnenlijst. Deze methode maakt gebruik van 2 verschillende manieren
om de fotometrische roodverschuiving te berekenen, in combinatie met een optisch kleur-kleur
criterium.
In hoofdstuk 5 bestuderen we met de catalogus van sterke radiobronnen die we in hoofdstuk 4
hebben samengesteld de interne eigenschappen van deze radiosterrenstelsels, en ook hoe hun omgeving eruit ziet. Om de omgeving van zo’n radiostelsel te karakteriseren definiëren we een maat
voor de dichtheid van naburige sterrenstelsels, waarbij we rekening houden met de onzekerheden
in de fotometrische roodverschuiving. We beargumenteren dat de resultaten van de analyse uit
dit hoofdstuk consistent zijn met het beeld dat bij botsingen van sterrenstelsels een accretieschijf
ontstaat die efficiënt energie uitstraalt, en dat de AGN uit de “radio groep” vooral ontstaan doordat
gas koelt in de buitendelen van een zwaar elliptisch sterrenstelsel.
In hoofdstuk 6 presenteren we een catalogus van AGN die we hebben geselecteerd in de harde
Röntgenband (2-10 keV), en we analyseren deze data op een vergelijkbare manier als die we in
hoofdstuk 4 en 5 hebben beschreven. We laten in dit hoofdstuk zien dat AGN die op basis van hun
Röntgenstraling zijn geselecteerd, van hetzelfde type zijn als de AGN die we hebben geselecteerd
op grond van hun emissielijnen. Hieruit concluderen we dat het in beide gevallen gaat om AGN
uit de “quasar groep”.
Tenslotte presenteren we in hoofdstuk 7 de belangrijkste conclusies van dit proefschrift, en
we bespreken mogelijke implicaties. Onze resultaten bevestigen dat er twee verschillende typen
AGN zijn (“quasar groep” en “radio groep”), en dat deze AGN worden gevonden in sterk verschillende omgevingen. betekenen dat de manier waarop een AGN actief wordt te maken heeft met de
manier waarop materie naar een zwart gat in de kern van het sterrenstelsel valt. Volgens dit beeld
stroomt bij een botsing van sterrenstelsels koud gas naar het zwarte gat. Dit gas vormt dan een
accretieschijf, en kan op die manier heel efficiënt straling uitzenden. Gas dat koelt in de atmosfeer
van een zwaar elliptisch sterrenstelsel is te heet om een accretieschijf te vormen, en het zal daarom
veel minder efficiënt straling kunnen uitzenden. Het aantal botsingen tussen sterrenstelsels en de
Nederlandse Samenvatting
159
hoeveelheid gas die zich in de buurt van een sterrenstelsel bevindt hangt af van het aantal sterrenstelsels dat zich in een bepaald volume van het heelal bevindt. Daarom kan er een heel direct
verband bestaat tussen de vorming van sterrenstelsels en groepen van sterrenstelsels enerzijds, en
de waargenomen verandering in de helderheidsverdeling van AGN anderzijds.
Résumé en français
161
Résumé en français
Les noyaux actifs de galaxies (NAG) sont des objets fascinants (cf. la figure 9.1). La principale
théorie les décrivant, le Modèle Unifié, établit que les propriétés que l’on observe au sein des
NAG sont la conséquence de la chute de matière dans un trou noir dont la masse peut atteindre
plusieurs milliards de masses solaires. En tombant dans le trou noir, la matière forme un disque, et
libère de gigantesques quantité d’énergie suivant un processus que l’on nomme accrétion (on parle
alors du disque d’accrétion). Ce processus de transformation d’énergie est le plus efficace que l’on
connaisse : il correspond à la transformation d’énergie gravitationelle en énergie thermique, avec
une efficacité de l’ordre d’une dizaine de pour cent (contre ∼ 1% pour la fusion thermonucléaire).
Dans cette théorie, le trou noir est entouré d’un tore de poussière et de nuages de gaz orbitants
autour et à l’intérieur du système tore/trou noir. Le disque d’accrétion dégage un rayonnement
intense dans les domaines ultraviolet et X. Ce rayonnement provoque notamment le chauffage du
tore de poussière (cf. la figure 9.2). Dans certains NAG (les NAG radio), on observe des jets de
particules qui se propagent orthogonalement au plan du tore de poussière, en émettant des ondes
radio.
F. 9.1: La galaxie NGC 7742 est de type Seyfert 1. Elle montre des signes d’activité dans ses
régions centrales. Dans l’image communément acceptée, cette immense quantité d’énergie est produite par la chute de matière dans un trou noir supermassif situé en son centre.
F. 9.2: Vue d’artiste d’un NAG tel que décrit
par le Modèle Unifié (elle correspond aux
régions centrales de la galaxie de la figure 9.1).
Le trou noir super massif est entouré d’un tore de
poussière. Le rayonnement ionisant, ainsi que les
éventuels jets radio, se propagent dans la direction orthogonale au plan du tore.
Bien que le trou noir ait une masse négligeable à côté de celle de sa galaxie hôte, son rôle quant
à la formation des structures dans l’Univers, pourrait-être determinant. En effet, l’immense quantité d’énergie dégagée lors d’une courte période d’activité d’un de ces monstrueux trous noirs peut
162
Galaxies hôtes et environnements des NAG
F. 9.3: (A gauche) Dans l’univers local, les NAG sont associés à des collisions de galaxies. On pense
que l’interaction entre galaxies peut causer la chute de gaz froid vers les régions centrales de la galaxie –
où réside le trou noir – et déclencher l’activité du NAG dans son mode Quasar (voir texte). (A droite) De
grandes quantités de gaz chaud du milieu inter-galactique sont observées dans l’atmosphère des galaxies
elliptiques massives (le gaz à gauche, les étoiles à droite). Certains auteurs proposent que ce gaz chaud, en
refroidissant, est à même d’atteindre les régions centrales de la galaxie. Ce mécanisme peut constituer un
moyen alternatif afin de déclencher l’activité des NAG dans leur mode Radio.
égaler l’énergie de liaison d’un amas de galaxie, qui contient plusieurs milliers de galaxies, chacune contenant plusieurs centaines de milliards d’étoiles. Ainsi, il est de plus en plus couramment
accepté que pour conter la grande histoire de l’univers et décrire son état actuel, il est nécessaire
de parler de l’existence des NAG, et de leur rôle.
Bien que le Modèle Unifié (Fig. 9.2) décrive fidèlement nombre de propriétés observationnelles des NAG, certaines classes y échappent. C’est notamment le cas des radio-galaxies de faible
puissance qui ne présentent ni raies d’émission, ni excès en infrarouge, ni émissions en X liés à
l’existence d’un disque d’accrétion, alors que ces propriétés sont prédites par le Modèle Unifié.
Il a été démontré récemment que l’activité du trou noir dans ces NAG radio de faible puissance
résultaient d’un phénomène différent et indépendant. Certains auteurs ont alors proposé qu’il existe
deux classes distinctes de NAG, dont l’émergence est liée à la température du gaz atteignant le
trou noir central. Dans ce schema, l’accrétion de gaz froid produit une accrétion en disque, qui
transforme efficacement l’énergie gravitationelle en énergie thermique (on dit alors qu’il est radiativement efficace). Les propriétés pérdites par le Modèle Unifié sont alors observées. En revanche,
l’accrétion d’un gaz chaud produirait une accrétion advective ou sphérique, radiativement inefficace, où la majeure partie de l’énergie gravitationelle serait tranformée en énergie cinétique dans
les jets radio. Dans la suite, le mode d’accrétion en disque, radiativement efficace et caracterisant
le Modèle Unifié, est nommé “mode Quasar”, tandis que le mode d’accrétion sphérique, radiative-
Résumé en français
163
ment inefficace, est nommé “mode Radio”.
Comme nous l’avons exposé précédemment, les ingrédients nécessaires pour créer un NAG
sont : un trou noir super massif, un réservoir de gaz pour l’alimenter, et un mécanisme permettant
de faire chuter le gaz dans le trou noir. Si la température du gaz accrété détermine les propriétés
des NAG, on peut se demander ce qui détermine la température du gaz. Pour les NAG de forte
luminosité, il semble que cette chute se produise lors des collisions et interactions entre galaxies
(cf. Fig. 9.3). La collision de galaxie est alors le mécanisme déclencheur de l’activité du NAG.
Un schéma alternatif a été proposé, dans lequel ce n’est pas le gaz du milieu inter-stellaire (intragalactique) qui alimente le trou noir et déclenche son activité, mais le gaz chaud du milieu intergalactique. On observe de très grandes quantités de ce gaz chaud se refroidissant dans l’atmosphère
de galaxies elliptiques massives (cf. Fig. 9.3). En refroidissant, ce gaz chute vers le fond du puits
de potentiel gravitationnel généré par la galaxie et alimente éventuellement le trou noir central, ce
qui déclenche son activité.
Il a ainsi été proposé que le type de mécanisme déclencheur pouvait être lié au mode d’accrétion
(mode Quasar/Radio). Dans ce schéma, la collision/interaction entre galaxies chargées de gaz froid
déclencherait l’accrétion en disque du gaz froid (processus radiativement efficace). Quant au gaz
chaud du milieu inter-galactique, il alimenterait le trou noir central lors de son refroidissement,
déclenchant l’accrétion sphérique radiativement inefficace qui caractérise le mode radio.
Cette thèse
Le but de cette thèse est de tester le schéma décrit plus haut, dont les différents mécanismes
déclencheur de l’activité radio sont liés au type d’accrétion, donc aux propriétés des NAG et à leur
influence sur leur environnement. Les principales questions abordées lors de cette étude sont les
suivantes : comment sont distribuées les différentes classes de NAG, dans la structure à grande
échelle de l’univers ? Quels sont les mécanismes déclencheurs de l’activité des NAG ? Quels sont
les connexions entre mécanisme déclencheur et mode d’accrétion (mode Quasar/Radio) ? Comment ces relations ont évolué dans l’histoire de l’univers ?
Une manière moderne d’aborder ces questions consiste à étudier les propriétés statistiques d’un
grand échantillon de NAG. Dans cette thèse, nous sélectionnons deux échantillons de NAG dans le
champ XMM-LSS (XMM est le nom du satellite et LSS est l’acronyme pour “Large Scale Structure” ou “structure à grande échelle” en français), basés sur (i) la luminosité radio et (ii) la luminosité X. L’idée sous-jacente est de sélectionner deux échantillons de NAG en mode d’accrétion
Radio et en mode d’accrétion Quasar respectivement. En utilisant des modèles physiques, une
série d’estimateurs a été attachée à chaque objet dans ces échantillons : masse stellaire, distance,
taux de formation stellaire, excès en infrarouge et paramètre environnemental de surdensité. En
étudiant les propriétés statistiques de ces estimateurs sur ces ensembles d’objets, il est possible de
contraindre la nature de ces populations. Par exemple, les observations infrarouges nous informent
sur la présence de poussières chaudes, et donc sur la nature du mode d’accrétion. Voici le descriptif
détaillé du contenu de cette thèse :
Dans le Chapitre 2 un sondage à basse fréquence (domaine radio) du champ XMM-LSS a 74
et 325 MHz est présenté. Ces observations ont été conduites en utilisant l’interféromètre radio Very
Large Array (Fig. 9.4).
Pour augmenter la taille des échantillons de NAG, dans le Chapitre 3, le champs XMM-LSS
164
Galaxies hôtes et environnements des NAG
F. 9.4: Les données utilisées dans le cadre de cette thèse proviennent principalement des instruments
suivants (de gauche à droite, de haut en bas) : interféromètre radio Very Large Array (Nouveau Mexique,
USA), interféromètre radio Giant Meterwave Radio Telescope (Pune, Inde), télescope spatial infrarouge
Spitzer, télescope optique Canada France Hawaii Telescope, télescope spatial XMM-Newton.
est observé à 240 et 610 MHz avec l’interféromètre Giant Meterwave Radio Telescope (Fig. 9.4).
Dans le Chapitre 4, en utilisant les observations du satellite Spitzer et du télescope CanadaFrance Hawaı̈ Telescope (CFHT), on identifie les contreparties optiques et infrarouges des NAG
détectées dans le domaine radio. En utilisant un modèle de synthèse de population stellaire nous
estimons les masses, distance, et taux de formation stellaire de ∼ 3 million de galaxies normales,
et de quelques centaines de NAG. Nous construisons une méthode de rejection des NAG pour
lesquels ces estimations ne sont pas fiables.
Dans le Chapitre 5, nous étudions les propriétés de l’échantillon de NAG défini dans le Chapitre 4. Afin de quantifier l’environnement de ces sources, nous construisons un estimateur statistique de surdensité. Cette étude révèle une dichotomie a la fois interne et environnementale. Les
NAG localisés dans des galaxies massives ne présentent pas d’excès en infrarouge, alors qu’ils
sont situés dans des environnements denses. Les NAG de plus petite masses montrent un excès infrarouge, et sont situés dans des environnements sous-denses. Nos données sont consistantes avec
l’idée que le mode quasar est déclenché par les interactions de galaxies, alors que le mode radio
est déclenché par le refroidissement du gaz inter-galactique chaud.
Dans le Chapitre 6, un échantillons de NAG sélectionné en bande X durs en suivant la démarche
décrite dans les chapitres 4&5. Contrairement aux NAG sélectionnés en utilisant un critère dans
le domaine radio, cette population est très homogène : elle présente des excès importants en infrarouge à travers toute la gamme de masse stellaire, et réside dans des environnements sous-denses.
De la présence d’excès infrarouge, nous déduisons que cette population de NAG accrète dans son
Résumé en français
165
mode Quasar, et de sa préférence des milieux sous-denses, nous déduisons que les collisions de
galaxies jouent un rôle dans le déclenchement de l’activité de ces NAG.
Dans le Chapitre 7, les résultats importants de cette étude sont présentés, et nous discutons
leurs implications possibles. Nos résultats indiquent l’existence de deux types de NAG, résidants
dans des environnements différents. La relation entre environnement et type de NAG suggère un
lien entre mécanisme de déclenchement et mode d’accrétion. Les caractéristiques des échantillons
de NAG présentés dans cette these sont consistant avec le schéma suggéré par plusieurs auteurs, dans lequel la collision de galaxies apporte du gaz froid au trou noir central et déclenche
une accrétion en disque, radiativement efficace, alors que l’accrétion du gaz chaud du milieu
inter-galactique produit une accrétion sphérique radiativement inefficace. Ces résultats mettent
en lumière la nature possible de la relation entre formation de la structure à grande échelle, et
évolution des NAG.
167
Curriculum vitae
I was born in Saint-Brieuc (Cotes d’Armor, Breizh, France) on the 18th of August 1979 from
Michel Tasse and Beatrice Tasse (born Guillou). I grew up in Landerneau (Finistère), where I spent
most of my childhood observing our cloudy sky with a 115/900 Newton telescope, and building
home made explosives and rockets. In 1995 I moved to Rennes the capital of Britanny, where I
graduated from high school in 1997. During the same year I entered the Institut National des Sciences Appliquées (INSA) engineering school. I had my first professional contact with astronomy in
2001 at the Observatory of Sofia (Bulgaria) under the supervision of Valeri Golev, where I worked
on the calibration of the CCDs of the Rosen observatory. For my final research project in 2002,
I worked on the Weakly Interacting Massive Particle (WIMP) dark matter detection experiment
(EDELWEISS) at the Comissariat a l’Energie Atomique (CEA) in Saclay. During that period, I
modeled and experimentally characterised superconducting thin layers (with critical temparatures
of ∼ 10 mK), in the goal of building a µK sensitive thermometer. In 2002 I both graduated from the
Génie Physique department at the INSA, and from the Physique, matière et rayonnement master
degree of the Rennes university. During the same year I entered an astrophysics oriented master degree of the Observatoire de Meudon (Astrophysique et méthodes associées). I did my final research
project at the European Southern Observatory (ESO) in Santiago (Chile), trying unsuccessfully to
detect Lyα emitters at z ∼ 6.5.
I arrived in Leiden in september 2003, where I started my PhD research on the relationship
between active galactic nuclei and large scale structure, under the supervision of Huub Röttgering
and George Miley. During my PhD I had a few working periods abroad with Aaron Cohen at the
Naval Research Laboratory in Washington, Philip Best at the Royal Observatory of Edinburgh,
and Damien Leborgne and Marguerite Pierre at the CEA. I went observing twice at the Giant
Meterwave Radio Telescope (NCRA, Puna, India), and at the William Herschel Telescope in La
Palma (Canary Islands). I participated in the Very Large Array summer school in Soccoro (NewMexico, USA), in the high redshift radio galaxies conference at Granada (Spain), and in various
XMM-LSS consortium meeting. I have been a teaching-assistant for the active galactic nuclei
lectures at Leiden Observatory.
169
Acknowledgement
During these four years, there have been many good moments, and more rarely, difficult ones.
During all that time, my family and friends have been there. This thesis is dedicated to them:
Papa, Maman, Gilles, Marie, Mami Monique, Papi Jacques, Mami Emilienne, Papi Robert, Herve
& Marie and their children, Marie-Paul & Guillaume and their children, Maryse et Jean and their
children. This thesis is equally dedicated to the people who were a great source of inspiration for
me (in order of appearance): Ty & Aude & Eliott, Mik, Ben, Pedro & Miss & Edouard-Hamidou,
Doudouze & Cathel, Neven & Clémence, Jeff, Jean-Marie, Nivanh & Bea, Julien & Barbara,
Guillaume & Mayil, Jacques-Edern & Hélène, Cedric & Christelle, Fab’, Jean-No, Elen & Unar,
Gil, Caro, Biloute, Olive, Jule-Millien & Dim’ & Pinpin & Cat’ & Alain, Evelyne, Sylvouille, Paty,
Jose-Luı́s, La Vero, Calbuco, Bea & Alberto & Nagual, Andy, Carlos, Erik-Jan, Mirek & Lucia,
Tomash & Tia, Sophie, Pierre-Guillaumme, Marta, Alma, Marie & Patricia, Cedrouze, Mario &
Daisy, Eline, Bruno & Veronica, Maud, Toto & Eloı̈se & Chloé, Estelle, Olivera & Martin, Niruj,
Claudio, Annette la Paquerette, Simone, Davik-Charles, Eric, Brent, Raymond, Thibaud & Emilie,
Hayden & Laura, Malcom, Robert, Luc, Matthieu & Lara, and the Multipleks crew.
I thank Valeri Golev, Xavier-François Navick, Daniel Rouan, Didier Pelat, Jacqueline Plancy,
and Jean-Gabriel Cuby for helping me cross the important steps towards starting a PhD in Astrophysics. I thank my collaborators Aaron Cohen, Damien Le Borgne for always supporting me
when I needed it. I address my endless gratitude to Philip Best, without whom this thesis would
not be as it is. I thank the computer group, the secretaries, as well as all the administrative staff
from the observatory that have always been very nice and helpful to me. I thank the man that succeeded in translating the abstract of the thesis to dutch: Dominic Schnitzeler! A special thank to
the people who have been reading this thesis, to correct the many spelling mistakes: Tracy, Brent,
George, Niruj, and Nina. Thanks to Reinout who translated the stellingen to dutch. Finally, I thank
my brother Gilles for designing the cover of this thesis.
Cyril Tasse
Leiden, January 18, 2008
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