Galactic Archaeology

Galactic Archaeology
Draft version September 21, 2015
Preprint typeset using LATEX style emulateapj v. 5/2/11
arXiv:1509.05420v1 [astro-ph.IM] 17 Sep 2015
THE APACHE POINT OBSERVATORY GALACTIC EVOLUTION EXPERIMENT (APOGEE)
Steven R. Majewski1 , Ricardo P. Schiavon2,3 , Peter M. Frinchaboy4 , Carlos Allende Prieto5,6 , Robert
Barkhouser7 , Dmitry Bizyaev8,9 , Basil Blank10 , Sophia Brunner1 , Adam Burton1 , Ricardo Carrera5,6 , S. Drew
Chojnowski1,11 , Kátia Cunha12,13 , Courtney Epstein14 , Greg Fitzgerald15 , Ana E. Garcı́a Pérez1,5 , Fred R.
Hearty1,16 , Chuck Henderson10 , Jon A. Holtzman11 , Jennifer A. Johnson14 , Charles R. Lam1 , James E. Lawler17 ,
Paul Maseman18 , Szabolcs Mészáros5,6,19 , Matthew Nelson1 , Duy Coung Nguyen20 , David L. Nidever1,21 , Marc
Pinsonneault14 , Matthew Shetrone22 , Stephen Smee7 , Verne V. Smith13,23 , Todd Stolberg15 , Michael F.
Skrutskie1 , Eric Walker1 , John C. Wilson1 , Gail Zasowski1,7 , Friedrich Anders24 , Sarbani Basu25 , Stephane
Beland26,27 , Michael R. Blanton28 , Jo Bovy29,30 , Joel R. Brownstein31 , Joleen Carlberg1,32 , William
Chaplin33,34 , Cristina Chiappini24 , Daniel J. Eisenstein35 , Yvonne Elsworth33 , Diane Feuillet11 , Scott W.
Fleming36,37 , Jessica Galbraith-Frew31 , Rafael A. Garcı́a38 , D. Anı́bal Garcı́a-Hernández5,6 , Bruce A.
Gillespie7 , Léo Girardi39,40 , James E. Gunn41 , Sten Hasselquist1,11 , Michael R. Hayden11 , Saskia Hekker34,42 ,
Inese Ivans31 , Karen Kinemuchi8 , Mark Klaene8 , Suvrath Mahadevan16 , Savita Mathur43 , Benoı̂t Mosser44 ,
Demitri Muna14 , Jeffrey A. Munn45 Robert C. Nichol46 , Robert W. O’Connell1 , A.C. Robin47 , Helio
Rocha-Pinto40,48 , Matthias Schultheis49 , Aldo M. Serenelli50 , Neville Shane1 , Victor Silva Aguirre34 , Jennifer
S. Sobeck1 , Benjamin Thompson4 , Nicholas W. Troup1 , David H. Weinberg14 , Olga Zamora5,6
1
Dept. of Astronomy, University of Virginia, Charlottesville, VA 22904-4325, USA
2 Gemini Observatory, 670 N. A’Ohoku Place, Hilo, HI 96720, USA
Astrophysics Research Institute, Liverpool John Moores University, 146 Brownlow Hill, Liverpool, L3 5RF, UK
4 Department of Physics and Astronomy, Texas Christian University, Fort Worth, TX 76129, USA
5 Instituto de Astrofı́sica de Canarias, E-38200 La Laguna,Tenerife, Spain
6 16 Departamento de Astrofı́sica, Universidad de La Laguna, E-38206 La Laguna, Tenerife, Spain
7 Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21218, USA
8 Apache Point Observatory and New Mexico State University, P.O. Box 59, Sunspot, NM, 88349-0059, USA
9 Sternberg Astronomical Institute, Moscow State University, Universitetsky prosp. 13, Moscow, Russia
10 Pulse Ray Machining & Design, 4583 State Route 414, Beaver Dams, NY 14812 USA
11 New Mexico State University, Las Cruces, NM 88003, USA
12 Observatório Nacional, Rio de Janeiro, RJ 20921-400, Brazil
13 Steward Observatory, University of Arizona, Tucson, AZ 85721, USA
14 The Ohio State University, Columbus, OH 43210, USA
15 New England Optical Systems, 237 Cedar Hill Street, Marlborough, MA 01752 USA
16 Department of Astronomy & Astrophysics, The Pennsylvania State University, 525 Davey Laboratory, University Park PA 16802,
USA
17 Department of Physics, University of Wisconsin-Madison, 1150 University Avenue, Madison, WI 53706, USA
18 Steward Observatory, University of Arizona, Tucson, AZ 85721, USA
19 ELTE Gothard Astrophysical Observatory, H-9704 Szombathely, Szent Imre Herceg St. 112, Hungary
20 Dunlap Institute for Astronomy and Astrophysics, University of Toronto, Toronto, Ontario, Canada
21 Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA
22 University of Texas at Austin, McDonald Observatory, Fort Davis, TX 79734, USA
23 National Optical Astronomy Observatories, PO Box 26732, Tucson, AZ 85719, USA
24 Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany
25 Department of Astronomy, Yale University, PO Box 208101, New Haven, CT 06520-8101 USA
26 Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO 80303, USA
27 Center for Astrophysics and Space Astronomy, University of Colorado Boulder, Boulder, CO 80303, USA
28 Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY 10003,
USA
29 Institute for Advanced Study, Einstein Drive, Princeton, NJ 08540, USA
30 John Bahcall Fellow
31 Department of Physics and Astronomy, University of Utah, 115 S 1400 E #201 Salt Lake City, UT 84112 USA
32 NASA Goddard Space Flight Center, Code 667, Greenbelt, MD 20771, USA
33 School of Physics and Astronomy, University of Birmingham, Birmingham B15 2TT, UK
34 Stellar Astrophysics Centre (SAC), Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, DK-8000 Aarhus
C, Denmark
35 Harvard-Smithsonian Center for Astrophysics, 60 Garden St., MS #20, Cambridge, MA 02138, USA
36 Computer Sciences Corporation, 3700 San Martin Dr, Baltimore, MD 21218, USA
37 Space Telescope Science Institute, 3700 San Martin Dr, Baltimore, MD 21218, USA
38 Laboratoire AIM, CEA/DSM – CNRS - Univ. Paris Diderot – IRFU/SAp, Centre de Saclay, 91191 Gif-sur-Yvette Cedex, France
39 Osservatorio Astronomico di Padova – INAF, Vicolo dell’Osservatorio 5, I-35122 Padova, Italy
40 Laboratório Interinstitucional de e-Astronomia - LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil
41 Department of Astrophysical Sciences, Peyton Hall, Princeton University 08544, USA
42 Max-Planck-Institut für Sonnensystemforschung, Justus-von-Liebig-Weg 3, 37077 Göttingen, Germany
43 Space Science Institute, 4750 Walnut street, Suite 205, Boulder, CO 80301 USA
44 LESIA, CNRS, Universit Pierre et Marie Curie, Universit Denis Diderot, Observatoire de Paris, 92195 Meudon Cedex, France
45 US Naval Observatory, Flagstaff Station, 10391 West Naval Observatory Road, Flagstaff, AZ 86005-8521, USA
46 Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth, UK
47 Institut Utinam, CNRS UMR6213, Université de Franche-Comté, OSU THETA Franche-Comté-Bourgogne, Observatoire de
Besançon, BP 1615, 25010 Besançon Cedex, France
48 Universidade Federal do Rio de Janeiro, Observatório do Valongo, Ladeira do Pedro Antônio 43, 20080-090 Rio de Janeiro, Brazil
49 Université de Nice Sophia-Antipolis, CNRS, Observatoire de Côte d’Azur, Laboratoire Lagrange, 06304 Nice Cedex 4, France and
50 Institute of Space Sciences (CSIC-IEEC) Campus UAB, Torre C5 parell 2 Bellaterra, 08193 Spain
3
2
APOGEE
Draft version September 21, 2015
ABSTRACT
The Apache Point Observatory Galactic Evolution Experiment (APOGEE), one of the programs in
the Sloan Digital Sky Survey III (SDSS-III), has now completed its systematic, homogeneous spectroscopic survey sampling all major populations of the Milky Way. After a three year observing campaign
on the Sloan 2.5-m Telescope, APOGEE has collected a half million high resolution (R ∼ 22, 500),
high S/N (>100), infrared (1.51-1.70 µm) spectra for 146,000 stars, with time series information via
repeat visits to most of these stars. This paper describes the motivations for the survey and its overall
design — hardware, field placement, target selection, operations — and gives an overview of these
aspects as well as the data reduction, analysis and products. An index is also given to the complement
of technical papers that describe various critical survey components in detail. Finally, we discuss the
achieved survey performance and illustrate the variety of potential uses of the data products by way
of a number of science demonstrations, which span from time series analysis of stellar spectral variations and radial velocity variations from stellar companions, to spatial maps of kinematics, metallicity
and abundance patterns across the Galaxy and as a function of age, to new views of the interstellar
medium, the chemistry of star clusters, and the discovery of rare stellar species. As part of SDSS-III
Data Release 12, all of the APOGEE data products are now publicly available.
Subject headings: Galaxy: abundances — Galaxy: kinematics and dynamics — Galaxy: evolution —
Galaxy: stellar content — infrared: stars — surveys
1. INTRODUCTION
1.1. Galactic Archaeology Surveys
Modern astrophysics has taken two general observational approaches to understand the evolution of galaxies. On the one hand, increasingly larger aperture telescopes, on the ground and in space, give access to the
high redshift universe and offer “low resolution” snapshots of ever earlier phases of galaxy evolution. On
the other hand, increasingly efficient, multiplexing photometric and spectroscopic instrumentation, often on
smaller, workhorse telescopes, has made possible enormous, definitive surveys of nearby galaxies yielding a
“high resolution” view of the present state of these systems. These data can be tested against “end state” predictions for the growth of large structures in the universe
to provide critical constraints on cosmological models
— so-called “near-field cosmology”. These two observational approaches — overviews of global properties at
high redshift versus more detailed information at low redshift — provide complementary information that must be
accommodated by evolutionary theories.
The highest-granularity information about galaxy evolution is provided by stars in our own Milky Way, whose
present spatial distributions, ages, chemistry and kinematics contain fossilized clues to its formation. Guided
by detailed models for the chemical and dynamical evolution of stellar populations, critical telltale signatures and
correlations within the above observables provide constraints on the model predictions for physical quantities
that cannot be observed directly, such as the history of
star formation, the early stellar initial mass function, and
the merger history of Galactic subsystems. This “Galactic archaeology” remains the principal basis by which
models for the formation and chemodynamical evolution
of the Milky Way and analogous systems are formulated
and refined. The vast literature on Milky Way stellar
populations as tools for understanding Galactic evolution has been reviewed in the past by, e.g., Gilmore et
al. (1989), Majewski (1993), Freeman & Bland-Hawthorn
(2002), and more recently by Ivezić et al. (2012) and Rix
& Bovy (2013).
These efforts are of course greatly aided by access to
expansive, carefully designed, homogeneous, and precise
databases of properties for stellar samples that span large
regions of the Galaxy and include all of the principal stellar populations. Modern archetypes of such databases
are large photometric surveys like the Two Micron AllSky Survey (2MASS; Skrutskie et al. 2006) and the Sloan
Digital Sky Survey (SDSS; York et al. 2000). Over the
past decade, these photometric catalogs have been widely
exploited for insights into the nature of the Milky Way
and probing the complexities of Galactic structure —
e.g., halo substructure (e.g., Majewski et al. 2003; RochaPinto et al. 2004; Belokurov et al. 2006; Grillmair 2009),
satellite galaxies (e.g., Willman et al. 2005; Belokurov et
al. 2007), the warp of the disk (e.g., López-Corredoira
et al. 2002; Reylé et al. 2009), and the still unresolved,
composite anatomy of the bulge (e.g., Robin et al. 2012),
which includes the recently found X-shaped feature (e.g.,
McWilliam & Zoccali 2010), and one or more central
bars (e.g., Alard 2001; Hammersley et al. 2000; CabreraLavers et al. 2007). Follow-on, low and medium resolution spectroscopic programs provide additional dynamical discrimination of, and context for, these structures
as well as general information on their chemical makeup (e.g., mean metallicities and, in some cases, an additional dimension of chemistry, such as [α/Fe]); these
broad brushstrokes represent an important step in characterizing stellar populations and constraining galactic
evolution models.
Meanwhile, high-resolution stellar spectroscopy has become an increasingly indispensable tool for providing the
necessary detail to discriminate galaxy evolution models. Accurate multi-element chemical abundances provide insight into the stellar initial mass functions, and
histories of star formation and chemical enrichment of
stellar populations, which, in turn, fuel ever more sophisticated galactic dynamical and chemodynamical models
(e.g., Chiappini et al. 2001, 2003; Sellwood & Binney
2002; Abadi et al. 2003; Bournaud et al. 2009; Schönrich
& Binney 2009; Minchev & Famaey 2010; Bird et al.
2013; Minchev et al. 2013, 2014; Kubryk et al. 2014).
Coupled with orbital information derived from precise
Majewski et al.
radial velocities, these data probe the role of dynamical phenomena such as large-scale dissipative collapses,
mergers, gas flows, bars, spiral arms, dynamical heating
and radial migration.
Conventional echelle spectroscopy programs to deliver
high resolution spectroscopic data useful for Galactic archaeology demand substantial resources, often on the
world’s largest telescopes. Consequently, while heroic efforts have been devoted to surveying stars in a wide variety of environments — including, e.g., dwarf spheroidals,
globular clusters, the Magellanic Clouds, tidal streams,
and the Galactic bulge — until very recently the solar neighborhood was the only region for which multiple hundreds or thousands of observations had been assembled for “Galactic field stars” (e.g., Edvardsson et
al. 1993, Bensby et al. 2003, Fuhrmann 2004, Venn et
al. 2004, Nissen & Schuster 2010, Soubiran et al. 2010,
Adibekyan et al. 2012, 2013, Bensby et al. 2014) — and
with these samples traditionally relying on kinematicallyselected samples to harvest from the nearby stars of accessible apparent brightnesses a broad spread of stellar
ages and population classes. For stellar populations not
represented in the solar neighborhood, like the Galactic bulge, and for in situ studies of field stars outside
of the solar neighborhood, high resolution observations
are only now generating samples with hundreds of stars.
In the inner Galaxy where foreground dust obscuration
is a formidable challenge, many previous samples were
concentrated to a handful of low extinction sightlines,
such as Baade’s Window. Unfortunately, the aggregate
of these piecemeal collections of spectroscopic data, heterogeneously assembled, can give a biased and incomplete view of the Milky Way.
Truly comprehensive evolutionary models for the
Milky Way must be informed and constrained by statistically reliable, complete or at least unbiased Galactic
archaeology studies, which requires the construction of
large, truly systematic and homogeneous chemokinematical surveys covering expansive volumes of the Milky Way
and sampling all stellar populations, including, in particular, those dust obscured inner regions where the bulk of
the Galactic stellar mass is concentrated. A number of
ambitious “Galactic archaeology” spectroscopic surveys
that aim to fill this need have been previously undertaken
— such as RAVE (Steinmetz et al. 2006), SEGUE-1
(Yanny et al. 2009), SEGUE-2 (Rockosi et al. 2009), and
ARGOS (Freeman et al. 2013) — are currently underway — such as LAMOST (Cui et al. 2012), Gaia/ESO
(Gilmore et al. 2012), GALAH (Zucker et al. 2012), and
Gaia (Perryman et al. 2001) — or are envisaged — e.g.,
those associated with the WEAVE (Dalton et al. 2014),
4MOST (de Jong et al. 2014), and MOONS (Cirasuolo
et al. 2014) instruments. While each of these surveys
focuses on large samples of & 100, 000 stars, all of those
surveys past and ongoing are based on optical observations and are therefore strongly hampered by interstellar obscuration in the Galactic plane (Fig. 1, bottom);
this makes it challenging to sample significant numbers
of stars within the very dusty regions of the Milky Way
that are both central to constraining formation models
and encompass most of the Galactic stellar mass (and
some projects, like the RAVE, SEGUE and GALAH surveys, specifically avoid low Galactic latitudes). Therefore, with optical wavelength surveys it is challenging to
3
assemble a systematic census having comparable or sufficient representation of all Galactic stellar populations
and across wide expanses of the Galactic disk and bulge.
While other surveys, such as BRAVA (Rich et al. 2007),
ARGOS (Freeman et al. 2013), and Gonzalez et al. (2011)
aim to fill at least part of this void by specifically focusing
on the Galactic bulge, they utilize target selection criteria that differ from those of surveys of other parts of the
Milky Way, which makes it difficult to generate a holistic
picture of stellar populations and their potential connections. Moreover, apart from GALAH and the Gaia/ESO
survey, these other studies are limited to medium resolution spectroscopy (R < 10, 000; Fig. 1), so are unable
to provide reliably the kind of detailed elemental abundance information that is now a key input to the models,
while at the same time the moderate velocity precisions
can limit their sensitivity to more subtle, second order
dynamical effects (e.g., perturbations by spiral arms and
the bar, dynamical resonances, velocity coherent moving
groups and streams).
Fig. 1.— APOGEE in the context of other Galactic archaeology surveys, past, present and future. The top panel shows the
number of Milky Way stars, observed or anticipated, as a function
of survey resolution. For those surveys with at least a resolution
of R = 10, 000, the bottom panel shows the expected nominal
depth of the survey for a star with MV = −1 in the case of no
extinction (right end of arrows) and in the case of AV = 10 (left
end of arrows). In both panels, already completed surveys are
shown in black, ongoing surveys in dark gray, and planned surveys in light gray. For surveys with multiple resolution modes,
data in the top panel are plotted separately for high resolution
(HR), medium resolution (MR) and/or low resolution (LR). For
the Gaia/ESO survey, data for “Inner Galaxy” and “Halo” subsamples are shown separately as well. “Gaia-RV” includes Gaia
high resolution spectra of enough S/N to deliver radial velocities,
whereas “Gaia” indicates only those with S/N high enough for
abundance work. For Gaia we adopted AG /AV from Jordi et al.
(2010), assuming (V −IC )0 = 1.7; sample numbers were taken from
http://www.cosmos.esa.int/web/gaia/science-performance.
1.2. APOGEE: Basic Architecture and Motivations
4
APOGEE
In contrast to previous and ongoing surveys, the
Apache Point Observatory Galactic Evolution Experiment (APOGEE) in Sloan Digital Sky Survey III (SDSSIII) was designed to tackle the fundamental problem of
galaxy formation through the first large-scale, systematic, precision chemical and kinematical study specifically optimized to include exploration of the “dusthidden” populations in the Milky Way. As planned,
APOGEE aimed to build a database of high-resolution
(R∼22,500), near-infrared (1.6 µm H-band) spectra for
over 105 stars — predominantly red giant branch (RGB)
and other luminous post-main-sequence stars — across
the Milky Way, but with particular emphasis on obtaining significant representation from heavily dust-obscured
parts of the Galactic disk and bulge. Operationally, this
plan, now successfully executed, exploits several key advantages:
• Near-infrared observations profit from a selective
extinction many times lower (for H-band, a factor
of 6) in magnitudes (i.e., 250 times in flux) than
that at visual wavelengths.
• High resolution spectra provide the chemical abundance and radial velocity precision needed for constraining Galactic evolutionary models and, in the
H band, sample lines of numerous elements up to
and including the iron peak and for which non-LTE
departures are typically small.
• Collectively, RGB stars, asymptotic giant branch
(AGB) and red supergiant (RSG) stars are good
tracers of the disk and bulge, together sample populations of all ages and metallicities, and are luminous in the NIR. Meanwhile the high sky density
of these stellar types — combined with the large,
3 deg diameter Sloan 2.5-m telescope field-of-view
(FOV) and high throughput, multifiber plugplate
handling system — allows simultaneous observation of several hundred targets at a time, and thousands per night.
Together these advantages translate into a Milky Way
survey trade-space “sweet spot” that permits efficient,
high resolution, near-infrared spectroscopic study of
large numbers of stars that are not easily accessible to
optical programs, and enables a consistent database of
stellar spectra to be assembled across all Galactic stellar populations, from the inner bulge to the more distant
Galactic halo. Thus, with APOGEE, it is possible for the
first time to explore and compare with great statistical
significance the chemokinematical character of all Milky
Way stellar subsystems using the same set of chemical
elements and line transitions represented in data of uniform high quality that has been gathered, processed and
analyzed identically.
1.3. High Level Science Goals
constrain Galactic chemical evolution (GCE) models. Among the target elements are the preferred
GCE tracers and most common metals — i.e., carbon, nitrogen and oxygen — as well as other metals with particular sensitivity to the star formation
history (SFH) and the initial mass function (IMF)
of stellar populations.
2. To derive high precision kinematical data useful for
constraining dynamical models for the disk, bulge,
bar and halo, and for discriminating substructures
within these components.
3. To access the often ignored, dust-obscured inner
regions of the Galaxy, and for the observed stars in
these regions derive the same data as is available for
other, more accessible stellar populations, which
will also be included in the survey; furthermore,
by collecting large survey samples, provide statistically reliable measures of chemistry and kinematics
in dozens of Galactic zones (R, θ, Z) at the level
currently available in the solar neighborhood.
4. To contribute to explorations of the early Galaxy
by inferring the properties of the earliest stars,
thought to reside or to have resided in the Galactic bulge (Tumlinson 2010). This can be achieved
either by detecting them directly if they survive
to the present day, or (more likely) by measuring
their nucleosynthetic products in the most metalpoor stars that do survive.
5. To achieve a dramatic (more than two orders of
magnitude) leap in the total number of available
high resolution, high S/N infrared stellar spectra,
which will enable substantial advances in stellar astrophysics, GCE modeling, and dynamical modeling of the Milky Way.
Among the more specific issues that APOGEE addresses are:
• Completing the first systematic determination of
the 3-D chemical abundance distribution — for numerous elements — across the Galactic disk, determining the Galactic rotation curve and examining
correlations between abundances and stellar kinematics at all disk radii.
• Determining distribution functions of chemical
abundances for a variety of elements in the bulge,
bar(s) and inner disk, and probing correlations between abundances and kinematics there, with the
goal of investigating the physical mechanisms that
connect these structures and determining the origin(s) of the bulge.
The principal scientific goals of APOGEE, which together provide a broad but integrated approach to furthering our understanding of galaxy evolution, are:
• Establishing the nature of the Galactic bar and spiral arms and their influence on the disk through
detailed assessment of abundances and velocities
of stars in and around them.
1. To measure high precision abundances for multiple elements in ∼ 105 stars across the Galaxy, and
derive distributions of these chemical properties to
• Assessing the properties that discriminate the thin
and thick disks to clarify the nature and origin of
the latter.
Majewski et al.
• Drawing a comprehensive picture of the chemical
evolution of the Galaxy via the placement of strong
constraints on the initial mass function and star
formation rates of the bulge, disk and halo as a
function of position and time.
• Searching for and probing the chemistry and dynamics of low-latitude substructures in both the
disk and halo, whether from dynamical resonances
or the accretion of satellites.
• Investigating the kinematics and chemistry of the
Galactic halo and its substructure, and using them
to assess the relative contribution of accreted versus stars formed in situ.
• By reference to other available optical, near-IR,
mid-IR and radio data, exploring the interstellar
medium, mapping the Galactic dust distribution
and constraining variations in the interstellar extinction law.
• By combining spectroscopic data with the detailed
information on stellar structure provided by asteroseismology surveys, deriving accurate ages for
Galactic field stars, which provide key timestamps
for the exploration of all manner of Galactic evolutionary phenomena.
• Through the marriage of accurate stellar parameters and detailed chemical compositions from
APOGEE with accurate asteroseismological data,
providing fundamental constraints on models of
the structure of stellar interiors, opening doors to
progress in important areas of stellar physics.
1.4. Goals of this Paper
The goal of the present paper is to give a broad
overview of the APOGEE survey, with particular focus
on the scientific motivations and technical rationale that
led to the instrument and survey design choices (§2). The
“birds-eye” descriptions of the APOGEE project given
here are at a level intended to give the potential user of
APOGEE data sufficient background to understand the
basic structure of the instrument (§3) and survey (§4),
how the data were collected (§5) and processed (§6), and
what the data look like and how they may be accessed
(§8). We also summarize how the survey met its intended
goals (§7), further illustrated via several science demonstrations (§7.4). The latter also introduce some of the
variety of applications to which APOGEE data may be
directed. Based on the success of the APOGEE project,
a new collaboration has been formed to expand upon
this initial survey via the APOGEE-2 project; these and
related future efforts are discussed briefly in §9.
This paper is a primer to those interested in a general
understanding of the overall structure of the APOGEE
survey. For more details on particular aspects of the survey, users are encouraged to consult a series of focused
technical papers that address various specific elements of
the survey, the software and algorithms used to produce
the publicly released databases, and post-survey assessments of the calibration and accuracy of the data (Table
1). These papers and other survey documentation are
described further in §8.4. On-line information describing
5
the data release file formats and available on-line tools
for data visualization and download may be found at
http://www.sdss.org.
TABLE 1
APOGEE Survey Technical Papers
Topic
Spectrograph
Target Selection
Data Reduction Pipeline
Stellar Atmosphere Models
Stellar Spectral Libraries
APOGEE Line List
Tests of the APOGEE Line List
Stellar Parameters and Chemical
Abundances Pipeline (ASPCAP)
ASPCAP Calibration for DR10
Tests of Individual Element
Abundances from ASPCAP
Overview of DR12 APOGEE data
Kepler Asteroseismology Collaboration
CoRoT Asteroseismology Collaboration
Reference
Wilson et al. (2015)
Zasowski et al. (2013)
Nidever et al. (2015)
Mészáros et al. (2012)
Zamora et al. (2015)
Shetrone et al. (2015)
Smith et al. (2013)
Garcı́a Pérez et al. (2015)
Mészáros et al. (2013)
Cunha et al. (2015)
Holtzman et al. (2015)
Pinsonneault et al. (2014)
Anders et al. (2015)
2. TOP LEVEL TECHNICAL REQUIREMENTS
The requirement for accurate abundances of a large
number of chemical elements necessitates an intricate
interplay between S/N , spectral coverage and spectral
resolution, which are the most fundamental factors that
drove the APOGEE instrumental design. On one hand,
the desire to obtain abundances for a large number of
chemical elements calls for a wide wavelength baseline,
so that numerous absorption lines from many chemical species are represented in the observed spectra. On
the other hand, the accuracy achievable in abundance
analysis work is strongly dependent on spectral resolution, which, for a fixed detector format in the limit of
Nyquist sampling, is inversely proportional to spectral
bandwidth. Additionally, the lower the resolution, the
higher is the S/N required to achieve a given abundance
accuracy goal. Finally, the higher the S/N , the fewer the
stars that can be observed in a given time period, for a
given multiplexing power. We discuss here the scientific
considerations that led to the final instrument technical
requirements for APOGEE.
2.1. Wavelength Window of Operation
Recent technology development has made high resolution NIR spectroscopy a new and vigorous area of astrophysical investigation, particularly in the area of stellar
atmospheres analysis. The value and promise of high
resolution NIR spectroscopy for exploring stellar abundances is attested by the growing number of papers on
the subject over the past decade using instruments suitable for the purpose on the world’s largest telescopes —
e.g., CRIRES on the VLT, NIRSPEC at Keck, IRCS at
Subaru, and, formerly, Phoenix at Gemini-South (e.g.,
Rich & Origlia 2005; Cunha & Smith 2006; Cunha et al.
2007; Ryde et al. 2010; Tsuji & Nakajima 2014). While
the flow of high resolution NIR data has recently seen a
dramatic upturn, the study of stellar photospheres on the
basis of NIR spectroscopy has a long tradition (e.g., see
6
APOGEE
the early review by Merrill & Ridgway 1979). The current state of the art in interpreting these data is proving
highly successful, competitive with, and complementary
to, traditional analyses in the optical (see references below).
To probe the largest distances in the Galaxy most easily one should focus on the intrinsically brightest population tracers. A particular advantage realized by working in the NIR is that the intrinsically brightest common stars found in different aged populations — RGB,
AGB and RSG stars (collectively referred to as “giants” throughout this paper) — all have cool atmospheres, and are even brighter in the infrared than at
optical wavelengths. Moreover, selecting for red stars
in dereddened color-magnitude diagrams made from a
magnitude-limited survey like 2MASS guarantees a virtually giant-dominated sample. Fortunately, the analysis of giant star atmospheres is an area that has received
particular attention in high resolution NIR spectroscopy,
given that these stars are the most accessible in star clusters, resolved galaxies (like the Magellanic Clouds), and
fields towards the Galactic Center, like Baade’s Window. The earlier papers by Smith & Lambert (1985,
1986, 1990) focusing on the CNO abundances in red giant stars were among the first efforts to explore chemical
abundances from high-resolution spectra in the infrared.
More recently, the analysis of high-resolution spectra in
the H band for stars in the Magellanic Clouds as well as
the Galactic bulge and center (Smith et al. 2002; Rich &
Origlia 2005; Cunha & Smith 2006; Cunha et al. 2007;
Ryde et al. 2010) have helped to demonstrate the feasibility of determining precise chemical abundances in the
H-band and have helped to lay the foundation for the
APOGEE Survey.
Choice of the specific NIR wavelength range to be used
for APOGEE involved optimizing a trade-off between
competing desires:
• Penetration of Interstellar Dust: The longer
the infrared wavelength observed, the smaller is the
sensitivity of the light to the extinguishing effects
of interstellar dust, and the greater is the ability of
the survey to penetrate highly obscured regions of
the inner Galaxy.
• Thermal Background: At longer wavelengths
the contribution of the thermal background increases, and becomes significant in the K-band and
beyond.
• Airglow: The intensity of airglow emission (particularly from OH) varies across the near infrared,
with the weakest lines in the J-band, and the
strongest in the H-band.
• Telluric Absorption: The ranges of the groundbased NIR bands are defined by major telluric
absorption bands, most especially from CO2 and
H2 O; however, bands of various strengths from
these molecules, as well as from CH4 , O2 and O3 ,
are found all across the near-infrared.
• Available Line Transitions: Some key atomic
elements, like Fe, C, N and O (the latter expressed
in molecular line absorption from diatomics like
CO, OH and CN) are represented by spectral features all over the NIR, whereas other interesting
elements, like K, F, Al and Sc, have only a few
lines.
Weighing the various aspects of this trade-space led
to the selection of the H-band for APOGEE, with relatively strong weighting given to the first two considerations above: While the K-band is less sensitive to dust
extinction than is the H-band (AK /AV ∼ 1/9 compared
to AH /AV ∼ 1/6; e.g., Cardelli et al. 1989), the H-band
still confers a powerful degree of insensitivity to dust,
whereas, in the meantime, S/N considerations motivate
avoiding the large K-band backgrounds. Moreover, a Kband instrument requires much greater consideration to
mitigating contamination from local sources of thermal
background than does an instrument working in the Hband.1
Unfortunately, while the above thermal background
issues favor it, the H-band does include by far the
strongest lines of the OH airglow spectrum. On the other
hand, in principle, with high enough resolution the impact of those airglow lines could be confined to a small
fraction of the total spectrum, whereas in the K-band
the thermal background would affect all pixels. In the
ultimately selected APOGEE spectral range, the airglow
spectrum includes about a dozen strong lines and a few
dozen weaker lines (e.g., Fig. 2); coincidentally, these
lines span the entire APOGEE spectral region, which
makes them potentially useful for wavelength calibration.
The shape of the telluric absorption spectrum strongly
drove the primary part of the H-band worth considering
for APOGEE. The H-band itself was defined as the atmospheric transmission window between the strong and
broad water absorption bands at ∼1.4 µm and ∼1.9 µm.
By far, the lowest absorption in this region is in the range
of approximately 1.5-1.75 µm, although this region is
punctuated by the 30013 ← 00001 and 30012 ← 000012
bands of the CO2 molecule (Miller & Brown 2004), which
cover roughly the λλ1.568-1.586 and λλ1.598-1.617 µm
spectral intervals, respectively (Fig. 2). An initial, twodetector design of APOGEE sought to avoid most of
these bands, but eventually these bands were almost fully
included in the near-contiguous wavelength coverage of
the final, three-detector APOGEE instrument (§2.3).
2.2. Chemical Elements
In principle, different near-infrared windows offer some
variance in available elements, but for many important
elements (C, N, O — the most abundant metals in the
universe — and the fiducial element Fe) there is ample
representation in all three of the NIR bands (J, H and
K). Inspection of the Hinkle et al. (1995) infrared atlas reveals the J-band to have lines for almost the same
set of elements as the H-band, but the H-band lines
1 Indeed, initial designs for the APOGEE spectrograph considered the notion of a highly accessible bench spectrograph operating in a commercial-grade food storage freezer, but eventually
converged toward the conventional liquid-nitrogren-cooled cryostat
design described in §3.2 (not least because of problems with the
significant heat dumping into the telescope environment that the
freezer would contribute).
2 The notation for the vibrational states follows the convention
established by HITRAN (Rothman et al. 2013).
Majewski et al.
7
(see, e.g., Rayner et al. 2009, in particular their Figures
10 and 11). And while a number of α-elements are represented in either the H or K bands, other atoms with
few transitions are represented in only one or the other
(e.g., the H-band offers the important odd-Z elements Al
and K). While these trade-offs — typically between elements tracking similar nucleosynthetic families — were
not strong drivers in the decision process leading to the
choice of the broadband NIR bandpass in which to operate (i.e., J versus H versus K), they did play a larger role
in fine tuning the precise limits of the wavelength coverage (see below). Fortunately, the H-band, preferred for
other reasons given above, was determined to offer an appealingly wide range of chemical elements that could be
sampled, covering a range of nucleosynthetic pathways.
A detailed visual inspection of the infrared spectrum of
Arcturus by Hinkle et al. (1995, Fig. 2) was used to define
the specific limits of the APOGEE spectral range. Initially, a survey of potentially accessible elements (atomic
and in molecular combinations) in the H-band was made,
and showed potentially useful representation from the
following elements: C, N, O, Na, Mg, Al, Si, S, K, Ca,
Ti, V, Cr, Mn, Fe, Co and Ni (element by element maps
are shown in Fig. 34 in Appendix A). This is a useful subset of atomic species with which to probe most
types of nucleosynthesis. Moreover, many of these elements are now accessible to integrated spectroscopy of
extragalactic systems, which makes it possible to place
the Milky Way in context with other galaxies having a
range of masses and morphological types. Unfortunately,
conspicuously absent from this initial assessment are any
significant lines from neutron-capture elements, a general
problem across the NIR.3
The above panoply of H-band-accessible elements offers a number of potentially interesting insights into various aspects of Galactic chemical evolution (see, e.g., Matteucci 2001 and the recent review of nucleosynthesis and
chemical evolution by Nomoto et al. 2013):
Fig. 2.— In three overlapping wavelength regions, the distribution of telluric absorption (top spectra in each panel), airglow (middle spectra), and atomic lines in the spectrum of the star Arcturus
(bottom spectra). Some prominent atomic lines in the Arcturus
spectrum that guided the ultimate selection of the APOGEE wavelength region are identified and color-coded as high priority (red),
medium priority (blue) and lower priority (black). Also indicated
are the extremes in the potential shift in the lines from extremes in
radial velocity variation for potential (e.g., halo) Milky Way stars
(adopted as ±700 km s−1 in the lines).
tend to be stronger in the spectra of giant stars than
their J-band counterparts, as attested by inspection of
medium resolution NIR spectra from the IRTF library
• C, N: Important elements produced in significant
amounts in intermediate-mass stars (Ventura et al.
2013), and thus sensitive to ∼100 Myr timescales of
star formation and chemical evolution. Carbon is
synthesized in both massive stars (M ≥ 10M ) and
lower-mass AGB stars (M ∼ 1 − 4M ), in roughly
equal amounts (Nomoto et al. 2013). Because AGB
stars produce no Fe, [C/Fe] can present an interesting behavior as a function of time in systems with
ongoing star formation and chemical enrichment:
initially increasing due to the contribution by corecollapse type II supernovae (SN II) and AGB stars,
then declining as a result of the onset of enrichment by Type Ia supernovae (SN Ia). Moreover,
because oxygen is produced in large amounts by SN
II, the C/O ratio tracks the relative contributions
of low to intermediate-mass stars versus massive
stars in a given stellar population. Nitrogen is produced efficiently in intermediate-mass AGB stars
(Karakas 2010), and there are suggestions in the
3 Subsequent work (e.g., Appendix E) has resulted in the identification of weak lines from several neutron-capture elements —
e.g., associated with Nd II and Ce III — in the APOGEE spectra of some s-process enhanced stars (e.g., Majewski et al. 2015;
Shetrone et al. 2015).
8
APOGEE
literature (Chiappini 2013, and references therein)
for an important contribution by massive stars as
well. Analysis of integrated spectra of M31 globular clusters (Schiavon et al. 2013) and early-type
galaxies (Schiavon 2007; Conroy et al. 2014) suggests that secondary enrichment was important in
these systems. Although N can exhibit complicated behavior as a result of chemical evolution,
it provides information on the relative importance
of intermediate-mass stars to chemical evolution.
Finally, because the [C/N] ratio is affected by internal mixing, it is a function of stellar mass, metallicity, and evolutionary stage, which suggests that
it might be useful for relative age determinations
of stellar populations (e.g., Masseron & Gilmore
2015).
• O: The quintessential SN II yield from hydrostatic
He-burning in massive stars and the most abundant element in the universe, after hydrogen and
helium. The timescale for the release of oxygen
by SN II is much shorter than that of iron by
SN Ia (e.g., Tinsley 1979). Therefore, one can be
argue that [O/H] is a more suitable and sensible
chronometer and independent variable than [Fe/H]
as a surrogate for “metallicity” in investigations of
temporal abundance ratio variations benchmarked
by overall enrichment level. That iron is more
commonly used to indicate stellar metallicity is at
least partly historically-rooted in the relative ease
with which [Fe/H] can be estimated from analysis of high resolution blue/optical spectra of solar
type stars. However, because the H-band includes
many OH and CO lines that can be easily measured (and modeled) in the spectra of cool giants,
APOGEE can provide reliable and precise [O/H]
abundances for large stellar samples to lend better insights into crucial observables such as, e.g.,
the age-metallicity relation in different Galactic
subcomponents. Moreover, stellar oxygen abundances can be more directly compared with gasphase metallicities, which are predominantly based
on measurements of oxygen lines (e.g., Kewley &
Ellison 2008). The [O/Fe] ratio has been extensively used as an indicator of the relative contribution of SN II and SN Ia to chemical enrichment,
which makes it sensitive to the timescale and/or
efficiency for star formation as well as the shape of
the high-mass end of the IMF (e.g., Tinsley 1979,
1980; Wheeler et al. 1989; McWilliam 1997).
• Mg: Another important α-element, Mg is an excellent complement to O. Its main isotope, 24 Mg is
produced in massive stars during carbon burning.
Therefore, magnesium can also constrain enrichment by SN II, having become commonly used in
part because it is relatively easier to measure than
oxygen in optical spectra, with early abundances
being based on medium resolution spectra (Wallerstein 1962; Tomkin et al. 1985; Laird 1986).
When combined with oxygen, magnesium can both
probe the importance of Wolf-Rayet winds in chemical evolution and provide insights on the slope of
the stellar initial mass function (IMF) (e.g., Ful-
bright et al. 2007; Stasińska et al. 2012; Nomoto
et al. 2013, and references therein). Magnesium
is also important as the main element constraining the [α/Fe] ratio from integrated-light studies
of extragalactic stellar systems (e.g., Worthey et
al. 1992; Schiavon 2007). Thus, Mg measurements
may provide a key bridge between Galactic and extragalactic chemical composition studies and facilitate the placement of the Milky Way within the
broader context of galaxy evolution. In early-type
galaxies (Worthey et al. 1992) and, to a lesser extent, in the bulges of spirals (Proctor & Sansom
2002) magnesium is found to be enhanced relative
to iron, which is commonly interpreted as due to a
short timescale for star formation in those systems.
• Na, Al: Odd-Z elements. Sodium is produced
during carbon burning and returned to the ISM
via SN II. Aluminum, in turn, is expected to be
produced mostly during neon burning, with only a
small contribution from carbon burning. The SN II
yields for these elements are moderately dependent
on metallicity (Nomoto et al. 2013). Both Na and
Al also particiapte in H-burning in intermediatemass stars (e.g., Karakas 2010), so these elements
can also monitor the impact of intermediate-mass
stars on chemical evolution. Interestingly, studies
of chemical evolution in the Galactic thin and thick
disk and halo reveal different trends for the abundances of these elements as a function of [Fe/H]
(e.g., Bensby et al. 2014).
• Si, S: These α-elements are produced mostly in SN
II (with small amounts in SN Ia). Silicon, as 28 Si,
is the most abundant product of oxygen burning,
with the dominant sulfur isotope, 32 S, also synthesized in oxygen burning (e.g., François et al. 2004;
Nomoto et al. 2013). The abundances of these elements, in principle, provide constraints on the stellar IMF by comparison to the abundances of lighter
α-elements O and Mg (e.g., McWilliam 1997).
• K: Another odd-Z element whose chemical evolution is poorly understood. Shimansky et al. (2003)
suggest that the evolution of K comes from hydrostatic oxygen burning and we expect an increase in
[K/Fe] with [Fe/H].
• Ca, Ti: Two more elements with strong ties to
SN II yields, but which may also have some fraction produced in SN Ia (e.g., François et al. 2004;
Nomoto et al. 2013). In Galactic populations, these
elements display similar trends to those of O, Mg,
Si, and S, but there has been debate in the literature as to whether they behave like SN Ia products in early-type galaxies (e.g., Milone et al. 2000;
Saglia et al. 2002; Cenarro et al. 2004; Schiavon
2010; Conroy et al. 2014).
• V: Produced in both explosive oxygen-burning and
silicon burning, 51 V is synthesized through radioactive parents, 51 Cr and 51 Mn, and is made in both
SN II and SN Ia (Nomoto et al. 2013). Reddy et al.
(2006) find [V/Fe] to be approximately solar in the
thin disk and slightly enhanced in the thick disk
(by about 0.1 dex).
Majewski et al.
• Mn: While most iron-peak elements follow iron,
Mn does not, with [Mn/Fe] decreasing with decreasing [Fe/H]. Manganese is produced mainly
from radioactive decay of 55 Co in both corecollapse and Type Ia supernovae (Nomoto et al.
2013); the dominant source of Mn has not been
definitively identified.
• Cr, Fe, Co, Ni: These elements represent the
Fe-peak in APOGEE spectra and are produced in
varying amounts in both SN Ia and SN II.
The mere presence of a line transition, of course, is not
sufficient for it to provide scientifically useful abundance
measurements. As a means to assess the identified lines,
extensive tests were made of model RGB spectra of different metallicities ([Fe/H] = −2, −1, 0) at a number
of potential spectrograph resolutions to determine their
suitability for 0.1 dex precision measurements (see §2.3).
Given the results of these tests, and to inform the final
selection of the specific spectral coverage, these elements
were ranked in a prioritization scheme that considered
not only the nucleosynthetic family to which the element
belonged and their value to mapping Galactic chemical
evolution, but the strength and number of the available
transitions:
9
spectral range is achieved and as ASPCAP’s performance
is improved.
2.3. Resolution, S/N and Specific Wavelength Limits
As with most spectrographs, the precise specifications
of the APOGEE spectrograph were the product of balancing the competing benefits of high resolution, high
S/N and a broad wavelength range. To model these factors we calculated a series of synthetic H-band spectra
for RGB stars (Tef f = 4000 K, log g = 1) with [Fe/H]
= −2, −1, 0, at a number of values for resolving power
between R = 15, 000 and 30,000. For each case we computed two spectra, one with solar-scaled composition,
and a second in which the abundance of a particular
element, X, was modified by ∆[X/Fe] = 0.1. These calculations were used to derive an estimate of the S/N
required to measure abundance variations of the order of
0.1 dex at each resolution, as described in Appendix B.
The results are summarized in Figure 3.
• Top priority (i.e., “must have” elements): C, N, O,
Mg, Al, Si, Ca, Fe, Ni.
• Medium priority (i.e., valuable elements worth trying to include in APOGEE, but that should not
necessarily drive requirements for the survey): Na,
S, Ti, Mn, K.
• Lower priority (i.e., “if at all possible” elements —
interesting elements but not deemed essential for
success): V, Cr, Co.
A census of the H-band shows that the reddest third
(approximately 1.7-1.8 µm) is the most deficient in interesting spectral lines whereas the middle third (approximately 1.6-1.7 µm) has the highest density. Moreover,
the 1.7-1.8 µm subwindow has significantly worse telluric
absorption (Fig. 34). This ultimately drove the primary
APOGEE wavelength of interest to roughly 1.5-1.7 µm.
The precise wavelength limits were set by the specific
line transitions desired, after detailed assessment of resolution and S/N considerations.
The ultimately adopted wavelength setting includes
sufficient lines for abundance work on all of the top and
medium priority elements listed above. However, a subsequent assessment of the available lines for the low priority elements suggested that abundances for Cr and Co
would be very difficult to obtain reliably, given the excitation potential, log gf and strength in the Arcturus
spectrum of these lines. Therefore, abundances of Co
and Cr were not attempted in the first round of elemental abundance determinations leading up to DR12. The
additional element Cu, on the other hand was not considered as a viable APOGEE product when the survey was
initially conceived, but later Cu was successfully explored
in FTS spectra of standard stars in the APOGEE region
by Smith et al. (2013). The situation of these elements
will be reevaluated in the near future as a better understanding of available line transitions in the APOGEE
Fig. 3.— Summary of the S/N experiments described in Appendix B for each of 15 chemical elements. For each, the minimum
required S/N to measure 0.1 dex precision abundances is plotted
for a variety of resolutions from R = 15, 000 to 30,000, and for
three metallicities, [Fe/H] = −2, −1, and 0. For Al, Si, and Mg
the data points for all three modeled metallicities fall on top of one
another.
These calculations give rise to a number of general considerations:
• Clearly the highest S/N are required at the lowest
metallicities and resolutions, with metallicity being
the stronger driver. For instance, measuring the
Mg abundance to 0.1 dex at [Fe/H] = −2.0 would
require S/N > 50 at R = 15, 000 and S/N > 25 at
R = 30, 000. At the other extreme, measuring K
to 0.1 dex requires S/N > 700 at R = 15, 000 and
S/N > 400 at R = 30, 000 for the same metal-poor
star (outside the range shown for this element in
Fig. 3).
10
APOGEE
• The Galactic thin disk is dominated by stars with
[Fe/H] > −1, for which the number of elemental
abundances that can be determined with 0.1 dex
precision is maximum for a given S/N . For example, at R = 21, 000 and S/N = 100 we are able to
measure all of the listed elements except Na, S and
V for thin disk stars.
• For more metal-poor stars, the challenging elements (at the top of Fig. 3 and Tables 3 and 4)
are measurable with less demanding precision. It
might also be possible to do at least a statistical analysis of abundance patterns in metal-poor
stars with the minimum nominal S/N by combining spectra for multiple stars of similar chemistry
or position in phase space.
• Obviously, for a constant exposure time, we can
achieve higher S/N by probing stars of brighter
magnitudes and thereby recover more of the challenging lines.
Even more specifically, this analysis led to the following
considerations:
• Na is challenging for all but the most metal-rich
stars (even ignoring that the available Na lines are
affected by non-negligible blending by molecular
lines), but we have Al as a substitute. Therefore
Na was not used as a requirement driver.
• V is similar in chemical character to Al, and behaves similarly to the α-elements Ca and Ti (Reddy
et al. 2006). Therefore, loss of this element for some
stars was not considered a substantial setback.
• S is perhaps the most valuable element with weak
lines in the potential APOGEE line list. The S
I lines at 15422Å and 15469Å are the cleanest
two lines, whereas the strongest line at 15478Å is
blended with a strong Fe I feature. In some ways Si
can play the same role in terms of constraining the
high mass end of the IMF, though the combination
of S and Si is better. While it was expected that S
could be measured for bright stars, it was accepted
that S should not be a requirement driver at the
nominal survey magnitude limit.
• Given the above logic that we would not use Na,
V or S to drive the survey specifications, it seemed
reasonable to adopt the measurement of the stellar
K abundance for [Fe/H] > −1 stars as a requirements driver.
• For metal-poor stars ([Fe/H] <
∼ –1) it was considered desirable to have, at minimum, O, C, Fe, Mg,
Si, Al, Ca and Ni, making the measurement of Ni
in all stars a requirements driver.
• Overall improved resolution lowers the S/N requirements, but the gains from R = 15, 000 to
R = 21, 000 are modest, according to the calculations. However, the above estimates were assumed
to be somewhat optimistic, given that telluric lines,
sky emission, and blends of stellar lines were not
considered. Telluric and sky lines will be better
removed at higher resolution. All elements studied have at least some lines that are free of telluric or sky interference for most stellar RVs, and
fairly isolated at solar metallicity and intermediate
temperatures (Teff ' 4000 K). However, at cooler
temperatures and similar metallicities, molecular
lines due to CN, CO, and/or OH affect virtually
all wavelengths in the H band.
Taking into consideration these calculations and the
wavelengths of the transitions of the target elements (all
those listed above, except Na, V, and S), we obtained
the following constraints on wavelength coverage: The
blue limit of the APOGEE range was set to capture the
single available K I line at 15160 Å as well as the best
Mn I lines at 15157-15263Å , for reasons discussed above.
Meanwhile, the red limit was set by the goal to make sure
to include at least one of the three Al I lines at 1672016770Å .4 The specified wavelength range also needed to
account for potential heliocentric velocity variations in
Galactic stars, and a contingency of ±700 km s−1 was
adopted.
Initially it was thought that the goals for the APOGEE
science might be met with a baseline, single grating instrument sampling two disjoint H-band windows, but a
desire to sample multiple lines for each element for redundancy, as well as the greater than linear gains of increased
spectral resolution drove to a three-detector design with
nearly continuous coverage from the K I to Al I lines.
Nevertheless, even with three detectors, the desired minimal spectral resolution leaves the short wavelength end
slightly undersampled. To address this problem, it was
decided that the three detector spectrograph would include a mechanism by which the focal plane arrays can
be dithered precisely by half pixel steps. By taking exposures in dithered pairs, the spectral resolution can be
recovered as properly (Nyquist) sampled through interpolation of the paired exposures during post-processing.
A final issue that had no bearing on the instrument
design but did bear on the allocation of survey resources
is that of unidentified lines. At the start of the survey,
approximately 6% of all lines deeper than 5% of the continuum within the APOGEE wavelength interval were
still not identified with a transition from a given excitation and ionization state of a known chemical element.
This number went up to 20% when all lines deeper than
1% of the continuum were considered. To improve this
situation, the APOGEE team initiated a collaboration
with a team of laboratory astrophysicists. For details,
we refer the reader to Appendix E.
2.4. Kinematical Precision
For many problems in large-scale Galactic dynamics
— e.g., measuring the disk rotation curve or the velocity
dispersions of stellar populations, sorting stars into populations, looking for kinematical substructures — velocity
precision at the level of 1 km s−1 per star is not only
4 In addition, there is a weak Co line at 16764Å and an atomic
C I line at 16895Å . Although CO should be fine as a carbon
abundance indicator, the atomic carbon line provides a check on
C abundances derived from molecules. While not put as a requirement, the C I line lies within the wavelength range recorded by the
spectrograph (see §3.4 and Fig. 5).
Majewski et al.
suitable, but substantially better than has been available in these kinds of investigations heretofore. However, the combination of high resolution and a very stable
instrument platform made possible achieving kinematical precision beyond these initial survey specifications.
In fact, the APOGEE instrument and the existing radial velocity software routinely deliver radial velocities
at a precision of ∼ 0.07 km s−1 for S/N > 20, while
the survey provides external calibration sufficient to ensure accuracies at the level of ∼ 0.35 km s−1 (Nidever
et al. 2015; §7.3), which allows more subtle dynamical
effects to be measured. For example, the detection of
pattern speeds of — or kinematical substructure in the
disk due to perturbations and resonances from — spiral
arms, the bar, or other (e.g., dark matter) substructure
(e.g., Dehnen 1998; Famaey et al. 2005; Junqueira et al.
2015), the detection of stellar binary companions (e.g.,
Terrien et al. 2014), the assessment of stellar membership in star clusters (e.g., Terrien et al. 2014; Carlberg
et al. 2015) or extended stellar kinematic groups (i.e.,
“moving groups” or “superclusters”) in the disk (e.g.,
Eggen 1958, 1998; Montes et al. 2001; Malo et al. 2013),
and the accurate measurement of stellar velocity dispersions in star clusters or satellite galaxies (Majewski et
al. 2013) are all made possible with radial velocity measurements of the RMS precision and external accuracies
routinely achieved by APOGEE for main survey program
stars. But it has been shown that even greater precision
and accuracy may be obtained from APOGEE spectra,
which greatly benefits sensitivity to low mass stellar companions (Deshpande et al. 2013) and the exploration of
the intricate dynamics of young star clusters (Cottaar et
al. 2014; Foster et al. 2015) greatly benefits from even
greater precision and accuracy.
2.5. Sample Size and Field Coverage
The largest detailed chemical abundance studies are
typically focused on stars in the solar neighborhood, and
include samples of order 103 stars (Venn et al. 2004;
Bensby et al. 2003). A primary goal of APOGEE is to
obtain similar-sized samples of several thousand stars in
many dozens of Galactic zones across the Galaxy, and
this led to a basic technical requirement to obtain data on
100,000 stars distributed across all major Galactic populations. For example, a typical prediction from GCE
models that we aim to test are gradients in mean abundance for critical elements (Fe, C, N, O, Al), with differences in the models seen at the level of a few 0.01 dex
at each radial or vertical point in the Milky Way. Discriminating the present models demands an accuracy in
mean abundances of ∼ 0.01 dex per Galactic zone, or
more than 100 stars with
√ 0.1 dex accurate abundances
in that zone assuming N statistics. Similar precisions
are needed to determine, within each zone, the variation of [X/Fe] with [Fe/H] or [O/H] (which are important discriminants of the IMF and SFH), and therefore require 100 stars with 0.1 dex accurate abundances
in each metallicity bin. Thus, deriving not only mean
abundances but accurate and useful multi-dimensional
abundance distribution functions (such as [α/Fe] and
[Fe/H]) in each zone requires orders of magnitude more
stars per zone. Such accounting (e.g., [dozens of Galactic zones][∼ 20 metallicity bins][100 stars/bin]) leads
to samples of ∼ 105 stars. Fortunately, such numbers
11
were estimated to be achievable if a three year observing
campaign were feasible within the duration of SDSS-III
(which had a well-defined end of mountain operations in
the summer of 2014; §2.7).
While a ∼ 105 sample of stars with R ≈ 22, 500 spectra
is orders of magnitude larger than had been previously
available for Galactic archaeology, implicit to making this
a true milestone is that the stars be distributed systematically and widely across the Galaxy, to include: (a) fields
that cover a substantial part of the Galactic bulge including the Galactic Center, (b) fields that span a substantial
fraction of the Galactic disk from the Galactic Center to
and beyond the longitude of the Galactic Anticenter, (c)
high latitude fields to map the halo, and (d) fields that
probe a variety of specific targets of interest, such as star
clusters (valuable as both science targets and calibration
standards) and known Galactic substructures (e.g., the
bar, disk warp/flare, tidal streams). In addition, a small
fraction of the survey time/fibers would be available for
potential ancillary science projects §4.3), though these
would drive neither the science requirements nor instrument design, nor significantly impact the net throughput
of the main survey.
2.6. Survey Depth and MARVELS Co-Observing
For APOGEE’s primary target — evolved stars — the
survey seeks to reach across the Galactic disk in moderate extinction, to the Galactic Center in fairly heavy
extinction, and to the outer halo in low extinction. With
some variation due to metallicity, the tip of the red giant
branch (TRGB) lies at MH ∼ −5.5. AGB stars extend
still brighter, whereas red clump stars have MH ∼ −1.5.
To achieve the goal of readily and abundantly sampling
all Galactic populations in situ, it was required that for
“typical” survey fields and exposure times that APOGEE
routinely be able to reach to a depth of H = 12.2, which
translates to probing the TRGB to 35 kpc for no extinction and to > 8.5 kpc (i.e., to at least the distance of
the Galactic center) through ∼ 3 magnitudes of H-band
extinction (AV ∼ 18). Thus H = 12.2 was adopted as
the “baseline” magnitude limit for “normal” APOGEE
fields.
Special consideration was required for bulge fields, for
which the considerable zenith distance even at culmination translates to short observing windows and more
extreme differential refraction at APO. To enable greater
bulge spatial coverage, a baseline magnitude limit of
H = 11.1 was implemented for these fields to reduce
the integration time by a factor of three. Nevertheless,
Galactic center distances are reachable for TRGB stars
when AH . 2.
However, in other fields longer integrations were anticipated to enable APOGEE to probe red clump stars in
low extinction fields to >8.5 kpc or TRGB stars to >50
kpc, or TRGB stars to the Galactic Center in fields with
AH ∼ 4 (AV ∼ 25). Such longer fields were not only
desired for APOGEE, but they were a necessary part of
the observing plan because, initially, APOGEE shared
bright time observations with the Multi-Object APO Radial Velocity Exoplanet Large-area Survey (MARVELS)
project (Ge et al. 2008; Zhao et al. 2009). The baseline MARVELS program observed fields for ≥ 24 epochs
at about 1 hour per visit; thus, at least some APOGEE
fibers on these same cartridges could sample fainter stars
12
APOGEE
by accumulating integrations of up to 24 hours. At
first, MARVELS “controlled” half of the bright time,5
so that about half of the APOGEE time was in these
“long fields”. Subsequent termination of the MARVELS
observing campaign in the second year of APOGEE observations enabled some reconfiguration of our observing
plan (§4.1.2).
2.7. Throughput
For throughput and target selection requirements, the
APOGEE team assumed that it would be able to observe during 95% of the available bright time (i.e., after
accounting for a ∼ 50% loss for weather plus one SDSSIII-wide engineering night per month) for the final three
years of SDSS-III. This high fraction would be achieved
by carrying out simultaneous MARVELS/APOGEE observing with the two instruments sharing the focal plane.
The ability to carry out such simultaneous observations
was thus a technical requirement for achieving the desired survey size and depth.
It was determined from the onset that APOGEE would
feature a 300-fiber spectrometer, which is the number of
spectra that can be maximally packed along the spatial
direction on a 2048 pixel-wide detector (allowing ∼7 pixels per spectrum, assumed to be sufficient to span both
the PSF of each spectrum and leave dark gaps between).
Initially it was assumed that 50 fibers would be needed
for simultaneous observations of sky and telluric calibration stars.6
As discussed above, the requirement of detailed and
precise chemical composition determinations drives
requirements of S/N ≈ 100 per pixel at resolution
R ≈ 22, 500. The requirement to observe ∼ 105 stars
then implied that, after adopting conservative estimates
for all variables, the instrument would have to achieve
this S/N at the typical observation depth H = 12.2 in 3
hours of total integration time: Nstars ≈
• (3 year observing campaign) ×
• (11 months per year7 ) ×
• (30 nights per month) ×
• (11 hours per night) ×
• (40% bright time) ×
• (95% of bright time to APOGEE) ×
• (50% clear weather) ×
• (250 targets per field) /
• (3 + 1.5 hours per field8 ) = 1.15 × 105 .
5 This control included some choice in field location, but primarily the cadence of observations.
6 In the end, this number was increased to 35 fibers for sky plus
35 for telluric absorption stars (§4.2.4).
7 One month is lost to summer shutdown during monsoon season.
8 This is assuming 1.5 hours of overhead per 3 hours of exposure
(30 minutes for each of three one hour visits; see §2.8) — a generous
overhead but one that includes some allowance for longer exposures
in sub-optimal conditions.
More detailed analyses that, for example, included lost
nights for engineering time, various weather models, and
more sophisticated observing plans all yielded estimates
within 15-20% of this conservative estimate.
2.8. Binary Stars, Field Visit Duration and Field Visit
Cadence
Because the majority of APOGEE targets are RGB
stars, a substantial fraction are expected to be singlelined binaries. The amplitudes of radial velocity variations in binary stars can reach as much as 10-20 km s−1 ;
thus it is useful for such binary systems to be identified and flagged so that they can, when necessary, be removed from APOGEE kinematical samples to minimize
deterioration of the precision of derived bulk dynamical
quantities for stellar populations — e.g., the inflation of
measured velocity dispersions.
Identification of the radial velocity variability associated with single line binaries can be achieved by splitting the total integrations for each star into visits optimized in cadence to identify the binaries with problematical barycentric velocities. Given the expected instrument throughput, it was understood early on that to
reach distances of interest for studying a large fraction of
the Galaxy (and in particular, crossing the full extent of
even just the near side of the disk for the nominal Galactic plane pointing) detector integrations of multiple-hour
net length would be needed. However, because differential refraction limits the duration of hour angle viability
for any drilled fiber plugplate9 , it is necessary to break
long exposures into multiple observing stints — either
using plugplates drilled for different hour angles (potentially observed on the same night) or using the same
plate observed on multiple nights. It was most efficient
and natural to adopt the latter solution and exploit the
multi-visit strategy for the added purpose of binary star
identification.
For effective identification of binaries, more velocity
samples over a longer time baseline is always preferable.
This desire must be balanced against that of survey efficiency, which pushes in the direction of breaking long
exposures into the fewest possible number of visits, to
limit the fraction of time surrendered to overheads of
plugplate cartridge (§3.1) changing and field acquisition.
While mountain observing staff showed that this overhead can be as low as 12-15 minutes per plugplate cartridge change, 15-20 minutes is a more realistic “typical” situation. Under these circumstances, field visits
of less than 30-45 minutes accrue substantial overhead.
Moreover, frequent visits of such short duration place
substantial physical demands on the observers. In any
case, there were only eight available “bright time” Sloan
plugplate cartridges available on which to put APOGEE
fibers, so that no more than eight APOGEE plugplates
were observable on a given night. Thus, given the tradeoffs between observing efficiency and differential refraction limits as well as the eight cartridge limit, it was
decided that the baseline APOGEE visit would include
about an hour of integration plus overhead (see §5.1) and
that the “nominal” survey field integration of ∼ 3 hour
length (see §2.7) would be divided into no less than three
9 For definitions of this and other terms specific to the fiber
system of the 2.5-m SDSS telescope, see §3.1.
Majewski et al.
visits.
With this basic multi-visit plan in place, one last requirement imposed is the adopted cadence for the visits.
To understand the potential effects of binary stars on
measured APOGEE dynamical quantities, and to assess
the best way to distribute three visits over time to maximize the ability to detect “problem” binaries, a series
of Monte Carlo simulations of stellar populations having nominal binary fractions and mass, period and orbital eccentricity properties was undertaken. The details of these models, wherein the parent sample of typical APOGEE targets had their radial velocities sampled
over varying time intervals and net spans, are given in
Appendix C.
These simulations showed that the majority of binary
systems (∼ 74 %) are not expected to adversely affect the
kinematical measurements, where “adversely affected”
had been defined as a measured velocity of the primary
star that is > 2 km s−1 different from the true systemic
motion of the binary system. Given the above visit strategy, the most effective way of identifying the remaining
26% of binaries is by calculating the radial velocity difference between every combination of paired measurements and flagging stars showing a maximum velocity
difference above a certain threshold (we adopted for our
modeling 4 km s−1 ). These simulations indicated that,
for a set of at least three radial velocity measurements
of 0.5 km s−1 precision, a temporal baseline spanning at
least one month was sufficient to make evident at least
a third of the remaining (26%) binaries most likely to
have a significant impact on the APOGEE survey velocity distributions. While longer baselines improve detectability, that improvement is only marginally better
for baselines lengthened to a full season of typical object
visibility (Fig. 35); thus, a requirement of at least a 25
day span for the visits to a single plugplate was adopted
as a rule, with a minimum span between epochs of 3 days.
In their CORAVEL study Famaey et al. (2005) find
13.7% of their local K giant sample to be in binaries and
that with their strategy (two radial velocity measurements per star spanning 2-3 years) and 0.3 km s−1 velocity accuracy “binaries are detected with an efficiency
better than 50 percent (Udry et al. 1997)”. Famaey et
al. actually find an even lower binary fraction of only
5.7% for their M giant sample. These numbers suggest
that one might expect 27.4% and 11.4% binary fractions
among K and M giants, respectively. If two-thirds of
26% of these (i.e., 9%) slip through the APOGEE ability to detect them, then perhaps only a few percent of
APOGEE targets would remain kinematically “problematical”, with measured velocities deviant from their systemic motion by more than 2 km s−1 . Even this fraction is likely an upper limit because: (a) one month is
the minimum temporal baseline, whereas, at survey end,
the median baseline for all multi-visit fields is almost
two months (see Fig. 18b); (b) a significant fraction of
the primary APOGEE sample — ∼32,600 stars, or 30%,
had more than three visits, by design or circumstance
(see Fig.18a); and (c) the per-visit velocity precision is
substantially better than 0.5 km s−1 (at 0.07 km s−1 ; see
§10.3 of Nidever et al. 2015). A more complete assessment of the detected APOGEE binary fraction is currently underway (Troup et al., in preparation).
13
3. SURVEY INSTRUMENTATION
The APOGEE survey is made possible through
the construction of the world’s first high-resolution
(R∼22,500), heavily multiplexed (300 fiber), infrared
spectrograph (Wilson et al. 2010,2015). This cryogenic
instrument (Fig. 4), covering wavelengths from 1.51µm
≤ λ ≤ 1.70µm, was conceived, designed and fabricated
in the University of Virginia (UVa) astronomical instrumentation laboratory, but with considerable collaboration on the design and fabrication of specific subcomponents from a number of SDSS-III institutions and private vendors. A full description of the instrument can be
found in Wilson et al. (2015). We provide here a broad
overview of the instrument sufficient to understand the
format and character of the data it delivers.
3.1. Fiber Train and Plugplate System
The APOGEE instrument leverages the wide-field (3
degree diameter field-of-view) capability of the Sloan 2.5m telescope (Gunn et al. 2006) at Apache Point Observatory, New Mexico (USA), and the highly efficient and
proven survey infrastructure that has led to the very successful SDSS-I and SDSS-II suites of experiments using
optical spectrographs (Smee et al. 2013). For the optical spectrographs, which are mounted to the telescope
back end, short-length (1.8 m) fiber optic bundles run directly from the telescope focal plane to the pseudo-slits of
the spectrographs. In contrast, because of the sheer-size
of the APOGEE spectrograph, it is housed in a separate building adjacent to the 2.5-m Sloan Telescope and
fed light via a single, approximately 40-m “long fiber”
run from the telescope. This set of 300 “long fibers”
(called the “fiber link”) is permanently attached to the
APOGEE instrument with one end of each fiber hermetically sealed inside the cold, evacuated cryostat and terminating at the spectrograph “pseudo-slit”. The warm
end of the fiber link terminates at the base of the telescope.
At the telescope APOGEE employs the same plugplate system designed for use in the SDSS-I and SDSSII surveys (Owen et al. 1998; Siegmund et al. 1998),
and, indeed, makes use of eight plugplate cartridges
from those previous surveys that were converted to
“bright time” operations through the incorporation of
distributed and mingled “anchor blocks” of fibers linked
to the MARVELS and APOGEE instruments. As with
other Sloan spectrographic surveys, aluminum plugplates
with precision-drilled holes matching the positions of
APOGEE targets in a specific sky field are interchanged
and manually plugged with the cartridge “short fibers”
each day in preparation for the ensuing night time observing, The APOGEE fibers are step index, multi-mode,
low-OH (i.e., “dry”) fibers with a 120 µm diameter silica core that subtend 2 arcseconds of sky at the telescope focus. The sets of “short fibers” installed in the
fiber plugplate cartridges terminate in pluggable, stainless steel ferrules that impose a fiber-to-fiber proximity
limit (the so-called “fiber collision limit”) of 71 arc seconds. Each APOGEE anchor block of 6 fibers covers a
linear extent across the plugplate cartridge equivalent to
a roughly circular sky patrol area of about a 1.0 degree
(22 cm) radius. However, the distribution of these anchor blocks is non-uniform, forming a ring around the
14
APOGEE
central part of the field. This arrangement allows either
a uniform plugging across the entire plate or a higher
central concentration with all 300 fibers in a relatively
narrow FOV. The latter application is for those plugplates that are drilled for high airmass (low declination)
fields, where all targets are selected within a restricted
FOV (potentially as small as a 1 deg diameter circle; see
§5.2).
One primary difference in the plugging process for
APOGEE plates compared to previous SDSS projects
is that APOGEE fiber plugging imposes one level of
fiber management by separating fibers into three Hmagnitude-defined groups that are plugged into holes
corresponding to the faintest, mid-brightness and brightest thirds of targets on each plate. This fiber management is accomplished by color-coding the target holes on
each plate either red, green or blue by their brightness
rank, and filling these holes with fibers having matching colored sheathing. No other requirement is imposed
on which science fiber is plugged into which hole. Each
anchor block has two fibers of each color, so that the
“bright”, “medium” and “faint” fibers are evenly distributed across the field. At the spectrograph end, these
different fibers are sorted along the pseudo-slit into a repeating pattern of faint-medium-bright-bright-mediumfaint to ensure that bright spectra are never placed next
to faint spectra in the spectrograph focal plane (this pattern of alternating spectrum brightness may be seen in
Fig. 14 below). This scheme minimizes the degree of contamination of any given spectrum by overlapping wings
of the PSF from the adjacent spectrum of a brighter star.
During survey operations, the short-length fibers in
each of the eight plugplate cartridges are mated to
the long-length fibers approximately hourly (after each
cartridge/plugplate change) via a custom-built “gangconnector” that simultaneously mates each of the 300
short fibers with its corresponding long fiber to within
a few µm accuracy. Because of the frequency of this
mating operation, the need for efficiency of operations,
and the sometimes dusty conditions at the site, no indexmatching gel is used in this fiber coupling operation; as
a result, there are some light losses at the connector, but
they are small enough (5%) that the ease of operation
without use of optical gels more than makes up for the
lost light.
An additional modification is required for the
APOGEE fiber mapping system. In the case of the optical Sloan spectrographs fiber mapping is undertaken after each plugplate is plugged by running a laser directly
up the pseudo-slit (which is integrated as part of the
cartridge) and recording which plugged fibers light up on
the plugged plate in succession. In the case of APOGEE,
however, the true instrument pseudoslit is inaccesible, as
it lies within the cryostat. Therefore, a warm copy of
that pseudoslit is mated to the gang connector for this
operation.
3.2. Spectrograph
3.2.1. Technological Innovations
Although the APOGEE spectrograph design is simple in concept, the sheer size of the optics and the need
to feed 300 fibers to a pseudo-slit inside a cryogenic instrument posed considerable technological challenges. In
particular, the success of the instrument depended on the
development of five distinct technologies:
1. Implementation of the custom-made “gang connector”, described above, which makes possible the simultaneous high-efficiency coupling of 300 infrared
transmissive fibers and enables rapid swapping of
telescope focal-plane plugplates.
2. Innovating hermetic fiber vacuum penetrations of
the cryostat stainless steel wall that simultaneously
limit stress-induced fiber focal ratio degradation
(FRD). This was accomplished after extensive testing of a wide range of materials and epoxies (Brunner et al. 2010) for the seal.
3. Collaboration with Kaiser Optical Systems, Inc.
(KOSI; Ann Arbor, MI) in the design and fabrication of a volume phase holographic (VPH) grating
larger than any previously deployed in an astronomical spectrograph via innovation of a technique
for precisely laying down multiple (three) holographic exposures onto one glass substrate (Arns
et al. 2010).
4. The design — in collaboration with New England
Optical Systems, Inc. (NEOS; Marlborough, MA)
— of a 6-element infrared transmitting camera that
includes several unprecedentedly large diameter (40
cm) lenses of monocrystalline silicon.
5. The creation — in collaboration with the James
Webb Space Telescope (JWST) Near Infrared
Camera (NIRCam) team, Princeton University,
and Johns Hopkins University — of a precision multi-array mount and translation stage for
three Teledyne HAWAII-2RG (2048×2048) detectors. With this stage, the arrays can be “dithered”
together in the dispersion direction to <1 µm accuracy to mitigate the modest undersampling of the
spectra as delivered to the instrument focal plane.
3.2.2. Basic Instrument Layout
The basic optical design of the spectrograph leverages the successful optical design of the multifiber optical SDSS spectrographs (Smee et al. 2013), but modified as needed for high spectral resolution at infrared
wavelengths. The basic optical layout of the APOGEE
instrument is illustrated in Figure 4, and was built in a
highly modularized fashion with distinct subcomponents:
Cryostat: Past the gang connector the long fibers route
to the spectrograph and enter the cryostat via vacuum
feed-throughs at the cryostat vacuum wall (keeping the
fibers intact avoids the introduction of another optical junction and thus minimizes throughput losses and
focal ratio degradation). The cryostat is a speciallydesigned, stainless steel, liquid nitrogen-cooled vessel
built by PulseRay Machining & Design (Beaver Dams,
NY). Together, the 1.4 × 2.3 × 1.3 m cryostat, optical bench and instrument subcomponents weigh approximately 1.8 metric tons (2 U.S. tons). The entire cryostat
sits on four pneumatic isolation stands to minimize vibration. Within the steel container, an aluminum cold
radiation shield surrounds the entire cold volume; this
entire shield is surrounded by blankets consisting of 10
Majewski et al.
layers of double-sided aluminized mylar interspersed with
layers of tulle.
Optical Bench: The spectrograph optics are mounted
to an optical bench that is a single, 3-inch thick cold plate
with extensive underside lightweighting and suspended
from the vacuum shell. A 97 liter LN2 tank is suspended
from the bottom of the cold plate. In the vicinity of the
camera the cold plate maintains a temperature of about
78K, and the entire cryostat experiences no more than a
35W heat load on the cold volume and has a 5.5 day hold
time. An LN2 liquid level sensor monitors the fill level,
but, in any case, an automatic filling system tops off the
dewar every morning after observing is over. Although
overall the bench-mounted, fiber-fed spectrograph confers a distinct advantage in maintaining a vibration-free,
temperature-stable system with a constant gravity vector that ultimately makes the APOGEE instrument deliver spectra that are extremely stable and repeatable,
the small nightly variation in LN2 liquid level was later
found to induce slight variations in mechanical flexure on
the cold plate that can be observed as small, ∼ 0.1 pixel
shifts in the spatial position (and even smaller, ∼ 0.01
pixel shifts in the spectral position) of the spectra on the
detector over the course of the night; fortunately these
slowly varying changes can be mapped and accounted for
by regularly observed flatfield exposures.
Pseudoslit and Collimator: The final 2 m of the longlength fiber link trains are contained within the cold
volume and terminate on a curved pseudo-slit. Fiberto-fiber spacing at the pseudo-slit is physically 350 µm
between centers to yield 6.6 pixel spacings between spectra on the detectors. An “uncorrected Schmidt” camera,
used in reverse, collimates the light of each of the fibers.
Thus, in keeping with the Schmidt design, the fiber tips
are carefully positioned to lie on, or close to, a curved
surface with radius of curvature approximately 1/2 that
of the collimator and to emit light in a direction orthogonal to that surface, so that they axially point back as
close as possible to the center of curvature of the pseudo
slit. Moreover, the pseudo-slit and spherical collimator
mirror have a common center of curvature near the system pupil, which is at the approximate position of the
spectrograph grating. In addition, the fiber ends are also
aligned on a curved lateral surface to ensure that at the
detector the fiber ensemble gives straight slit images; this
lateral curve enables each fiber to deliver the same rest
wavelength range on the detectors. As the only active
means to effect small changes in instrument focus, the
collimator is controlled by 3-axis (tip-tilt-piston) movement. This capability is also useful for implementing
dithering in the spatial dimension, an operational mode
that is periodically activated for the creation of spatiallysmoothed instrument flatfields. The resulting optical design is on-axis so that the pseudo-slit is an obscuration
in the collimated beam.
Cold Shutter and Flat Field Illumination: A “swinging
gate” cold shutter with a light trap (not shown in Fig.
4) covers the pseudo slit to prevent excessive light leaking into the cold volume when the spectrograph is not
taking observations. This minimizes the accumulation of
unwanted charge that could contribute to detector “persistence” (see §3.4). This mechanism also contains a set
of infrared light-emitting diodes that can provide a diffuse illumination onto the detectors useful for creating
15
flatfield exposures.
Fig. 4.— Layout of the APOGEE spectrograph optical bench
within the cryostat. The fiber train coming from the telescope
enters the cryostat on the left.
Fold Mirrors: Two fold mirrors are used for efficient
packaging of the optical train within the cryostat. The
second mirror flat is also a dichroic, which passes light
longward of the APOGEE spectral range into a trap behind the mirror; this assists in the mitigation of stray,
thermal light (see below).
VPH Grating: The disperser is a three-segment mosaic VPH grating, the first ever fabricated by KOSI. Because the required grating size exceeds that which can be
recorded in a single VPH exposure, the APOGEE VPH
is made by recording, in close temporal succession, three
adjacent segments in gelatin on a common fused-silica
substrate. While prototype mosaic VPH’s have been
fabricated in the past, none have been deployed in an astronomical instrument, cryogenically cooled or not. The
VPH has 1009.3 grooves/mm and operates in first order with a 54◦ angle of incidence. The grating is found
to deliver peak efficiency of 90% near the center of the
APOGEE spectral range, and 40% at the edges.
Camera: The wavelength-dispersed beams are focused
by a six-element refractive camera designed and fabricated by NEOS. The APOGEE camera is very large for
an astronomical spectrograph: the largest element is 394
mm in diameter and the smallest element is 237 mm in
diameter. Given such large camera elements, the variety of lens materials that can be considered is severely
limited by economic and fabrication limitations. Fortunately the narrow wavelength range of APOGEE means
that only two materials — monocrystalline silicon (for
four of the lenses) and fused silica (for the other two) —
are necessary, and those two materials are also, coincidentally, very robust to thermal shock. Overall the finished opto-mechanical camera assembly alone weighs 250
lbs. When combined with (minimal) absorption through
the fused silica and the performance of the anti-reflection
coatings, the throughput for all six lenses is 93% across
the wavelength coverage.
Detector Array: The spectra are recorded on three
Teledyne Imaging Sensors H2RG, 2.5 µm cut-off, 2048 ×
2048 pixel detector arrays. These are mounted in a de-
16
APOGEE
tector mosaic opto-mechanical package similar to that
used for the NIRCam instrument for the JWST, with
the arrays tilted to approximate the field curvature of
the optical system within a tolerance of 15 µm through
precise shimming of piston, tip and tilt. These detectors
are operated in sample-up-the-ramp mode, with readouts every 10.7 seconds; thus, “images” of the spectra
are in datacubes for each of the three arrays. As mentioned above, the arrays lie on a movable stage which is
used for “dithering” translation of the entire assembly in
the dispersion direction; in practice, images are taken in
pairs with half-pixel shifts, which, in the data processing,
can be used to recover the full sampling of the spectral
line-spread-profile (§6.3).
Baffling and Other Stray Light Mitigation: Mitigation
of stray light is an important consideration for achieving the required S/N because the APOGEE wavelength
range is small compared to the wavelength sensitivity
range of the detectors; of particular concern is thermal
light, because of the use of 2.5 µm cut off arrays. Zero’th
order light transmitted through the grating is intercepted
with a blackened panel behind the VPH. Of more concern are first order wavelengths outside of the nominal
APOGEE wavelength range. Light shortward of 1.0 µm
is absorbed by the four silicon elements in the camera.
Thermal light is mitigated in several ways: (1) The fibers
are cooled over 2 m of travel within the cryostat before
reaching the pseudo-slit. (2) The “long-pass” dichroic on
the front face of the second fold mirror and a broadband
anti-reflection coating on the backside creates a lightdump that intercepts some 95% of the residual thermal
light (λ > 2 µm) before if gets to the grating. (3) The
silicon lenses in the camera have antireflection coatings
that, together, permit transmission of only 9% (3%) or
the thermal light to the detectors at 2.3 µm (2.5 µm).
Calibration Box: Unlike the optical SDSS spectrographs, which take wavelength calibration images by illuminating the closed telescope covering petals, APOGEE
employs a separate, off-telescope calibration module that
enables access to calibration lamps at any time. When
not attached to a bright time plugplate cartridge, the
APOGEE gang connector can be connected to separate fiber runs that terminate at an integrating sphere
that can illuminate the fibers with nominal f /5 light
(to mimic the telescope) with either a ThArNe hollowcathode lamp, a UNe hollow-cathode lamp, or a tungsten
halogen light source. During commissioning and testing,
the sphere also at times was illuminated with a precisioncontrolled blackbody source. Two possible fiber links to
the integrating sphere are available: (a) a “DensePak”
bundle with a full set of 300 fibers, or (b) a “SparsePak”
bundle that sends light to every sixth fiber in the spectrograph focal plane. The latter is particularly useful for
evaluating the wings of the point spread function and the
effects of scattered light.
Instrument Control: At the observer level, operation of the APOGEE instrument takes place through
scripted sequences in the Sloan Telescope User Interface
(STUI; §5.1). For manipulation of the spectrograph detectors, the STUI interfaces with a Digital Signal Processor (DSP) based controller that provides both clocking
and bias/power supply voltages to the three arrays. All
three share a common clock and most of their bias lines,
with just a few power supply voltages unique to the indi-
vidual arrays. This ensures common timing for the three
arrays as they are read out. The read out scheme utilizes
“sampling up the ramp” (SUTR), where the arrays are
clocked and read out continuously and non-destructively
with a period of 10.6 seconds. The data are formatted
as a single output image containing the data for all three
arrays, and including the three H2RG reference outputs.
Because the array clocking is DSP based, the interval
between reads is very stable, which allows for accurate
curve of growth analysis of the developing signal in each
pixel (§6.1). This is facilitated by the rearrangement of
the flat, three-array data frames into time series data
cubes for each array during post processing of the data
for each observation.
3.3. Instrument Development and Operations Timeline
A white paper describing the potential of high throughput, multifiber, near-IR spectroscopy on the SDSS 2.5-m
telescope was presented to the Astrophysical Research
Corporation (ARC) Futures Committee by (Skrutskie &
Wilson 2015) in August 2005. The APOGEE project,
refining the concept to a focus on high resolution spectroscopy of Milky Way stars, was proposed as an SDSSIII10 project in August 2006 and was officially approved
by the ARC Board as one of the four SDSS-III projects
in November 2006. The APOGEE instrument Conceptual Design Review (CoDR) was held in April 2008, with
the goal of having the spectrograph collecting data on
the Sloan Telescope by 2011Q2. The instrument Preliminary Design Review (PDR) took place in May 2009,
with approval to start fabrication given at a Critical Design Review (CDR) held in August 2009. Despite the
technical challenges enumerated in §3.2.1, the primary
APOGEE hardware construction phase spanned only 16
months from CDR to obtaining spectra of bright stars
in February 2011.11 The instrument was delivered to
APO in April 2011 and on-site first light occurred on
the evening following deployment of the fiber train, on
6 May 2011 — consistent with the original instrument
development schedule.
Testing/commissioning observations of the instrument
commenced immediately. It was soon realized that while
instrument performance met, or exceeded, the original
requirements, it was also suffering from significant astigmatism that made it impossible to achieve simultaneous
focus in the spatial and spectral directions. In addition, the placement of the arrays, particularly the array recording the reddest wavelengths, was non-optimal.
While the source of the astigmatism has yet to be identified confidently, it was possible to mitigate its effect
by introducing a slight cylindrical bend on the first fold
mirror using a specially made fixture that induces a calculated axial force along the center of the mirror backside. On the other hand, the realignment of the focal
plane arrays, which required shipping the entire detector
10 At the time, the SDSS-III project was called the “After SloanII” project, but, for clarity, we use “SDSS-III” throughout this
paper.
11 This starlight was delivered to the APOGEE instrument while
still in the UVa instrument lab by way of a 10-inch Newtonian reflector with the diagonal flat replaced by a “hot mirror” dichroic
that directed optical light to the nominal Newtonian port for eyepiece acquisition and guiding, but passed the H-band light to a
sparsely packed grid of fibers linked to the APOGEE instrument.
Majewski et al.
17
assembly package to the University of Arizona, was not
effected until the APO “summer shutdown” in July 2011.
Thus, from May-July observations with the APOGEE
instrument were taken without parfocal arrays, and this
resulted in data being taken with a reduced resolution of
R ∼ 15, 000 across the “red” detector array. The observations collected during this phase of operations are commonly referred to as “pre-shutdown” or “commissioning” data; although these data are being released publicly, application of APOGEE data reduction and analysis pipelines to those data does not lead to data products
within the science quality requirements, and any results
from them are not of survey quality. Moreover, all but
a few plugplates observed with this instrument configuration were eventually repeated (see Fig. 10). Nevertheless, the data are of some interest, for example, in
providing additional epochs for the study of time series
phenomena.
Official APOGEE survey data collection commenced
after summer shutdown, August 2011, with all three detectors in focus. The instrument parameters given in
Table 2 pertain to this configuration of the APOGEE
spectrograph, which was maintained throughout the remainder of SDSS-III operations (which concluded July
2014). Throughout this period, APOGEE observations
were smoothly carried out by the SDSS observers with
minimal daily oversight by the APOGEE team and no
loss of time due to instrument problems.
3.4. Basic Instrument Performance and Properties
The overall instrument performance is obtained from
a variety of test data taken on-site. Table 2 summarizes
the instrument characteristics. Much greater detail on
the instrument performance can be found in Wilson et al.
(2015), Nidever et al. (2015) and Holtzman et al. (2015).
Wavelength Ranges: While the APOGEE spectrograph was designed to meet technical specifications that
included the specific wavelength limits set by the 15160Å
potassium line and the 16720-16770Å aluminum lines
(§2.3), the spectral range recorded by the detectors extends almost to 1.7 µm (Fig. 5), although these “extra”
wavelengths were not designed to meet the resolution,
throughput and other technical specifications and did
not drive design considerations. Of course, because of
the physical limitations of butting detectors together, the
spectral coverage is interrupted by inter-chip gaps. The
exact wavelength region falling onto the array ensemble
can be controlled by micro-positioning of the dithering
stage, but the default position of the instrument delivers
the wavelength regions on each chip as shown in Figure
5 and given in Table 2.
PSF, LSF and Resolution: Image quality can be
judged from the delivered line-spread function (LSF) and
point-spread function (PSF) across the arrays. The PSF
has a FWHM of typically 2.16, 2.14, and 2.24 pixels at
the fiducial centers (1.54, 1.61 and 1.66 µm)12 of each of
the three array wavelength spans (Fig. 5). The wings
of the fiber PSFs reach far enough from the peak that
there is a small amount of overlap between the PSFs of
adjacent fibers on the detector focal plane. When the
12 For the “red” array, the fiducial wavelength lies at the midpoint of the blue edge of that array and the 16770Å red limit of
the technical specification (Fig. 5).
Fig. 5.— Schematic figure showing the arrangement of fibers and
wavelengths across the three APOGEE detectors. The wavelengths
indicate the edges of the arrays as well as fiducial wavelength positions (indicated by grey dots) corresponding to the “mid-chip”
properties given in Table 2. The location of the Littrow ghost
(curved line) and the super-persistent region (grey area) are also
indicated. The dashed line at 1.68 µm shows the red limit of the
wavelength coverage for which the technical performance of the
instrument was specified by the science requirements, but the instrument performance is still good redward of this.
magnitude difference between stars on adjacent fibers is
large, contamination of the spectrum of the fainter one
by the wings of the brighter spectrum can become important. The amount of contamination varies across the
three arrays, but analysis of commissioning data showed
that between ∼0.1 and 0.2% of the total power of the PSF
is located within 3 pixels of the central pixel of the adjacent PSF. It is for this reason that the fiber management
scheme described in §3.1 was implemented. The LSF also
varies across the arrays, both as a function of wavelength
and fiber, as discussed by Nidever et al. (2015, see their
Figures 14-16). In particular, it is slightly undersampled
in the blue part of the spectrum, which is what motivated
our use of the detector array dithering mechanism during
APOGEE
5
Spectral Type Sequence
HD 200927
O
HD 112152
B
HD 23732
A
HD 282967
F
KIC 10748390
G
TYC 3899−14−1
K
CSS2 1
M
Normalized flux + offset
4
3
Spectral Type
2
1
15200
15400
15600
15800
16000
16200
Wavelength [Å]
16400
16600
16800
Fig. 6.— An image showing continuum normalized APOGEE
spectra as a function of stellar spectral type. The earlier spectral
types are representative of those seen among the telluric standards,
whereas the later types are typical of those seen among the main
APOGEE survey.
6
Temperature Sequence
5
4
[Fe/H]=−0.25
log(g)=2.26
Teff=4203 K
[Fe/H]=−0.11
log(g)=2.13
Teff=4364 K
[Fe/H]=+0.03
log(g)=2.27
Teff=4489 K
[Fe/H]=−0.31
log(g)=2.37
Teff=4633 K
[Fe/H]=−0.01
log(g)=2.57
Teff=4777 K
[Fe/H]=−0.05
log(g)=2.65
Teff=4920 K
[Fe/H]=−0.15
log(g)=2.59
Teff=5061 K
[Fe/H]=−0.16
log(g)=2.41
Teff=5173 K
[Fe/H]=−0.05
log(g)=1.85
Teff=5349 K
Temperature
observations (§2.3 and §3.2.1). The resulting resolution
in the properly sampled spectra varies by ∼ 25%, peak
to peak, being higher at shorter wavelengths. Typical
values at 1.55, 1.61 and 1.66 µm are R = 23, 500, 23,400
and 22,600 (for details, see Nidever et al. 2015).
Instrument Throughput: Observations of stars with
well known 2MASS magnitudes make possible empirical estimates of the throughput of the APOGEE instrumental apparatus. The end-to-end (i.e., from primary
mirror to detector) measured throughput has a peak
of 20 ± 2% at λ ∼ 1.61 µm. This number is somewhat higher than expected from predictions based on the
product of the component-by-component (measured or
manufacturer-supplied) wavelength-dependent throughputs (see Table 2). These throughput measurements have
obvious implications for the S/N achieved under survey
conditions; those are discussed in §7.2.
Array Persistence: As with most Teledyne infrared detector arrays, those installed in the APOGEE instrument
have a small degree of image persistence, which results
in the carryover of latent charge from exposure to exposure. This typically does not affect most APOGEE
data. However, roughly one third (in the spatial direction; see Fig. 5) of the detector used for the bluest wavelengths is affected by excessive and long-lasting “superpersistence”, which appears to behave like normal persistence, but with significantly greater accumulated charge
and a very long time constant (see §5 of Nidever et al.
2015). Thus, intensely exposed pixels on one image can
yield inordinately “hot” pixels in subsequent exposures.
A small portion of the “green” array (a thin “frame”
around the edges) is also affected. The seriousness of
this phenomenon has had a strong influence on our observing procedures — e.g., the timing and strength of
calibration exposures and the imposition of a bright limit
to targeted sources — with the goal of limiting unnecessary overexposure whenever possible. This problem also
motivated the installation of the cold shutter (§3.2.2) to
prevent stray light to enter the instrument when not in
use. While there is hope that the effect of the superpersistence on the data may be correctable in software, it is
a complex hysteresis problem that we currently have not
fully resolved and no mitigation is currently implemented
up to, and including, Data Release 12.
Ghosts: Despite mitigation efforts, there remain two
in-band sources of stray light in the form of ghosts: (1) A
Littrow ghost of each fiber (created by light reflected off
the detector surface, recollimated by the camera, recombined by, and reflected from, the grating, and reimaged
by the camera onto the detector; Burgh et al. 2007) forms
on the detector at 0.4% the intensity of all of the recorded
spectral light in each fiber near the spectrograph Littrow
position at 1.604 µm. Because the pseudo-slit is actually
curved, the Littrow ghost centers this excess light at a
slightly different wavelength for each fiber, ranging from
1.6056-1.6067 µm (∼35 pixels; Fig. 4). This spectral
region was chosen through an optimization procedure
aimed at minimizing the impact of loss of absorption
lines due to ghost overlap on the quality and diversity
of the final APOGEE abundances. Optimal ghost positions were identified for which only very few interesting
lines are lost, and for which in all cases there are other
lines for the same element making up for the missing
ones. The final position, was selected so as to minimize
Normalized flux + offset
18
3
2
1
20−4 19−4 18−4
15200
17−4
15400
16−4
15−4
15600
14−4
15800
13−4
16000
16200
Wavelength [Å]
12−4
16400
11−4
16600
HI
16800
Fig. 7.— An image showing continuum normalized APOGEE
spectra as a function of stellar surface temperature for typical
APOGEE main survey RGB stars of near-solar abundance. At
the bottom, Bracket hydrogen lines are identified; these lines show
the clear trend of increasing strength for increasing temperature.
any additional grating tilts that could lead to a substantial change in spectral resolution. The resulting spectral
interval happens to coincide with the natural position of
the Littrow ghost for the nominal APOGEE grating with
no fringe tilt. The FHWM of the ghost in the wavelength
dimension is about 9 Angstroms (∼32 pixels). (2) Fiber
tip ghosts occur from light that reflects off the detector
face, transits through the entire instrument in reverse,
reflects off the fiber face (or v-groove block area adjacent
to the fiber) and returns through the instrument a third
time, back to the detector. While ghost intensity varies
with wavelength and fiber position, stray light analysis
of the optical design predicts the ghost images will have
spot size RMS radii approximately 1.5-4.5 times larger
and intensity < 1/1000 compared to the primary images
at the detector. Moreover, ghost images should arrive
within 1 pixel of the primary image positions.
3.5. Example Spectra
Majewski et al.
6
5
4
log(g)=2.39
[Fe/H]=+0.39
Teff=4636 K
log(g)=2.38
[Fe/H]=+0.19
Teff=4633 K
log(g)=2.41
[Fe/H]=+0.01
Teff=4664 K
log(g)=2.38
[Fe/H]=−0.20
Teff=4666 K
log(g)=2.42
[Fe/H]=−0.40
Teff=4576 K
log(g)=2.32
[Fe/H]=−0.60
Teff=4667 K
log(g)=2.43
[Fe/H]=−0.88
Teff=4664 K
log(g)=2.31
[Fe/H]=−1.20
Teff=4895 K
log(g)=2.23
[Fe/H]=−1.82
• A desire to sample, with minimal bias, all stellar
populations of the Galaxy, from the bulge, across
the disk, and into the halo.
Metallicity
Normalized flux + offset
The APOGEE field targeting strategy was designed
around several motivations and requirements:
Metallicity Sequence
Teff=4686 K
3
2
1
Fe I
15200
Fe I
Si I
15400
Si I Fe I
15600
Mg I
15800
Si I
Si I
Si I
Fe I
16000
16200
Wavelength [Å]
Si I + Fe I Fe I
16400
Si I Al I
16600
19
CI
16800
Fig. 8.— An image showing continuum normalized APOGEE
spectra as a function of metallicity for giant stars of similar temperature. Some of the strongest metal lines seen are identified at
the bottom of the figure.
• The need to probe fields to a variety of magnitude
limits to access stars over a wide range of distance
in all parts of the Galaxy.
• The ability to calibrate efficiently against stars with
well-established physical properties, such as the
chemical abundances and radial velocities that are
often well established for star cluster members, or
the masses and gravities that can be derived for
asteroseismology targets.
• The need to coordinate with the other SDSS-III
bright time program, MARVELS, which relied on
frequent visits to a relatively limited number of
fields.
In the end, changes in the latter two requirements as well
as the realities of the actual distribution of clear weather
and several other considerations led to the evolution of
the APOGEE target selection over the three year observing campaign.
4.1.2. Field Selection Evolution
Fig. 9.— Comparison of a section of the APOGEE spectra for
two stars of the same temperature (approximately 4060 K) with
about a 100× ratio in abundance of iron. The red spectrum is for a
star that has [Fe/H] = -1.8 and log g = 0.158. The black spectrum
is for a star that has [Fe/H] = 0.365 and log g = 1.5.
Examples of the appearance of stellar spectra as obtained by the APOGEE spectrograph are shown in Figures 6-9. Figure 6 shows stars ranging from spectral type
O to M; the primary APOGEE science targets are of
type G and K, whereas most of the early spectral types
were observed as telluric standards and some M types
are selected by the random sampling of the parent distribution (§4.2). Across the temperature range of the
primary survey target types (G-K stars), it is still possible to discern line strength variations, as shown in Figure
7. A primary driver of the APOGEE project is the exploration of chemical abundance variations among its late
type stellar sample; Figure 8 demonstrates the appearance of RGB stars of similar temperature but a 2.2 dex
metallicity spread. To show greater detail and a broader
array of chemical species, Figure 9 highlights the blue
array spectra for two giant stars separated by about 2.2
dex in [Fe/H].
4. SURVEY DESIGN
4.1. Field Selection
4.1.1. Field Selection Principles
Initial Survey Design: For its expansion into bright
time observing the SDSS-III collaboration planned to
capitalize on the existence of two new fiber-fed instruments that could operate simultaneously from shared
plugplates, thereby doubling the effectiveness of the
Sloan Telescope. Because the MARVELS project required many visits to each of its target fields, whereas
APOGEE had always planned at least some deep field
probes, the original SDSS-III plan was for 75% of the
bright time to be in co-observing mode, whereas the remaining 25% of bright time would be given to APOGEE
to observe fields of no interest to MARVELS and to fill
out its sky coverage. Moreover, because MARVELS targets were relatively bright, relatively nearby stars, both
surveys could make good use of many visits to fields at
high latitude (in APOGEE’s case, for accumulating signal on faint, distant halo stars) as well as in the disk
(where APOGEE could both accumulate flux on highly
dust-extinguished stars across the disk as well as cycle
through large numbers of brighter stars).
The baseline for co-observed fields was to accumulate
a total of 24, approximately one hour visits. Under these
overriding restrictions, the initial APOGEE field selection plan focused on fulfilling the other principles described in §4.1.1. The 75% shared survey time was distributed in a series of 24- and 12-visit fields across the
disk and halo (the latter used for fields that MARVELS
began observing before APOGEE came on line). The
disk plan included, a regular “picket fence” Galactic longitude distribution of these deep fields, and with multiple
visits at each picket broken up into a series of plate designs that enable stars of different magnitudes (i.e., mean
distances) to be cycled through for different numbers of
total visits. The adopted distributions of Galactic latitude and cycling of stars were based on modeling stellar
20
APOGEE
TABLE 2
Summary of APOGEE Instrument Characteristics
Property
On-sky field of view (typical declinations)
On-sky field of view (high airmass)
Total number of spectrograph fibers
Fiber center-to-center collision limit on plugplate
Fiber scale on sky (diameter)
Detectors
Detector pixel size
Detector wavelength regions
Littrow ghost position
Littrow ghost intensity
Dispersion (at 1.54, 1.61, 1.66 µm)
Point Spread Function (spatial FWHM) (at 1.54, 1.61, 1.66 µm)
Line Spread Function (resolution element) FWHM (1.54, 1.61 1.66 µm)
Median native (λ/FWHM) resolution (at 1.54, 1.61, 1.66 µm)
Predicteda throughput (1.54, 1.61 1.66 µm)
Measuredb throughput (1.61µm)
S/N for H=12.2 K0III star in an 8×500 sec visit (1.61 µm)
Specific fiber numbers most affected by excessive persistence
Performance
3.0 deg diameter circle
1.5 deg diameter circle
300
70 arcseconds
2.0 arc seconds
2.5 µm cut-off, 20482 pixel, Teledyne H2RG Imaging Sensors
18 µm
1.514-1.581, 1.585-1.644, 1.647-1.696 µm
1.6056-1.6067 µm
0.150% of full fiber intensity
0.326, 0.282, 0.235 Å/pixel
2.16, 2.14, 2.24 pixels
2.01 , 2.44 , 3.14 pixels
23,500, 23,400, 22,600
14, 15, 10%
20 ± 2%
105
1-100
a Calculated as the product of the wavelength-dependent transmittance or reflectivity for all components of the as-built telescope+instrument design.
b Based on measured flux for stars of known H magnitude. Error bars reflect uncertainties regarding extinction by Earth’s atmosphere
and (seeing-induced) fiber losses.
population distributions using both the Trilegal (Girardi
et al. 2005) and Besançon (Robin et al. 2003) Galaxy
models. A major concern addressed by this modeling
and that drove the specific latitude distributions chosen, was ensuring ample representation of stars from the
Intermediate Population II, “thick disk”. Further information about this modeling is given in Appendix D; an
example of the results are given in Figure 11.
For the shared halo pointings, the APOGEE team focused on fields containing globular clusters, which serve
as both science and calibration targets. A number
of globular cluster stars having high resolution spectroscopy in the literature are faint enough to require deep
APOGEE observations. Moreover, the multiple globular
cluster visits make it possible to increase the number
of globular cluster stars to be sampled, given the limitations posed by fiber collisions (Table 2). Additional
high latitude long fields were placed in fields known to
be traversed by halo substructures, such as the Sagittarius stream (with field placement guided, e.g., by the
results of Majewski et al. 2003) or the Virgo Overdensity
(e.g., Vivas et al. 2001; Newberg et al. 2007; Jurić et al.
2008).
With this basic structure in place for 75% of the
planned observing time, the remaining bright time time
was distributed to various classes of “APOGEE-only”
fields: (1) fields across the bulge, a primary region of the
Galaxy sought for our primary science goals (§1.3); (2)
fields at low declinations that are not viable for MARVELS work; (3) a number of fields across the disk, filling in the relatively large gaps between the long field
“pickets”; and (4) additional disk fields that include open
clusters useful for further calibration of APOGEE spectroscopy. There are two important considerations relevant to fields of class (1) and (2): First, the limited accessibility for these fields resulted in them typically being
reduced to having single 1-hour visits which, at constant
S/N , mandated a brighter magnitude limit (H = 11.1,
see §4.2.3) there. This was needed to ensure that statistically significant samples would be obtained at the end
of the survey, particularly in high value regions such as
the Galactic bulge, where good spatial coverage was also
desired. Second, the sizes for these fields had to be reduced to only a 1-2◦ diameter field because of the severe
differential refraction experienced over the course of a 1
hour visit at high air masses. Fortunately these reduced
field-of-view fields occur in high stellar density environments, so that there is no shortage of targets from which
to choose.
This overall plan was in place by early 2011 — as
needed to begin drilling plugplates in preparation for
2011Q2 instrument commissioning and 2011Q3 survey
operations — and thus dictated the early survey observing plan.
Reconfiguration at MARVELS Descope: During the
2011 summer shutdown, and just prior to the commencement of the formal APOGEE survey operations, a decision was made to gradually curtail the MARVELS program over the course of the following year. A select number of MARVELS fields that had already obtained at
least 12 pre-APOGEE epochs of MARVELS observation
were chosen for completion, but reduced to either 6 or 12
hour APOGEE fields. Thus, in the end, only a handful
of the original 24-hour fields were preserved (primarily
the “deep disk mid-plane spokes” at l = 30, 60 and 90◦
and a few globular cluster fields: see Figs. 10 and 11).
The sudden, substantial increase in the share of
“APOGEE-only” bright time observing allowed a number of new pointings to be added to the baseline
APOGEE field placement plan:
• more bulge pointings, including fields useful for
cross-calibration to the BRAVA (Rich et al. 2007)
and ARGOS (Freeman et al. 2013) surveys;
Majewski et al.
• additional calibration open and globular clusters;
• numerous 3 hour fields to give a finer angular sampling at latitudes of b = 0, ±4, ±8 and ±12◦ ) between the preserved 12/24-hour pickets at l =
30, 60, 90, 120, 150, 180 and 210◦ ;
• rings of high latitude fields at b = +30, ±45, +60
and +75◦ .
The greater flexibility afforded by the increased control
over field placement also aided in the implementation of
the initial set of Ancillary Science programs.
Incorporation of the Kepler Field: The success of
ESA’s CoRoT mission (Auvergne et al. 2009) and
NASA’s Kepler mission (Borucki et al. 2010), and, in particular, the asteroseismology programs for each (Michel
et al. 2008; Chaplin et al. 2010; Gilliland et al. 2010)
— through which non-radial oscillations were detected
and characterized for a substantial sample of RGB stars
and subgiants (Mosser et al. 2010; Hekker et al. 2011) —
presented a special opportunity for the APOGEE program. The asteroseismic frequencies are sensitive probes
of stellar masses and radii (Chaplin & Miglio 2013).
Apart from providing invaluable independent measurement of stellar gravities for testing and calibrating the
APOGEE stellar parameters pipeline (§6.5), when combined with precision abundance measurements of the
quality that APOGEE could provide, asteroseismically
measured stellar masses can provide reliable age estimates, at the level of 15% (Gai et al. 2011). The opportunity to obtain such reliable age data for a large number
of field stars is unprecedented, and provides pivotal temporal benchmarks for a survey of Galactic chemical evolution, the primary mission of APOGEE. Moreover, the
APOGEE instrument presents the only practical means
to obtain high resolution spectroscopic assays for a large
fraction of this Kepler sample, which is distributed over
a relatively large area of sky; serendipitously, the FOV
of the SDSS plugplates is nicely matched to the size of a
Kepler tile.
With formally established collaborations — the
APOGEE-Kepler Asteroseismic Science Collaboration
(APOKASC) and the CoRoT-APOGEE Collaboration
(COROGEE) — plans were established to target a
large fraction of the KASC giant/subgiant sample as
well as CoRoT giants in the direction of the Galactic
center and anticenter; however, practically, this meant
non-negligible reorganization of the APOGEE targeting
scheme. The two Kepler tiles containing the star clusters NGC 6791 and NGC 6819 already were planned to
have long pointings, but the remaining 19 Kepler tiles
were now included with two 1-hour visits each, and with
each visit focusing on a unique set of targets.13 This
resulted in observations of (a) some 8,000 APOKASC
giant stars, along with (b) about 600 subgiant and dwarf
stars, whose ages could be determined using gyrochronology (e.g., van Saders & Pinsonneault 2013), as well as (c)
targets for other ancillary science programs (e.g., eclipsing binaries). Further information on the Kepler field
13 Because chemistry was a primary goal of the APOGEE visits,
and the amount of available observing time was greatly limited,
the normal three-visit cadence for binary detection was not implemented for the APOKASC program.
21
targeting can be found in Zasowski et al. (2013) and Pinsonneault et al. (2014). Meanwhile, in the CoRoT fields
APOGEE targeted 121 giant star candidates in one plate
designed for the CoRoT LRa01 (“anticenter”) field and
363 giant candidates on 3 plates designed for the LRc01
(“center”) field. Unfortunately, incorporation of the Kepler field observing required thinning out the APOGEE
survey of the Galactic plane at similar longitudes (though
see below).
Survey Year Three Modifications: Several circumstances led to further modifications in the third year of
APOGEE observations, fortunately in the sense of allowing expansion of the APOGEE footprint. First, overall
clearer than average winters put the surveying of the anticenter disk ahead of schedule. This enabled an expansion of the Galactic anticenter grid with broader latitude
coverage and more finely sampled longitude coverage at
all latitudes, both an advantage for exploring the properties of the disk warp, disk flare and the presence of low
latitude substructure in the outer Galaxy, such as the
Monoceros and TriAnd structures. In addition, several
Ancillary Science programs that could take advantage of
the relevant LSTs were slightly expanded.
Meanwhile, our somewhat lagging spring and summer
field schedule was greatly aided in the final year by both
the twilight and dark time observing campaigns (§5.3).
With this extra telescope time the APOGEE program
not only was able to catch up on observations of spring
and summer fields (including Kepler field pointings), but
to restore previously removed disk grid pointings near the
Kepler field.
4.1.3. Final Field Plan
The final APOGEE targeting footprint is thus the
product of the evolving plan described in §4.1.2. Figure 10, which supersedes the previously published
APOGEE field targeting plan in Zasowski et al. (2013),
shows the final implemented survey plan (§4.1.2) with the
targeted fields color-coded according to different criteria.
In the top panel, the fields are color-coded according to
the intended primary purpose. The middle panel shows
the same fields color-coded by number of visits. Finally,
in the bottom panel the fields are broken up by formal
survey versus commissioning fields and, for the former,
by completion status. Few “commissioning-only” fields
remain because most commissioning observations were
repeated during the main survey with the spectrograph
in its proper survey configuration (§3.3).
4.2. Target Selection
APOGEE targeting consists of (1) the “main sample”
or “normal science targets”, (2) “special targets”, which
include (among others) calibration stars with measured
stellar parameters and abundances from other spectroscopic studies, star cluster members, and targets submitted by one of APOGEE’s Ancillary Science programs,
and (3) a sample of early-type stars observed as telluric
absorption monitors for each exposure. A complete and
detailed discussion on how each of these targets is selected, and how they are identified within the publicly
released databases, is given by Zasowski et al. (2013).
We only give a broad overview here, with an emphasis
on motivations for the overall procedures followed.
22
APOGEE
Fig. 11.— The expected Galactic distribution of APOGEE targets as projected on the Galactic plane, as predicted by the Trilegal
model for the field plan prior to the final, survey year three modifications. Stars are color-coded by expected stellar population: blue
= bulge, green=halo, red=thick disk, black=thin disk.
Fig. 12.— Same as above Fig. 11 for the expected Galactic
azimuthally-averaged RGC −ZGC distribution of APOGEE targets
as predicted by the Trilegal model.
Fig. 10.— (Top) The final APOGEE field targeting plan, the
product of the evolving strategy described in §4.1.2. Fields are
color-coded by their primary purpose or sought-after target class.
The grey fields include both Kepler and CoRoT asteroseismology
targets in the Kepler and CoRoT databases, as well as MARVELS
Calibration fields. (Middle) Distribution of observed APOGEE
fields, color-coded by the number of approximately 1-hour visits. (Bottom) Distribution of APOGEE survey and commissioning
fields, and, for the former, whether the survey observations were
completed. Most commissioning observations were repeated during
the main survey with the spectrograph in its survey configuration.
4.2.1. “Minimum Criteria” Philosophy
From the start of APOGEE survey planning there was
a strong desire to maintain the utmost simplicity in the
rules for target selection for the main sample of normal
science targets. As the first large spectroscopic project
to truly survey all major components of the Milky Way,
questions related to interface and overlap of these compo-
nents are central to the APOGEE mission. To see these
signatures with clarity a homogeneous sample and a well
understood selection function are both critical. Moreover, a first exploration of uncharted territory mandates
a prudent attitude, curbing a natural temptation towards
forcing overrepresentation of certain populations in any
given position in the sky. As a consequence, however,
the resulting sample strongly favors the most common
stellar types (e.g., metal-rich disk stars), with rare populations (e.g., metal-poor stars) constituting a small—
even negligible—fraction of the whole. To some extent,
this situation is mitigated by the field distribution, which
naturally leads to variable relative sampling of the bulge,
thin disk, thick disk, and halo by Galactic line-of-sight
(§4.1, Fig. 10). In addition, the emphasis of APOGEE’s
targeting on a stellar color range dominated by RGB star
candidates (§1.2) enhances the representation of more
distant populations, despite the relatively bright magnitude limits of the survey.
Majewski et al.
Nevertheless, nearly every APOGEE field has many
more objects in it than APOGEE can reasonably observe, and the strategy for selecting targets from the
available parent population inherently imposes additional biases in the selection function. In particular, the
adopted schemes for selecting stars across the magnitude
distribution (see §4.2.3) have been designed to achieve
large spreads in distance representation along each line
of sight. Moreover, additional photometric criteria were
adopted in the halo fields to favor the targeting of halo giants and minimize foreground dwarf star contamination
(§4.2.3). Despite these concessions, which were meant
solely to improve the spatial sampling of the Galaxy, a
goal of maintaining the simplest and most consistent selection function at each position was central to the survey
design.
4.2.2. Source Catalogs and Supplemental Data Used
Target selection for APOGEE was made primarily using the Point Source Catalog (PSC) of the Two Micron
All-Sky Survey (2MASS; Skrutskie et al. 2006), which is
complete to H < 15.1, and therefore more than sufficient
for our primary selection of targets with H < 12.2. In effect, APOGEE represents the first comprehensive stellar
spectroscopic follow-up survey of 2MASS.
These data were supplemented, where available, with
Spitzer IRAC data taken from the GLIMPSE I, II, and 3D surveys (e.g., Churchwell et al. 2009). The addition of
IRAC data in the Galactic mid-plane, where extinction
is greatest, allows us to take advantage of star-by-star
dereddening techniques exploiting JHKs [3.6][4.5] data
(Majewski et al. 2011). In the vast majority of fields
falling outside of the Spitzer footprint, we made use of
the mid-IR data from NASA’s WISE mission (Wright et
al. 2010). Finally, to enhance our efficiency in identifying
stars from the distant halo, we also made use of an ad hoc
Washington M, T2 ,DDO51 filter observing campaign in
high latitude fields using the Array Camera on the U.S.
Naval Observatory 1.3-m reflector; this filter system has
been shown to be effective in photometrically distinguishing dwarf from giant stars (Geisler 1984; Majewski et al.
2000; Morrison et al. 2000).
4.2.3. Main Survey Targets
Color Selection Criterion: A primary driver of the
APOGEE survey was the desire to exploit luminous,
evolved (RGB, RSG, AGB, RC) stars as our primary
tracer of the Galaxy because they allow access to large
distances, even in regions of high extinction, at magnitudes reachable with the Sloan Telescope. Moreover,
these post-main-sequence stars are found in stellar populations of almost all ages and metallicities and so do not
impose a strong bias in this regard. Finally, it is possible to generate relatively pure samples of these stars with
simple color criteria applied to the 2MASS PSC. It is well
known that the red side of the typical (J −Ks , H)0 colormagnitude diagram (CMD) produced from the 2MASS
PSC is dominated by red giant and red clump stars, so
that a simple red color selection suffices to generate a
target catalog dominated by such evolved stars.
Choice of an optimal blue (J −Ks )0 limit entails a trade
between (1) increasing dwarf star contamination towards
the blue, (2) increased fractional representation of fainter
(and therefore typically closer) red clump versus RGB
23
stars towards the blue, and (3) increasing bias against
metal-poor giant and red clump stars towards the red.
Comparison to stellar atmospheric models, Galactic stellar population models and theoretical isochrones indicate
that within APOGEE’s typical magnitude range, a color
limit of (J − Ks )0 ≥ 0.5 produces a sample that substantially reduces the dwarf star contamination in the final
sample while imposing a minimal bias against metal-poor
giants (see §4.3 of Zasowski et al. 2013), and this limit
was adopted for the main APOGEE survey.
Correction for Extinction: To obtain the extinctioncorrected CMDs we applied a correction to each potential
target based on its E(H − 4.5µm) color excess according
to the Rayleigh-Jeans Color Excess Method (“RJCE”;
Majewski et al. 2011), if 4.5 µm photometry is available from Spitzer or WISE, with the former preferred
because of its better resolution. Unfortunately, most of
the Spitzer data derive from the GLIMPSE or other programs that are tightly confined (generally to within 1
deg, and at most 4 deg) to the Galactic mid-plane. Fortunately, these are the latitudes where image crowding is
worst and the need for Spitzer’s better spatial resolution
is greatest. For halo fields, it was found that a slightly
more sophisticated, “hybrid” dereddening method, invoking limits from the Schlegel et al. (1998) maps, proved
more effective (see §4.3.1 of Zasowski et al. 2013).
Magnitude Ranges: Given the requirement of S/N =
100/pixel for the faintest targets in any field, the magnitude limits are set by the number of visits (thus integration time) to each field. Thus, because of the variable
numbers of visits across the survey (§4.1.3, Figure 10),
different lines of sight probe to different magnitude limits, and, consequently, distances. The nominal 3-visit
survey field is limited to H ≤ 12.2, but across the survey magnitude limits range from H ≤ 11.0 to H ≤ 13.8
for fields ranging from 1 to 24 visits (see Table 4 of Zasowski et al. 2013). A universal bright magnitude limit
of H = 7.0 prevents saturation of the detectors and minimizes scattered light contamination of adjacent spectra.
However, only a fraction of the stars in a particular
FOV require the full integration delivered by all visits to
that field. Moreover, as described in §4.1.2, numerous
visits to the same field afford the opportunity to sample
discrete groups of stars and accumulate a much larger
stellar sample. Therefore, a “cohort” scheme was developed to divide the parent target sample into groups of
stars that could be successfully observed in only a fraction of the visits and then rotated out and replaced with
new targets. The details of the breakdown on the number of fibers per plate design delegated to each cohort
and the magnitude ranges assigned to each cohort are
detailed in §4.4 of Zasowski et al. (2013).
Magnitude Distribution Function: With magnitude
limits established for each cohort in a field, stars within
the relevant color and magnitude limits are then sampled randomly within each cohort. Consequently, the
final magnitude distribution of spectroscopic targets in
a field may differ significantly from the distribution of
candidates, because the former also depends on (a) the
number of each type of cohort in the field, (b) the fraction
of APOGEE’s science fibers allocated to each cohort, and
(c) the vagaries of which targets may be rejected during
the actual plate design phase due to fiber collisions (see
§4.5 of Zasowski et al. 2013, for details).
24
APOGEE
Halo Field Considerations: A larger fraction of available stars can be targeted in the halo fields than at lower
latitudes, because of the lower target density. However, because of the steep density fall-off, the nominal
dwarf:giant ratio in the standard survey color and magnitude range is substantially higher in the halo. Therefore, to ensure access to the smaller fraction of giant stars
available per field, in many halo fields we used combined
Washington (M and T2 ) and DDO51 photometry to classify stars as likely dwarfs or giants prior to their selection
as spectroscopic targets (see Zasowski et al. 2013 for details). In some halo fields the number of targets brighter
than the nominal magnitude limit was too small to employ all APOGEE fibers. Unused fibers were assigned
to stars that either lacked DDO51 classification or were
classified as dwarfs, or on stars classified as giants, but
fainter than the magnitude limit, with the expectation of
getting at least some useful data from the resulting lower
S/N spectra (see §3.3 and 7.1 in Zasowski et al. 2013).
4.2.4. Calibration Fibers
Despite the great multiplexing advantage afforded by
a 300 fiber instrument, observing in the near-infrared
means that unfortunately a non-negligible fraction of
these fibers must be surrendered to real-time calibration.
APOGEE spectra are affected (see Figs. 2 and 34) by
both airglow (OH emission) and telluric absorption (by
CO2 , H2 O and CH4 ),14 and both phenomena vary on
short enough timescales that they must be monitored simultaneously with science observations. Moreover, these
atmospheric effects vary on angular scales comparable to
the APOGEE/SDSS FOV (see, e.g., Fig. 19 of Nidever
et al. 2015). Thus, large numbers of broadly distributed
calibration fibers are needed for the derivation of twodimensional airglow and telluric absorption corrections
across the same FOV as the science fibers. For airglow
correction, 35 APOGEE fibers are assigned (by the plate
design algorithm — see §4.4) to an evenly distributed selection of blank sky positions.
To monitor the telluric absorption it is most useful to
depend on the spectra of hot stars, which are characterized by very few and very broad atomic lines that can be
easily distinguished from telluric lines. Thirty-five of the
bluest and brightest stars evenly distributed across the
field are chosen for telluric absorption calibration (see §5
and Fig. 8 of Zasowski et al. 2013). Although not originally envisioned as part of the primary science focus of
APOGEE, the number of hot stars targeted and the ample spectral time series collected for many has turned out
to yield a number of interesting science results, particularly in the study of emission line (B[e]) stars and other,
non-emission stars with circumstellar disks (Chojnowski
et al. 2015, see §7.4.1 and Fig. 22), and including the
discovery of rare stellar types (Eikenberry et al. 2014).
4.3. Ancillary Science Program
Several motivations led to the inclusion of an ancillary science program in the APOGEE survey plan: (1)
The APOGEE spectrograph, its mating to the very large
FOV Sloan 2.5-m Telescope, and the extremely effective multifiber optical interface between the two repre14
A detailed breakdown of this telluric absorption by molecular
species is shown in Fig. 17 of Nidever et al. (2015).
sents a unique, state-of-the-art capability applicable to a
broad range of groundbreaking Galactic science applications that may not fall within the purview of the primary
APOGEE mission. (2) Not all interesting and relevant
Galactic science could be included in the primary survey program, but could be addressed in a limited way
through an ancillary science program. (3) Some science
programs that might be worth pursuing as main survey
science require some verification and testing in pilot programs. (4) Leaving some amount of survey time in reserve allows the opportunity to respond to new developments in the field or to incorporate originally unanticipated science of great value.
Given these motivations, 5% of the total fiber-hours15
of the APOGEE survey were made available for a formal APOGEE Ancillary Science Program. The main
criteria for selecting such proposals was that the ancillary observations result in novel and compelling scientific
contributions and that they not impact negatively the
primary objectives of the APOGEE survey. Especially
compelling were proposed programs that could enhance
the productivity and impact of the primary APOGEE
survey. Several of the most meritorious proposals in the
APOGEE calls for ancillary science had as primary goals
the improved calibration of the APOGEE database. A
few approved ancillary science programs served as the
basis for a major redefinition of APOGEE targeting to
include significant attention to Kepler mission targets
(§4.1.2).
Three calls for proposals to the APOGEE Ancillary
Science Program were solicited: September 2010, March
2012, and March 2013. Two flavors of ancillary science
targeting were implemented: (a) sets of individual fibers
placed on specific targets in already-existing APOGEE
survey pointings, and (b) use of up to all ∼230 APOGEE
“science” fibers in a new pointing not already within the
general APOGEE survey plan. The selected Ancillary
Science programs are described in detail in Appendix C
of Zasowski et al. (2013). Note that, while all collected
APOGEE spectra are automatically processed through
the data reduction and analysis pipelines, for some of
the programs focused on targets significantly different
from those in the main survey, there is no guarantee that
the automatically generated data products are optimal,
or even reliable. All special processing and analysis of
Ancillary Science Program data are the responsibility of
the principal investigators of each selected project.
4.4. Plate Design and Drilling
Once prioritized lists of selected targets (science, telluric calibrators, sky positions) have been generated for
each plate design, they are fed to standardized SDSS
plate design software. This software takes the input targets’ celestial coordinates and generates the final linear
(x, y) plug plate drill pattern for the plate design. The
software accounts for potential fiber collisions between
all fibers from both APOGEE and MARVELS, as well as
collisions between science fibers and acquisition or guide
fiber bundles. The algorithms also take into account the
field curvature of the Sloan 2.5-m Telescope (to which the
15 The “fiber-hour” metric is defined so that one fiber-hour represents the allocation of one fiber for one visit, which is about one
hour long.
Majewski et al.
25
stars in each plate design are not directly correlated to
any designated cohort divisions, except as the sorting by
magnitudes of stars in the cohorts places them into an
appropriate fiber color by default.
Fig. 13.— Photo of a shared APOGEE/MARVELS plugplate
(plate #5632), as marked for plate plugging. This particular plate
is for a field featuring the globular cluster M3, whose position on
the plate can be identified by the concentration of fiber holes to the
lower left. The holes connected by the zigzagging tracings mark
those associated with MARVELS, whereas the red, green and blue
circled holes show those intended for the bright, medium and faint
APOGEE fibers, respectively. The latter holes are grouped into
small “zones” (indicated by the irregularly-shaped, closed loops)
by the pluggers as a way to organize areas on the plate anticipated
to be serviced by fibers in a single anchor block (having two red,
two green and two blue fibers each). (Photo by W. Richardson.)
plugplates are bent during observing) and the differential
refraction expected for the nominal hour angle at which
each plate of a given declination might be observed. In
some cases, due to the uncertainty in scheduling, multiple plates might be generated from the same plate design
input files, differing only in the potential hour angle of
observation.
In addition to establishing the precise coordinates for
each star based on refraction considerations, the plate design code also sorts the intended targets into three magnitude bins of 100 stars each. The stars in each magnitude
bin are assigned to fibers of a given sheathing color (red,
green or blue), by which fiber management is achieved in
the telescope focal plane to separate the brightest spectra
from the faintest spectra in the spectrograph focal plane
(see §3.1); this separation is needed to minimize contamination of any spectrum by the PSF wings of adjacent
spectra. Figure 14 illustrates how this fiber management
scheme creates a repeating pattern of variable spectrum
brightness as a function of fiber pseudoslit position as
projected onto the spectrograph focal plane.
The plates are drilled on a 6-axis, computerized (CNC)
milling machine at the University of Washington, and
then shipped to APO. At APO, the plates are manually marked to identify which holes correspond to stars
designated to red, green or blue-sheathed fibers by way
of an overhead projection onto the aluminum plate of
the fiber plugging color scheme (Fig. 13). Note that
the red/green/blue = bright/medium/faint division of
Fig. 14.— A portion of a raw 2-D APOGEE image from observations of a bulge field. The horizontal stripes correspond to individual stellar spectra. Vertical bright bands correspond to airglow
features at the same rest wavelength in each spectrum, whereas absorption features at the same horizontal position from spectrum to
spectrum correspond to telluric absorption features. Also obvious
are variations in the expression of stellar atmospheric absorption
features from star to star, evidenced by their varying strengths
due to temperature and chemical composition differences, as well
as changing relative positions due to Doppler shifts. Fiber assignments were managed by color-coding the fiber jackets at the
telescope end for stars in each field sorted into three brightness
groups (bright, medium, faint). These fibers were sorted at the
spectrograph slit head into a repeating pattern of faint-mediumbright-bright-medium-faint to minimize the contamination of any
given spectrum by the PSF wings of a much brighter spectrum
in an adjacent fiber. This management scheme gives rise to the
brightness modulation pattern apparent in this image.
5. SURVEY OPERATIONS
5.1. Standard Observing Procedures
As with all SDSS observing, APOGEE observing was
typically conducted with the use of a package of standard operating scripts that orchestrate nightly activities
through the observatory STUI (§3.2.2).
APOGEE science observing was based on standardized “visits” (§2.8) to a scheduled set of fields using corresponding plugplates designed and drilled for specific
hour angles (§4.4), and plugged with fibers in advance.
Each standard visit consisted of eight 500 second exposures taken at two array dither positions (“A” and “B”;
§3.2.1) in two ABBA sequences. A 500 second exposure
consists of a sequence of 47 detector readouts, performed
in intervals of 10.7 seconds, which generates a sampleup-the-ramp data-cube (see §3.2.2). This ∼67 minute
exposure sequence plus two dark exposures taken during
the change of the plugplate cartridges yields a typical
visit length of 75 minutes. A plugplate was typically revisited on multiple nights to build up the required S/N
according to the cadence rules described in §2.8.
26
APOGEE
To operate usefully in less than ideal weather conditions and to take full advantage of extra pockets of observing time, guidelines had to be established for maximizing the usefulness of “non-standard visits”. Therefore, a minimum data quality to count a visit as successful was set at at least one AB dither pair with each
500 second exposure having a S/N ≥ 10, the minimum
needed to derive the stellar radial velocity at the required
survey precision.16 To aid in the assessment of exposure
quality, the observers had access to “quick look” reductions (simplified versions of the data reduction pipeline;
§6.2-6.3) of the data in near real time that produced plots
of accumulated S/N as a function of magnitude. Over
the course of a night, the available APOGEE time would
be divided into standard field visits, with any additional
observing time allocated to either gathering extra S/N
on a particular plate or creating a “short visit” with a
new plate, at the discretion of the observing staff to maximize observing efficiency.
Because telescope guiding is done at optical wavelengths, APOGEE plates were observed with the guiding
software making refraction corrections to keep 1.6 µm
light in the fibers. In the case of fields observed jointly
with MARVELS the guiding wavelength was set to a
compromise wavelength of 1.1 µm.
Stability of the APOGEE instrument limits the
amount of calibration needed on a nightly basis. At the
beginning and end of each night with potential APOGEE
observations the gang connector is connected to the calibration box to collect a standard calibration sequence
that includes long dark frames as well as exposures of
the tungsten halogen, ThArNe and UNe lamps at both
dither positions (§3.2.2). At the end of the night we also
take a set of internal flat fields. In addition, once each
night 4 × ABBA exposures are taken with all fibers on
sky; the resulting airglow spectra are used for monitoring
the LSF and PSF of the instrument.
A full observing night can generate ∼100 GB of data,
which are then compressed and transferred from the
mountain to the Science Archive Server (SAS) (see §8.3),
where they are stored in disk. These raw data consist of
large data cubes containing all the 47 readouts making
up every single 500 second exposure. The subsequent
processing and reduction of these data are described in
§6.1.
5.2. Observing Constraints, Strategies and Scheduling
From 2011Q2 to 2014Q2 APOGEE (and initially,
MARVELS in parallel) operated during all bright time
(lunar phase < 39%), as well as all “grey” time (lunar phase 39-56%) for LSTs when the North Galactic
Cap was not visible. APOGEE observations pushed the
Sloan Telescope to several new observing regimes and
limits — e.g., with respect to lunar phase, airmass, twilight, cadencing and sharing of the focal plane by two
different instruments (Fig. 15). With a number of observing constraints different than those required by the
optical programs, integrating the APOGEE program into
SDSS operations added new layers of complexity to telescope scheduling and plugplate cartridge organization,
especially on nights shared between all three operating
16 For reference, the typical visit of eight 500 second exposures
for a “3-hour” plate reached a S/N ∼ 63 for the faintest stars.
Fig. 15.— Photo of Apache Point Observatory during the
APOGEE first light observing run showing the Sloan Telescope
(right of center) pushed to new observing regimes — pointed to
the Galactic center at extreme airmass, near the full moon, and
near the light pollution from El Paso (which affects near-infrared
bright time observations less than dark time optical observations).
The constellations of Sagittarius and Scorpio are obvious on the
right hand side of the image. (Photo by S. R. Majewski.)
surveys (BOSS, MARVELS and APOGEE). Within the
APOGEE portions of nights, internal scheduling software was developed to organize the nightly observing for
efficiency, and to account for APOGEE observing constraints, as well as those for MARVELS during joint operations. These APOGEE scheduling constraints included:
• Moon avoidance: Observations were not allowed
within 15◦ of the moon (30◦ for MARVELS shared
observations). However, because the ecliptic passes
directly through the Galactic bulge, this limit was
loosened to 10◦ for bulge observations; without this
adjustment the amount of potential bulge observations would have been reduced by 50%.
• Airmass limits: The central regions of the Galaxy,
containing highly prized APOGEE targets, transit at very high airmass at APO. Compared to the
optical, near infrared observations benefit from reduced differential refraction and atmospheric extinction, which made it possible to undertake the
desired extreme airmass observations. One important limitation, however, is the still significant differential atmospheric refraction at low elevation,
which forced the adoption of more limited drilled
areas (1-2 degrees) on the plugplates. Despite the
smaller angular coverage it was easy to fill all the
science fibers in these fields, due to the high stellar density of the central regions of the Galaxy.
Those fortunate advantages made it possible for
APOGEE to probe the Galactic bulge, the Galactic center and even further south (to δ = −32◦ ).
Hardware limits set the maximum APOGEE airmass (X) to X < 3.2, but a limit of X < 1.7
was necessary for MARVELS co-observed plates.
APOGEE utilized the standard X > 1.01 limit of
the Altitude-Azimuth mounted Sloan Telescope.
• Hour angle: The APOGEE windows of opportunity were set so that plates had to be observed
with no more than 0.5 arc seconds of differential
refraction across the plate. However, the reduced
Majewski et al.
H-band differential refraction also allowed greater
APOGEE flexibility in observing plugplates farther
from their nominally drilled hour angles than is
possible for optical observations.
• Plate cadence: As discussed in §2.8, the nominal
survey plates had to be observed over at least three
visits each meeting the minimum S/N per visit requirement (§5.1) with separations of at least 3 days
between the two closest observations and at least
25 days between the first and last observation.
Survey plate scheduling was done by a module originally designed to optimize cadence observations for the
MARVELS survey that was later adapted to account for
both cadence and S/N constraints of the APOGEE survey. Beyond accounting for the above constraints, the
scheduling software invoked several additional rules to
optimize efficiency.
For example, bulge plates and other plates with limited observability windows were given highest priority.
Other plates were given relative priorities that accounted
for their individual cadence histories and net accumulated S/N . Special attention was needed for scheduling
of “non-standard” visits to take advantage of occasional
extra pockets of observing time. For example, standard
visits for the eight bright time cartridges were insufficient to fill the available time on long winter nights; in
this case, longer than standard visits could be applied to,
e.g., (a) halo plates that have been designed with fainter
than main survey stars (see §4.2.3), (b) plates that — due
to poor weather or prematurely ended previous visits —
were behind on S/N accumulation despite satisfying cadence constraints, or (c) plates that could, conversely, be
“pre-loaded” with extra S/N allowing useful, but shorter
than standard visits on other (e.g., shorter) nights. In
the interest of steady progress on the completion of fields,
another, albeit more loosely followed, scheduling strategy
was that the full set of observations for nominal, threevisit plugplates, if at all possible, not stretch beyond one
observing season.
On long nights, when the full eight fiber plugplate setups could be observed, APOGEE was able to record
spectra for 1840 target stars, along with 280 hot telluric
star calibrators and 280 sky fibers (§4.2.4).
5.3. Special Observing Strategies and Campaigns
5.3.1. Twilight Observing
Another advantage of near-infrared over optical spectroscopy is the ability to work deeper into twilight. By
the second year of the APOGEE campaign it became
clear that above average poor weather at certain LSTs
was going to make it challenging to complete the planned
observations of the bulge and Kepler field plates. In view
of this situation, the BOSS team and SDSS observing
staff graciously agreed to allow APOGEE to make use
of the small windows of the dark and grey time morning twilight not useful for BOSS observing. Fortunately,
the LSTs of greatest need could be serviced in spring and
summer, so this special twilight observing was conducted
only between the vernal and autumnal equinoctes to limit
the impact on the observers. BOSS observing is limited
to 15◦ twilight, but in cases where a standard BOSS observation concluded by 20◦ twilight there was insufficient
27
time for a new BOSS observation, but enough time for
APOGEE to observe a plate to 8◦ twilight. This was
sufficient to collect, at minimum, an AB dithered pair of
exposures and as much as an ABBAAB sequence. These
short visits — useful for accumulating S/N for the 1hour bulge and Kepler field plates, as well as cadence
visits for main survey plates that compete for the same
LSTs — were found to be essential to the completion of
the APOGEE survey plan.
5.3.2. Year 3 and Dark Time Campaign
In the final half-year of SDSS-III it became evident
that the BOSS survey was ahead of schedule and likely
to finish early; thus some dark time was made available to
the collaboration for additional projects. At this point,
though on pace to reach the required number of stars,
APOGEE was significantly behind schedule on completing plates in the inner Galaxy and Kepler regions, due
to atypically poor summer weather.17 Through access
to significant portions of that dark time, not only did
the main APOGEE survey manage to complete virtually
its entire field plan, but a number of APOGEE bulge
plates that had only lower quality commissioning observations could be reobserved for survey quality data (Fig.
10c). In addition, two new APOGEE ancillary science
programs18 were added beyond those described in Zasowski et al. (2013).
5.3.3. Bright Standard Star Calibration
Calibration of the APOGEE velocity, stellar parameter, and chemical abundance data relied, to a large extent, on data obtained from special targeting of numerous open and globular clusters as well as the asteroseismology targets in the Kepler and CoRoT fields (§4). In
addition a large range of bright, previously well-studied
“standard stars” were also observed for calibration purposes. A compiled target catalog of such stars included
an assortment of stellar types meant to calibrate specific
regions of stellar parameter space. Especially useful were
stars not well represented in clusters (e.g., carbon stars)
and subsamples designed to address specific issues, such
as, e.g., S class stars, which aided the search for lines
due to neutron capture species in the APOGEE wavelength window. Two targets critical to calibration efforts
were the well-studied metal-deficient K giant “reference”
standard Arcturus (e.g., Hinkle et al. 1995) as well as the
asteroid Vesta (providing a reference solar spectrum).
To obtain spectra of these bright sources is a challenge for the Sloan 2.5-m telescope and not practical
through drilling and observing specialized plugplates.
Initially these spectra were obtained using an observing script (“Any Star Down Any Fiber” or “ASDAF”)
that enabled the observers to put the bright standards
down an APOGEE fiber on any currently loaded plugplate, a procedure implemented only during moderately
17 APOGEE remained on pace to complete the 100,000 star goal
primarily because it was ahead of schedule in the Galactic anticenter region due to atypically good winter weather. As discussed in
§4.1.2, this enabled a significant expansion of the anticenter program.
18 “Infrared Spectroscopy of Young Nebulous Clusters (INSYNC)” ONC clusters” (e.g., Cottaar et al. 2014) and “Probing
Binarity, Elemental Abundances, and False Positives Among the
Kepler Planet Hosts” (e.g., Fleming et al. 2015)
28
APOGEE
cloudy nights when main survey observing was not practical. Subsequently, this rather labor-intensive strategy
was replaced by use of New Mexico State University’s
(NMSU’s) 1-m telescope, to which a fiber optic link was
run that can be connected to the APOGEE long fibers.
Through a time-sharing agreement with NMSU, a fraction of the dark time was reserved for 1-m bright star
calibration observations with APOGEE, made even more
efficient by it being robotized (the 1-m program is described further in Holtzman et al. 2015).
5.4. Survey Timeline
The APOGEE program consists of two distinct observing campaigns — “commissioning” (May-July 2011)
and “survey” (August 2011-July 2014) — divided by the
change in spectrograph optical configuration during the
shutdown in Summer 2011 (see §3.3). “Commissioning” observations consisted primarily of 1-visit and 3visit fields to test instrument performance, calibration,
and limitations. The “survey” observations were conducted over the originally intended three year APOGEE
campaign from August 2011 to July 2014 and produced
acceptable quality survey data during 520 days spanning
over 1900 individual field visits. The entire three year
survey campaign was conducted uninterrupted, with the
instrument continuously sealed and cold in the same optical state to provide an extremely uniform data set.
6. DATA HANDLING AND PROCESSING
The software chain used to convert the raw APOGEE
data to final data products is divided into three primary
programs: (1) real or near-real time codes to pre-process,
bundle and archive the raw data (§6.1); (2) the data reduction pipeline, which converts the collected data cubes
into extracted, 1-dimensional, calibrated spectra, and,
along the way, derives radial velocity information (§§6.2,
6.3, and 6.4); and (3) the APOGEE Stellar Parameters
and Chemical Abundances Pipeline (ASPCAP), which
aims at achieving the unprecedented feat of determining stellar parameters and up to 15 elemental abundances through the automatic analysis of APOGEE’s
high-resolution H-band spectra (§6.5). Steps (1) and
(2) are performed by the apred software (Nidever et al.
2015), whereas step (3) is performed by ASPCAP (Garcı́a
Pérez et al. 2015).
Because of the APOGEE observing strategy, the subsequent reduction routines generate a number of intermediate files. For the following discussion, a few terms
need a clear definition. Each final combined spectrum
(1D) consists of the combination of a number (NVISITS)
of visit spectra (1D). In turn, each normal visit spectrum
results nominally from the combination of 4 (AB-BAAB-BA) pairs of dither spectra (1D), obtained at two different dither positions (i.e., 8 distinct spectra). Each 1D
dither spectrum is extracted from a bias-subtracted, flatfield and cosmic-ray corrected 2D array, which in turn
is created by pixel-by-pixel fits to the numerous detector readouts that constitute the raw data cubes (§3.2.2).
Each data cube consists of a time series of 47 up-theramp readouts of all three detectors, performed every
10.7 seconds along the exposure (§5.1).
6.1. Basic Reductions: From Data Cubes to 2D Arrays
At the end of every observing night, APOGEE data
are compressed and transferred to the SAS (§5.1), and
all data reduction is done subsequently off the mountain.
In the following, we briefly describe the steps leading to
the generation of a final APOGEE combined spectrum.
In this first processing stage each data cube is corrected
for standard detector systematic effects and converted
into a 2D array. Every individual readout is corrected
for bias variations in the detectors and electronics. Bias
measurements are performed on a combination of pixels
generated by the readout electronics and a set of reference pixels around the edge of each detector. Next, a
dark frame resulting from combination of multiple individual exposures is subtracted from each individual readout. The 2D arrays are then generated through linear fits
to the time series of SUTR readouts for each pixel, and
the best fitting slope is multiplied by the exposure time
to generate the final pixel counts. The process allows for
detection, correction, and flagging of pixels affected by
cosmic rays. Finally, 2D arrays are corrected for pixelto-pixel sensitivity variations through division by a normalized flat field frame. The output of this reduction
step for one visit is eight calibrated 2D arrays, four for
each dither position.
6.2. From 2D Arrays to 1D Dither Spectra
As a next step, spectral extraction and wavelength calibration are performed on each 2D array. Spectra are
extracted through modeling of the spatial PSF of all 300
fibers as a function of wavelength in a way that accounts
for the overlapping of the PSFs between adjacent spectra.
The model is fit to a high S/N flat-field frame obtained
immediately after each science exposure.
Wavelength calibration is the next stage of the reduction, and as usual, is based on arc lamp exposures.
Because each fiber occupies a different position in the
pseudo-slit, fiber-to-fiber wavelength scale variations exist, so that individual calibrations for each fiber are necessary. The APOGEE spectrograph is stable enough that
a single polynomial relation is adopted for each fiber,
with zero point corrections applied on the basis of measurements of central wavelengths of airglow lines. In conformity with previous SDSS standards, APOGEE adopts
vacuum wavelengths. For details of the adopted conversion between vacuum and air, see Nidever et al. (2015).
The overall wavelength scale suffers drifts linearly over
time, due to a slowly varying flexure in the instrument
optical bench as the liquid nitrogen tank depletes over
time (§3.2.2). Every time the tank is refilled, the scale
undergoes a large “reset” shift, which brings it back to
the original scale. These shifts are measured using a set
of bright airglow lines and the wavelength scale is corrected accordingly. The accuracy of the resulting wavelength solution at any given pixel of an APOGEE spectrum is of order 0.1 pixel or 0.03-0.04 Å (Nidever et al.
2015). The outputs of this reduction stage for one visit
are 8 wavelength-calibrated 1D dither spectra, 4 for each
dither position.
6.3. Dither Combination, Sky Subtraction, Telluric
Correction, and Flux Calibration
In the next reduction stage, dither pairs are combined
into well sampled 1D spectra, sky subtraction is per-
Majewski et al.
formed, and the signature of telluric absorption is removed.
The shift between the spectra in each dither pair is
determined to high accuracy through cross correlation of
the two spectra. Before combination, each dither spectrum is subject to sky subtraction, which is critical due
to the presence of strong OH emission lines and a faint
continuum, which is stronger in the presence of clouds
and moonlight. The contribution of sky background to
the spectrum of any science fiber is determined through
interpolation of the spectra of the four closest fibers from
among the 35 sky fibers distributed across the APOGEE
FOV (§4.2.4). Because of fiber to fiber LSF differences,
subtraction of sky lines is not perfect, and can result
in the presence of significant residuals in pixels situated
at or near the positions of very strong lines that renders these pixels useless for science. While future improvements in the reduction pipeline may ameliorate the
situation, the S/N in those pixels will nevertheless be
substantially deteriorated due to high Poisson noise.
Telluric line absorption in the APOGEE spectral region due to the rovibrational transitions of the H2 O,
CO2 , and CH4 molecules are removed through the fitting
of telluric absorption models to observations of the 35 telluric standards distributed across the field (§4.2.4). For
each telluric standard, synthetic telluric spectra based
on model atmospheres by Clough et al. (2005) are fitted
to the full family of absorption lines from each molecule
separately to generate scaling factors to the model spectrum of each molecule at the position of each telluric
calibration fiber. Polynomial surfaces are then fitted to
describe the spatial variation of the scaling factors, and
the correct scaled model is determined for each science
fiber through interpolation within those surfaces. For
each science fiber, models are then convolved with fiberspecific LSFs, and divided into the science spectrum
Although the above telluric correction method works
well, it has shortcomings related to errors in the wavelength solution, and uncertainties in both the telluric absorption model and the adopted LSFs. Because a large
fraction of APOGEE pixels are affected by telluric absorption, improvements in telluric correction are a high
priority for future pipeline improvements.
Each sky-subtracted, telluric-corrected pair of dither
spectra are then combined into a single better-sampled
spectrum, using the shifts determined as described
above. Each of these resulting spectra are then coadded
to generate a single visit spectrum.
Flux calibration consists of two steps. First, an approximate relative flux calibration is applied to dither
spectra to remove the spectral signature of instrumental
response; this response function was determined through
observation of the black body spectrum from a calibration source (§3.2.2). Later on, after dither spectra are
combined to generate visit spectra, the latter are scaled
to match the object’s cataloged H-band magnitude. Because the spectra are later reshaped through polynomial
fits to the pseudo-continuum prior to performance of stellar parameter and abundance analysis, flux calibration is
not a critical aspect of data processing.
6.4. Radial Velocities and Generation of Combined
Spectrum
29
Radial velocities (RVs) are one of APOGEE’s key data
products. There are two main steps related to the RV determination within APOGEE. One step determines relative RVs between different visits, and the other fixes
these measures to an absolute scale.
In both steps, RVs are determined via a cross correlation between the object spectrum and a particular template. Observed and template spectra are initially both
in a log-linear wavelength scale, so that a Doppler correction can be performed by shifting all pixels by the same
value. Before cross correlation, bad pixels are flagged
and the pseudo-continuum is normalized through a loworder polynomial fit to the spectrum of each detector
separately. A Gaussian fit is performed to the crosscorrelation distribution and the position of the peak and
its error are converted into a velocity shift and uncertainty.
Visit RVs are determined through an iterative process
via cross correlation with the combined spectrum. Initial
relative RVs, obtained from cross correlation with the
highest S/N visit spectrum, are used to bring all visit
spectra to a common velocity scale, and making possible
the production of an initial combined spectrum. The
process is then iterated by adopting the most recently
created combined spectrum as a template.
Absolute RVs are obtained through cross-correlation
of the combined (and visit) spectra with synthetic spectra from an “RV mini-grid”, which is a subset of the
APOGEE spectral grid (§6.5.1), and consists of 538 spectra over a wide range of stellar parameters and chemical compositions. The numbers resulting from this cross
correlation are further adjusted by the barycentric correction, to produce heliocentric RVs.
6.5. Stellar Atmospheric Parameters and Elemental
Abundances
Elemental abundances are another primary data product of the APOGEE survey. Stellar parameters — effective temperature (Teff ), surface gravity (log g), metallicity ([M/H]), and microturbulence (ξt ) — are also necessary stepping stones towards elemental abundances and
spectroscopic parallaxes. An understanding of the possible systematic effects on the derived elemental abundances and distances inferred from APOGEE spectra requires a good grasp of the procedures followed for the
derivation of atmospheric parameters and metallicities.
In this section, a brief description of those procedures is
provided, but the reader is referred to Garcı́a Pérez et
al. (2015) for details.
The APOGEE Stellar Parameters and Chemical Abundances Pipeline (ASPCAP) implements a two-step process: first, the determination of stellar parameters from
a fit of the entire APOGEE spectrum to model spectra,
and second, adoption of these parameters as inputs for a
fit to small windows of the spectrum containing spectral
features associated with each particular element to derive
its abundance. In the following subsections we describe
each of the main ASPCAP processing steps.
6.5.1. Grid of Synthetic Spectra
Stellar parameters are obtained through determination
of the best fitting synthetic spectrum from across an extensive grid spanning six stellar atmospheric parameter
30
APOGEE
dimensions (Teff , log g, [M/H], [α/M], [C/M], and [N/M])
by χ2 minimization (§6.5.3). The accuracy of the results
is fundamentally dependent on the fidelity with which
spectra from the synthetic grid reproduce real stellar
spectra. We briefly describe the main ingredients entering the calculation of this spectral grid, and refer the
reader to Zamora et al. (2015) for further details.
Synthetic spectra were calculated using the Advanced
Spectrum Synthesis 3D Tool (ASSεT) code (Koesterke
2009), adopting 1D model atmospheres calculated in local thermodynamic equilibrium (LTE) by Mészáros et
al. (2012) and a line list customized for the analysis
of APOGEE spectra (Shetrone et al. 2015; Appendix
E). The adopted model atmospheres were calculated using the ATLAS9 code (Kurucz 1993), adopting newly
computed opacity distribution functions as described by
Mészáros et al. (2012) and the solar abundance pattern of
Asplund et al. (2005), as well as variations in the abundances of carbon and α elements. Spectra were calculated over a range of [M/H], [α/M] (where all α elements
are assumed to vary in lockstep), [C/M], and [N/M]. The
chemical compositions adopted matched those used in
the generation of the model photospheres, except for the
case of nitrogen, whose variation was not seen to affect
the photospheric structure in an important way.
The line list resulted from an initial implementation
of the Kurucz line list, improved by introduction of both
theoretical and laboratory transition probabilities (gf values) following an exhaustive critical search of the existing literature, and further supplemented by laboratory
values of key transitions obtained by our collaborators
(e.g., Wood et al. 2014) by request (see Appendix E).
Further refinement of gf values and damping constants
was achieved through spectral synthesis of the solar and
Arcturus spectra (see Shetrone et al. 2015, for details),
where departures from laboratory values were capped at
no more than twice the nominal uncertainties.
The synthetic spectra are smoothed to the APOGEE
resolution (R=22, 500) by convolution with a single,
empirically-determined, average APOGEE LSF (Nidever et al. 2015; Holtzman et al. 2015) and sampled into a
logarithmic scale to match the sampling of the APOGEE
data (∼ 104 wavelengths). Synthetic spectra are further
normalized through fitting of a polynomial to the upper
envelope of the spectrum, for comparison with observed
spectra treated in the same way (see below).
Efficient computation would require storage of the entire spectral grid in memory, which is currently not
practical. Therefore, fluxes are compressed using Principal Component Analysis (PCA) and it is the PCAcompressed grid that is compared with the observed spectra for atmospheric parameter determination. To expedite calculations further, the grid is split into two distinct
sub-grids, with Teff spanning ranges approximating those
of GK (3500-6000 K) and F (5500-8000 K) spectral types
(see Zamora et al. 2015).
Each synthetic spectrum is characterized by seven
parameters, namely Teff , log g, [M/H], [α/M], [C/M],
[N/M], and ξt (microturbulence). With multiple nodes
in each parameter, the final 7-dimensional spectral subgrids consist of about 1.7 million (GK stars) and 1.4 million (F stars) spectra covering the entire range of expected atmospheric parameters and chemical composi-
tions.
Abundances of individual elements are defined as follows:
[X/H] = log10 (nX /nH ) − log10 (nX /nH )
(1)
where nX and nH are the number, per unit volume of
the stellar photosphere, of atoms of element X and hydrogen, respectively. The metallicity [M/H] is defined as
an overall scaling of metal abundances for a solar abundance pattern, while [X/M] is the deviation of element
X from that pattern:
[X/M ] = [X/H] − [M/H]
(2)
Because the search for the best fitting spectrum within a
7-D space is considerably slow at present, the library dimensionality has been reduced to 6 (thereby reducing the
overall size of the libraries by a factor of 5) by constraining microturbulent velocities through the adoption of a
relation with surface gravity (see details in Holtzman et
al. 2015 and Garcı́a Pérez et al. 2015). For Teff > 8000 K,
where molecular lines are entirely absent, the grid is described by only three parameters, Teff , log g, and [M/H].
6.5.2. Pre-processing of Observed Spectra
A few additional processing steps are taken to prepare
the observed spectra for comparison with the synthetic
grid. First, to optimize the fitting process and increase
the robustness of the χ2 statistic, pixels affected by cosmic rays, saturation, cosmetic problems, or strong airglow lines are flagged and ignored during spectral normalization and χ2 minimization. Moreover, to account
for small systematic errors in spectral calibration, we set
a minimum flux error of 0.5 percent for all remaining
pixels.
Next, to minimize uncertainties due to interstellar reddening, atmospheric extinction, and errors in relative
fluxing, spectra are flattened and normalized through the
fit of a polynomial to their upper flux envelopes. Fits are
performed through a σ-clipping algorithm to the spectra
on each of the three detector arrays independently. An
identical normalization is performed on the grid of synthetic spectra, using the same spectral regions with the
same σ-clipping and polynomial form.
This process does not necessarily produce a normalization to the true stellar continuum, but rather to a
“pseudo-continuum”. This is because, at the APOGEE
resolution, it is impossible to resolve spectral regions
that are unaffected by any line opacity (i.e., true continuum regions) in the spectra of the coolest and/or most
metal-rich stars. This fact alone largely dictates our
methodological choice for normalized fluxes over equivalent widths as APOGEE’s fundamental observable for atmospheric parameter and elemental abundance determination through comparison with model predictions. This
choice is predicated on the notion that normalized fluxes
are less strongly affected by continuum placement uncertainties than equivalent widths, especially if synthetic
and observed spectra are normalized identically.
6.5.3. Stellar Atmospheric Parameter and Abundance
Determinations
Stellar atmospheric parameters and the relative abundances of C, N and the α elements are determined by the
Majewski et al.
FORTRAN90 code FERRE (Allende Prieto et al. 2006),
which searches within the 6-D grid of synthetic spectra
for the best match to each observed APOGEE spectrum.
The code uses a χ2 criterion as the merit function, and
the searching method is based on the Nelder-Mead algorithm (Nelder & Meade 1965). The search is run 12
times starting from different grid locations: three positions in Teff and two each in log g and [M/H]. Two points
symmetrically located around the grid center are adopted
for log g, [M/H], and Teff , whereas for the latter a starting
point at the central grid value is also adopted. A single
(solar) starting value is adopted for [C/M] [N/M], and
[α/M]. The code returns the best matching spectrum, obtained through cubic Bézier interpolation within the grid,
as well as the parameters associated with that spectrum
(Teff , log g, [M/H], and [C/M], [N/M] and [α/M] abundance ratios), the covariance matrix of these parameters,
and the χ2 value for the best-matching spectrum.
The analysis described above delivers an overall metallicity and a mean α-element relative abundance, as well
as relative abundances of carbon, and nitrogen. Based
on fits of the entire spectrum, these numbers can only be
considered as preliminary values. A subsequent, more
refined analysis takes place that directly and more accurately evaluates the abundances of carbon and nitrogen,
and also derives the abundances for all remaining target
elements (O, Na, Mg, Al, Si, S, K, Ca, Ti, V, Mn, Fe,
and Ni). This is accomplished by re-running FERRE on
each spectrum, this time restricting the search to a more
limited area of parameter space, where Teff , log g, and ξt
are held fixed. For each element, spectral windows are
defined that maximize the sensitivity to that particular
element’s abundance, while minimizing sensitivity to the
abundances of all other elements (see Smith et al. 2013;
Cunha et al. 2015, for details). After the first iteration
of FERRE determines the stellar parameters, a series of
new FERRE runs are performed, one for each element.
In each of them, all the dimensions remain fixed except
that used for the abundance of the element of interest
fitting only the specific spectral windows for that element. Each pixel is weighted on the basis of sensitivity
to the elemental abundance being fitted, and also according to the quality of the fit of the Arcturus spectrum at
that pixel by APOGEE synthetic models (Shetrone et al.
2015).
Elemental abundances are thus obtained by searching
for the best fit of each spectral window. For any given
element, the search is performed in only 1-D, where the
only varying parameter is the abundance of that element while the stellar parameters and all other elemental
abundances are held fixed. For further details we refer
the reader to Garcı́a Pérez et al. (2015).
7. ACHIEVED PERFORMANCE
In this section, we briefly examine the performance of
the APOGEE survey, understood as the result of the
combination of instrument, survey strategy, operations,
and data processing and analysis tools and procedures,
contrasting it with the requirements described in §2.
7.1. Final Sample Statistics and Galactic Distributions
Upon concluding three years of operation, the
APOGEE survey obtained over a half million spectra
of 163,278 stars. Of these, 12,140 were obtained during
31
commissioning but were never reobserved; due to the issues discussed in §3.3, these commissioning data do not
meet the survey science requirements. Therefore, the total number of survey quality targets delivered in DR12 is
151,138. Of these, 14,692 are telluric standards, which
leaves a net total of 136,446 survey quality science targets. Thus, APOGEE exceeded by more than 35% the
original technical requirement on sample size. Such a
substantial increase over the required performance was
achieved due to the combination of factors described in
§4.1.2 and §5.3.
Determining the sample breakdown according to
Galactic component is not simple, because many of the
fields present a substantial overlap of components, particularly those fields below |b| = 20◦ . Nevertheless, we
provide below a simple breakdown of the sampled survey
stars by rough field type, with the caveat that of course
not all targets in a field belong to the Galactic component
defining these types, which, for simplicity, we categorize
broadly by Galactic coordinates:
• 13,473 stars in bulge fields (|b| < 16◦ , −10◦ < l <
11◦ );
• 54,988 stars in halo fields (|b| > 16◦ );
• 82,677 stars in disk fields (|b| < 16◦ , 11◦ < l <
350◦ ).
The halo numbers are inflated by inclusion of fields targeting nearby stars at relatively large Galactic latitudes
(some examples are the Kepler fields and Ancillary Science fields focused on nearby clusters or associations).
Accounting for those, the total number of halo field targets drops to fewer than 40,000.
Along these lines, some specialized target classes of
particular interest include:
• 12,443 stars in Kepler/CoRoT fields;
• 2,035 stars in Sagittarius dSph core fields;
• 3,782 stars in fields in the direction of other known
halo substructure, including streams associated
with the Sagittarius dSph;
• 7,291 stars in fields placed on suspected halo overdensities from the Grid Giant Star Survey (GGSS,
Bizyaev et al. 2006; Majewski et al. 2012);
• 8,112 stars in star cluster fields (fields specifically
targeting open or globular clusters, but not counting disk fields in which open clusters serendipitously were observed);
• 12,115 objects in Ancillary Science fields;
• 880 bright stars observed with the NMSU 1-m telescope + the APOGEE spectrograph.
Figures 16 and 17 show the computed19 Galactic spatial distributions for the main APOGEE survey targets
19 Distances are calculated using the method of Santiago et al.
(2015) applied to stars in DR12 with signal-to-noise ratio larger
than 70, a temperature range of 3500 K < Teff < 5500 K, positive extinctions with AK < 3, good 2MASS photometry, ASPCAP
analysis χ2 < 50, and metallicity errors less than 0.3 dex. The dis-
32
APOGEE
of scientific applications, including the search for stellar
companions (§7.4.2) and stars with spectral variability
(§7.4.1).
100000
Number of Stars
10000
1000
100
10
1
Fig. 16.— The computed Galactic distribution of APOGEE targets as projected on the Galactic plane. Comparison to Figure 11
shows that the anticipated spatial coverage of the Milky Way has
been achieved.
0
Number of Stars
10000
10
20
Number of Visits
30
40
Visits
3
6
12
24
1000
100
10
1
0.5
Fig. 17.— Same as Fig. 16 for the computed azimuthallyaveraged RGC -ZGC distribution of main APOGEE survey targets.
Compare to pre-survey prediction in Fig.12.
and demonstrate that the targeting plan as implemented
achieved its general goals for Milky Way coverage (compare to Figs. 11 and 12). A full description of the data
is given in Holtzman et al. (2015).
A unique aspect of the APOGEE survey is that the
majority of the stars are visited multiple times. Figure
18a shows the distribution of numbers of stars having any
particular number of visits over the course of the survey.
As may be seen, the vast majority of stars have three
visits, but a significant tail of stars have been visited
many times more. For example, of order 1,000 stars have
more than two dozen visits. In many cases, these stars
were observed for over a year, and some as long as three
years (Figure 18b). Such time series data enable a variety
tance inference uses PARSEC isochrones (Bressan et al. 2012) applied to the spectro-photometric data from APOGEE and 2MASS
with the overall metallicity of each star determined as the [Fe/H]
measured from iron lines plus the overall [α/Fe] abundance determined from the synthetic fit to the whole APOGEE spectrum (see
Santiago et al. 2015 for further details).
1.0
1.5
2.0
2.5
Maximum Baseline (log days)
3.0
Fig. 18.— (Top) Distribution of the number of stars having a
given number of visits over the course of the survey. (Bottom)
Maximum time baseline for stars having at least 3, 6, 12, or 24
visits.
7.2. Signal-to-Noise Ratio
Achievement of survey requirements in depth would
only be possible if the instrument throughput met the
original specifications of S/N = 100 pixel−1 , at the specified resolution and sampling, at H = 12.2 in 3 hours
of integration time, which translates into an expected
S/N = 105 for a combination of three visits with eight
exposures of 500 s duration each. Overall survey performance is, of course, also impacted by observing conditions (transparency, seeing) and procedures adopted to
track and ensure accumulated signal over multiple visits. Delivered performance in this area is demonstrated
by Figure 19, which shows the distribution of S/N measured in 3-visit spectra of all stars (main science and
ancillary targets — ∼ 1,600 stars) in a narrow range
of (undereddened) magnitudes (12.15 < H < 12.20) at
the 3-visit plate limit of H = 12.2. The distribution is
nearly Gaussian (despite the long tail towards very low
Majewski et al.
S/N ), with a peak at S/N ∼ 93. The dispersion in S/N
is about 30 (FWHM), which is consistent with a ± 0.2
mag (1σ) dispersion in flux. Some of this spread can
be ascribed to photometric uncertainties in the 2MASS
catalog (σH ∼ 0.02 mag at H=12.2)20 , but most of it
reflects the realities of accommodating variable weather
conditions within a semi-rigid observing plan structured
on gathering set numbers of paired 500 second standard
exposures for each plugplate (nominally a dozen pairs
for each 3-visit plugplate). Assuming that the whole of
the scatter is due to weather and seeing variations, one
finds that for the best observing conditions, represented
by those observations more than 1σ (2σ) above the median of the distribution, the S/N achieved was about 106
(118), indicating that the spectrograph delivered, and
probably exceeded, the throughput needed to meet the
S/N requirement established at the survey outset.
33
∼350 ± 30 m s−1 , from comparison with data from other
sources for stars in common (Nidever et al. 2015).
Even though no technical requirements were specified
for the stellar parameters Teff and log g, they drive the accuracy of derived chemical abundances and spectroscopic
parallaxes. Figure 20 shows final DR12 values (Holtzman et al. 2015) for APOGEE stars in a “spectroscopist’s
HR diagram”. The final stellar parameters adopted and
shown in this plot result from calibration of the raw parameters delivered by ASPCAP against photometry and
abundance data from the literature, and asteroseismological surface gravities from APOKASC (Pinsonneault et
al. 2014), as described by Holtzman et al. (2015). There
is very good agreement between stellar parameters from
APOGEE spectra and independent theoretical predictions, shown by the isochrones. Of course, some scatter
of the data about the isochrones is expected given the
dispersion of the sample in age and [α/Fe] at any metallicity. Moreover, some unresolved issues remain. The
most important example happens for red clump stars,
which should be distinctly separated from the red giant
branch. Stellar evolution models, which are confirmed independently by accurate gravities from asteroseismology
for a sub-sample of APOGEE stars in the Kepler field
(e.g., see Fig. 18 of Pinsonneault et al. 2014), predict
a narrow range of surface gravities for red clump stars,
which are not seen in APOGEE data, which instead show
a correlation between Teff and log g. Another unresolved
issue is the slightly too warm APOGEE temperatures at
the cool end. These are areas where refinements in ASPCAP will bring improvements in the future. For a further
discussion of these issues, see Holtzman et al. (2015) and
Garcı́a Pérez et al. (2015).
Fig. 19.— Distribution of S/N for stars with 12.15 < H < 12.20
for stars on 3-visit plugplates.
7.3. Derived Parameters
More important than achieving a nominal S/N requirement is the ability to achieve the desired uncertainties
on higher level survey data products, and in this regard
APOGEE has generally succeeded. A good estimate of
RV precision can be obtained from analysis of repeat
measurements of single stars. In DR12, the RV residuals for stars with multiple visits and S/N ≥ 20 peak at
∼ 70 m s−1 (Nidever et al. 2015). Uncertainties in the
cross correlation method tend to be smaller for spectra
with lots of sharp absorption lines. Therefore, RVs are
more accurate for cooler and/or metal-rich giants than
for metal-poor and/or hotter/high surface gravity stars,
whose spectra are characterized by fewer and/or broader
absorption lines, respectively. In any case, the precision
achieved surpasses the stated requirement (§2.4). The
absolute RV zeropoint of APOGEE is estimated to be
20
Fig. 20.— Final, calibrated, ASCAP-derived parameters for main
survey stars in the nominal temperature range of the APOGEE
main sample. Colors show the metallicities derived for the stars
according to the scale at the top. The lines show Padova PARSEC
(Bressan et al. 2012) isochrones for 4 Gyr populations at various metallicities, against which the calibrated spectral metallicities
may be compared.
The precision and accuracy of the derived elemental
abundances is reviewed in detail by Holtzman et al.
www.ipac.caltech.edu/2mass/releases/allsky/doc/figures/secii2f9.gif
(2015). To achieve the goals in probing Galactic chem-
APOGEE
ical evolution specified in §1.3, APOGEE had a specification for an internal precision in measured chemical
abundances of 0.1 dex, and an external accuracy of 0.2
dex. The precision of the actually derived APOGEE elemental abundances was established by measuring the
dispersion of the measurements at fixed Teff in clusters
stars, where the abundances are expected to be constant.
Precisions were found to be better than 0.1 dex for all
top and medium priority elements, thus formally meeting
the original requirement. For many elements, precisions
are better than 0.05 dex and for only one low priority
element (vanadium) is the precision worse than 0.1 dex.
Comparison of APOGEE [Fe/H] with mean literature
abundances of cluster stars based on high resolution optical spectroscopy, shows that the 0.2 dex external accuracy requirement is met everywhere except perhaps
for [Fe/H] lower than ∼ −1.5. In view of the presence of clear systematic differences between “raw” derived APOGEE values via ASPCAP and literature values (where APOGEE values are higher by up to 0.2 dex)
a calibration to convert APOGEE [Fe/H] into the literature scale was derived (Holtzman et al. 2015). For some
of the other elements, abundance ratios — i.e., [X/Fe],
computed adopting the non-calibrated [Fe/H] — compare well with the literature, in spite of some scatter,
which is likely due to the heterogeneity and uncertainties
in the literature data. For a few elements, such as Ca and
Ti, trends with Teff are found. In the absence of a statistically significant homogeneous sample of optical data
from the literature, it is difficult at this stage to draw firm
conclusions regarding the APOGEE accuracy for some of
the elemental abundances. Nevertheless, while the original requirements are met for most of the elements, more
work is required to, on one hand, refine the elemental
abundances delivered by ASPCAP and, on the other,
more firmly establish the systematic differences that will
inevitably be present between the APOGEE abundance
scales and those of other large observational programs
(typically performed at optical wavelengths).
7.4. Science Demonstrations of Data Capabilities
The achieved performance and capabilities of
APOGEE are perhaps most eloquently demonstrated
by examples of how the collected data may be used in
a wide variety of science applications. In this Section
we briefly discuss a few examples of published and in
preparation research based on APOGEE data, with
an eye towards illustrating the breadth of potential
APOGEE science, both within the confines of Galactic
astronomy and beyond. The order of presentation of
these science examples roughly tracks the degree of
processing of the APOGEE data, as described in §6
— i.e., from direct analyses of the spectral character
of sources, to analyses of derived stellar velocities, to
explorations of bulk metallicity and then more detailed
chemical abundance patter data, to even higher level
results made possible by inclusion of derived stellar
ages, and concluding with analyses incorporating the
former information for the analysis of star clusters and
the interstellar medium.
Fig. 21.— Time series data of a double-line spectroscopic binary,
illustrating the variation in the position of the lines for both the
primary (lines marked by blue arrows) and secondary (red arrows)
stars in the system.
1.15
Oct. 14, 2011
Oct. 15, 2011
Oct. 18, 2011
Oct. 19, 2011
Feb. 1, 2012
Feb. 2, 2012
Aug. 31, 2012
Sep. 3, 2012
Sep. 5, 2012
Oct. 3, 2012
Oct. 27, 2012
Oct. 28, 2012
Nov. 1, 2012
Br11 16811 Å
1.10
Normalized flux
34
1.05
1.00
0.95
0.90
16760
16780
16800
16820
Wavelength [Å]
16840
16860
Fig. 22.— Close-up view of the variation in the Brackett 11
(16811 Å ) line in one of the Be stars discovered by APOGEE (HD
232940, with V = 9.45, H = 8.62).
targeted sources multi-epoch observations are obtained
(e.g., Fig. 18). These time series data lend themselves
to a number of useful applications, including the identification of binary stars that partly motivated this survey strategy (§2.8), and beyond. For instance, Figure 21
shows an example of a double lined spectroscopic binary
made evident through the multi-epoch spectroscopy). As
another example of the interesting phenomena that a detailed exploration of APOGEE’s time series spectral data
might uncover, Figure 22 shows the strong variation in
a Brackett series line for one of the numerous Be stars
discovered by APOGEE (Chojnowski et al. 2015) within
the sample of hot stars observed for telluric absorption
correction.
7.4.1. Time Series Spectral Data
7.4.2. Time Series Precision Velocity Data
A unique aspect of APOGEE in the context of Galactic archaeology surveys is that for a large fraction of the
The high precision of the APOGEE radial velocity
measurements, made possible by the tightly controlled
Majewski et al.
35
Fig. 23.— (Re-)discovery of the hot Jupiter around HD 114762
using eleven epochs of APOGEE data.
environmental conditions (gravity vector, temperature,
vacuum) of the APOGEE spectrograph combined with
the availability of multi-epoch visits, makes the survey
sensitive to not only stellar, but substellar mass companions. The opportunity for large-scale statistical explorations of brown dwarfs and even hot Jupiters is illustrated by Figure 23, which shows one of a number
of exoplanet candidates identified within the APOGEE
database. In this case, the star, HD 114762, has a known
11MJup companion in an 83.9 day period (Latham et
al. 1989). Before recognizing that this was a previously known exoplanet, the APOGEE team, through a
project to automatically fit multi-epoch APOGEE velocity data, determined this source to have a 14MJup
companion in a 77.9 day period. This result, based on
only 11 APOGEE spectra, is not far from that already
determined by Latham et al. The RMS of the best fit
to this relatively bright star system yields a residual of
only 33 m s−1 ; further refinements in the velocity measurement pipeline may bring similar RMS precisions to
fainter stars and perhaps enable further improvements in
velocity precision for brighter stars.
7.4.3. Radial Velocities Across the Galaxy
While not all stars in the APOGEE database will
have more than several radial velocity measurements (see
Fig.18), almost all of them will have mean radial velocities measured with an accuracy greater than is typically
found in previous large spectroscopic surveys of Milky
Way stars. Combined with its systematic probe of all
stellar populations, APOGEE’s radial velocity database
makes possible new large-scale explorations of Galactic
dynamics (e.g., see Fig. 24). Because APOGEE was designed to include extensive and deep probes of the low
latitude Milky Way, including the highly extinguished
regions of the disk and bulge, previously poorly studied regions of the Galaxy have now been opened up to
extensive kinematical canvassing. As a result, a number of Galactic dynamical phenomena have already been
discovered or reexamined with unprecedented statistical significance, including: the discovery of a kinematically extreme population in the bulge (Nidever et al.
2012), likely associated with the Galactic bar (Molloy
et al. 2015; Aumer & Schönrich 2015); a comprehensive
analysis of the dynamics of the bulge (Ness et al. 2015);
Fig. 24.— Star-by-star APOGEE heliocentric velocities as a function of Galactic X-Y position and projected on an artist’s conception image of the Milky Way. The points represent main APOGEE
survey stars (i.e., excluding those in the Kepler field) having projected |ZGC | < 2 kpc and log g ≥ 1. Only stars with no “bad”
flags in the database are used. (Background image credit, R. Hurt,
NASA/JPL-Caltech.)
a new derivation of the circular velocity of the Sun afforded by the more global APOGEE view of disk kinematics (Fig. 24; Bovy et al. 2012, 2015); and finally an
assessment of the power spectrum of non-axisymmetric
velocity perturbations of the disk, of which most can be
attributed to the action of the central bar (Bovy et al.
2015).
7.4.4. Metallicities Across the Galaxy
A primary goal of the APOGEE survey is to probe
the large-scale distribution of stellar chemical compositions across the Galaxy. Typical results are demonstrated by the [M/H] metallicity gradients shown in Figure 25, where the distribution of APOGEE targets projected onto the XY plane is displayed. Accurate measurements of gradients across a range of distances, both
Galactocentric and away from the Galactic plane, provide strong constraints on models for the formation of
the thick and thin Galactic disks (for further details on
the application of APOGEE to these problems, see Bovy
et al. 2014, Nidever et al. 2014, Hayden et al. 2014,2015,
Anders et al. 2014).
7.4.5. Multi-Element Abundance Variations
The power of APOGEE to deliver similar information
for multiple chemical elements and with great statistical power is illustrated by Figure 26, a representation
of the metallicity gradient in the Milky Way’s disk using APOGEE spectra directly. We stack the pseudonormalized spectra of red clump (RC) stars in the DR12
APOGEE-RC catalog (Bovy et al. 2014) as a function
of Galactocentric radius in bins of width 0.1 kpc and
normalize by the stacked spectrum at the solar circle
(R = 8 kpc). Because the RC occupies a small region
36
APOGEE
These results pose important constraints on disk formation models, in particular calling for the presence of two
stellar populations with different chemical enrichment
histories in the outer Galactic disk. That APOGEE data
can provide such data across a large multi-dimensional
space of elements probing different nucleosynthetic pathways is further demonstrated by the results shown in
Figure 30 below.
7.4.7. Stellar Ages
Fig. 25.— Same as Fig. 24, but with points color-coded by
metallicities [M/H] and using stars with projected |ZGC | < 2 kpc.
(Background image credit, R. Hurt, NASA/JPL-Caltech.)
in (log g, Teff ), the Teff and log g values for RC stars
are all very similar, so that spectrum-to-spectrum differences in absorption line strengths are mostly due to
star-to-star elemental abundance variations. This figure
clearly demonstrates that metal absorption increases toward the center of the disk and decreases toward the outskirts for every element with transitions in the APOGEE
spectral region (except for vanadium, which has such a
weak line that absorption disappears almost completely
around the solar circle). A quantitative measurement of
the overall radial [M/H] gradient of the RC sample is
−0.09 ± 0.01 dex kpc−1 (Bovy et al. 2014).21
7.4.6. Analysis of Abundance Patterns
A central rationale for APOGEE’s sensitivity to the
abundances of multiple chemical elements is to access
the information contained in variations of chemical abundance patterns, particularly for patterns containing elements synthesized on different evolutionary timescales
within populations. The power of APOGEE to probe
Galactic chemical evolution in this way is demonstrated
by Figure 27, which shows the spatial distribution of
[Mg/Fe] with [Fe/H] in different spatial zones of the
Galactic disk. Figure 27 is a more elemental-specific example of the more general [α/Fe] distributions explored
in Anders et al. (2014), Nidever et al. (2014), and Hayden
et al. (2015), and shows that APOGEE not only confirms
the existence of a bimodal distribution of [α/Fe] at fixed
[Fe/H], but that this bimodality exists over a large extent
of the disk, including for previously uncharted Galactocentric distances for |Z| < 0.5 kpc (see Hayden et al.
2015 for an even more extensive coverage of the disk).
21 While some radii do not follow the overall radial trend (e.g.,
the stacked spectrum at R = 7 kpc is almost exactly that at the
solar circle), this is most likely due to first-ascent RGB contamination in the APOGEE-RC catalog.
Collaboration between APOGEE and the Kepler and
CoRoT asteroseismology teams has greatly increased the
power of APOGEE data to shed light on Galaxy formation scenarios. The combination of APOGEE chemical
compositions with asteroseismological data makes possible the determination of accurate masses, thus ages, for
a large sample of APOGEE field giants (Pinsonneault
et al. 2014). Demonstrations of the power of analyzing
combined detailed chemical compositions and ages from
these APOGEE subsamples are starting to emerge (e.g.,
Epstein et al. 2014; Martig et al. 2015; Chiappini et al.
2015); one example is demonstrated in Figure 28, which
shows the unexpected discovery (Martig et al. 2015) of a
small sample of stars with high [α/Fe] and relatively low
ages (∼ 3–5 Gyr). The mere existence of intermediateage stars with such high [α/Fe] constitutes a serious challenge to existing chemical evolution models and is, as yet,
not fully explained.
7.4.8. Open Clusters
The APOGEE database has made the targeting of star
clusters a priority, not only because they provide reliable
abundance calibration references, but because they are
extremely useful stellar population components of great
interest in their own right. Open clusters, in particular, provide a powerful, independent means by which to
explore the chemistry, kinematics and extinction of the
Galactic disk because clusters distances can be precisely
gauged via isochrone fitting, made all the more accurate when the variable of metallicity can be removed using spectroscopic metallicities. An important output of
such isochrone fits are cluster ages, which provide critical
and confident timestamps for benchmarking evolutionary studies across the disk. Fortunately, apart from a set
of key open clusters that drove specific plugplate pointings, the vast majority of the large APOGEE sample
of over 150 sampled open clusters was a natural product of its extensive canvassing of the Galactic plane, and
only required attention to the allocation of fibers on already overlapping plugplates from the APOGEE grid to
known or suspected members of these clusters. As part of
the Open Cluster Chemical Analysis and Mapping (OCCAM) survey (Frinchaboy et al. 2013), these data have
already been used to study disk metallicity gradients and
abundance patterns as a function of both space and time;
an example of the opportunities opened up with this kind
of analysis is demonstrated in Figure 29.
7.4.9. Globular Clusters
Unlike the open clusters, where spectra of large samples of cluster members are easily generated as part of
APOGEE’s systematic coverage of the Galactic disk,
considerable investment of effort, planning and survey
Al(λ 16723)
Fe(λ 16697)
Si(λ 16685)
Ni(λ 16678)
Na(λ 16393)
Na(λ 16378)
CO(λ 16193)
12
Fe(λ 16169)
Ca(λ 16155)
Fe(λ 16157)
Ca(λ 16159)
Ca(λ 16161)
CO(λ 16126)
37
13
Mg(λ 15958)
V(λ 15929)
Ti(λ 15703)
Cr(λ 15684)
OH(λ 15509)
Fe(λ 15494)
S(λ 15482)
CN(λ 15486)
Mn(λ 15221)
Fe(λ 15211)
Fe(λ 15198)
13
K(λ 15172)
Majewski et al.
12
R (kpc)
11
10
9
8
7
6
0.005
0.006
0.014
0.020
0.027
!
"
log λ/15, 000Å
0.032
0.033
0.038
0.047
Fig. 26.— Direct spectral representation of the Milky Way disk’s metallicity gradient. This figure displays median-stacked normalized
APOGEE spectra of RC stars in bins of Galactocentric radius of width 0.1 kpc and normalized by the spectrum at the solar circle (assumed
to be R = 8 kpc, indicated by the dashed line). Redder/bluer colors represent more/less absorption compared to that at the solar circle.
Only about 1/6th of the full APOGEE spectral range is shown here, primarily regions containing strong, clean lines and representing all of
the elements whose abundances can be determined from APOGEE spectra. Some of the interesting lines of neutral-atomic and molecular
species as well as their central wavelengths are labeled at the top. Because RC stars span a narrow range of stellar parameters, absorption
line strengths translate into rough elemental abundances directly. All elements display a clear abundance gradient from the inner to the
outer disk. The only exception perhaps is vanadium, which is based on a very weak line at 15929Å that becomes vanishingly weak beyond
the solar circle.
Fig. 28.— Chemical abundance patterns informed by individual
stellar ages, derived from the combination of Kepler asteroseimology and APOGEE chemistry (see Martig et al. 2015).
Fig. 27.— Spatial variation of [Mg/Fe] vs. [Fe/H] distributions
across the Galactic disk, from Nidever et al. (2014)
time was needed to ensure ample attention to globular clusters. Many globular clusters are distant, which
makes even their giant stars relatively faint and means
that the stars are densely packed on the sky relative to
the fiber-to-fiber collision radius (see §2). These two facts
typically necessitated many visits to specialized fields
(see Fig. 10), with multiple plugplate designs to accumulate long integrations on fainter targets and to overcome the collision radius limitations to generate reasonable sample sizes. Nevertheless, the globular cluster visits were deemed high priority both because the member
stars are extremely useful calibration targets and because
the chemistry of globular clusters is extremely interesting and relevant to understanding the evolution of Milky
Way stellar populations.
The study of globular cluster formation is undergoing a revolution since the discovery of the commonality
of multiple populations in these systems (e.g., Piotto et
al. 2007; Milone et al. 2008; Gratton, Carretta & Bragaglia 2012), which has been established predominantly
on the basis of observations collected from the Southern Hemisphere. APOGEE is already making important contributions to the study of globular cluster evolution through the determination of accurate multi-element
abundances of relatively large samples of northern cluster members. This is illustrated by Figure 30, which,
for the majority of the ten clusters shown (eight that
were never previously studied in this way), gives evidence
for strong internal Al variations — in some cases anticorrelated with Mg — a feature commonly interpreted
as the signature of the presence of multiple stellar populations. Interestingly, APOGEE has shown that some
globular clusters (M3, M53) display apparently discrete
Al-Mg distributions (i.e., Al-rich and Al-poor groups),
38
APOGEE
guments (Tremaine et al. 1975) — through the identification of chemical signatures typical of globular cluster
stars in an APOGEE sample of field bulge stars (Schiavon et al. 2015).
Fig. 29.— APOGEE-derived metallicity and [α/Fe] (using the
reliable individual DR12 elements O+Ca+Si as α) as a function
of Galactocentric radius for 29 clusters in two age regimes: Blue
(orange) points denote clusters younger (older) than 1 Gyr. The
light grey region in the upper panel shows a gradient in [Fe/H]
of −0.06 dex kpc−1 within RGC = 15 kpc and hints of a flat
distribution (admittedly based on very few data points) beyond
with a spread of 0.2 dex. A shallower, positive gradient, of +0.007
kpc−1 dex, is seen in [α/Fe] (lower panel) consistent with previous literature-based open cluster studies (e.g., Yong et al. 2012).
Additional individual element abundance gradients are explored in
Frinchaboy et al. (2015).
Fig. 31.— Distribution of extinction across the Galactic plane, as
measured by comparing star-by-star photometric and spectroscopic
data to derive AKs , according to the isochrone matching method
discussed in Schultheis et al. (2014). Point positions correspond
to the projected locations of the stars against which the extinction
was measured. Only stars from DR12 with |ZGC | < 500 pc are
shown. Note the substantial increase in extinction at the spiral
arm towards the outer disk and also towards the Galactic center.
(Background image credit, R. Hurt, NASA/JPL-Caltech.)
7.4.10. Mapping Interstellar Extinction
Fig. 30.— Abundance anti-correlations as seen for 428 giant star
members in ten Northern Hemisphere globular clusters. Figure
from Mészáros et al. (2015).
while others (e.g., M13, M5) may show a more continuous distribution in the Al abundances (Mészáros et al.
2015). APOGEE data have also been used to test previous claims of the presence of multiple populations in
the well known, unusually large, old but super-metal-rich
open cluster, NGC 6791 (Cunha et al. 2015).
A distinct advantage afforded by these APOGEE studies of globular clusters is that the inferred chemistry of
the member stars may be directly compared to those of
the numerous field stars surveyed in precisely the same
way. An example of the power of such comparisons,
with interesting implications for our understanding of
both globular cluster and galaxy bulge formation, is the
discovery of remnants of globular cluster destruction in
the Galactic bulge — long-predicted by theoretical ar-
The combination of APOGEE spectroscopy with available photometric databases, such as those provided by
2MASS or the Spitzer IRAC surveys, increases the
scope of approachable astrophysical problems. For example, comparison of spectroscopically-derived atmospheric properties with measured broadband colors can
give key, star-by-star measurements of the distribution
of interstellar reddening, and therefore a powerful means
by which to explore the three-dimensional distribution
of dust. For example, Figure 31 shows the distribution of derived Ks band extinction across the Galactic
plane, with the extinctions derived by comparing observed colors to the intrinsic colors expected from theoretical isochrones matched to the ASPCAP-derived values of Tef f , log g, and [M/H] (see Schultheis et al. 2014),
and distances derived from comparing apparent and associated isochrone absolute magnitudes. Apart from showing global trends in dust properties, studies such as this
can help calibrate or highlight inadequacies in extinction
maps derived by other means.
7.4.11. Mapping Diffuse Interstellar Bands
The accuracy with which the APOGEE spectral library matches the intrinsic spectra of typical giant stars
Majewski et al.
39
Fig. 32.— (Left) Comparison of synthetic (red lines) to observed
APOGEE spectra (black lines) for two late type giant stars (panels a and b) and a hotter star (panel c) star. (Right) The difference spectra (blue points) clearly show the DIB feature at 15,272
Å , which can be fit with simple Gaussians (black lines) to measure column density and varying Doppler velocities (Zasowski et
al. 2015).
makes possible further explorations of interesting astrophysics that is unrelated to the stars themselves. For instance, disagreements between spectral synthesis and observations may reveal the presence of absorbers/emitters
along the line of sight. A compelling example of this
is shown in Fig. 32, where the difference between an
observed spectrum and its best-matching synthetic template reveals the presence of a relatively strong diffuse interstellar band (DIB, Herbig 1995). Using the fact that
this same DIB feature is seen in the vast majority of
APOGEE spectra along disk and bulge sightlines, Zasowski et al. (2015) for the first time thoroughly mapped
the spatial distribution and velocity field of a DIB carrier
over many kpc of the Galactic disk. Comparison of these
properties to those of other components of the ISM (dust,
atomic and molecular gas) will yield critical evidence towards the identification of the carriers of these mysterious spectroscopic signatures — a longstanding puzzle in
Galactic astrophysics.
8. DATA PRODUCTS, DISTRIBUTION AND
DOCUMENTATION
8.1. Data Products
There are two main sets of APOGEE data products.
The first consists of wavelength–calibrated, normalized,
1-D restframe spectra, and the radial velocities resulting from analysis of visit spectra. These data products
are the outputs of the data reduction and radial velocity
pipelines, described in §§6.1-6.4. The second set of data
products are the stellar parameters (Teff , log g, [M/H])
and the abundances of up to 15 elements, namely: C, N,
O, Na, Mg, Al, Si, S, K, Ca, Ti, V, Fe, Mn, and Ni. The
latter set results from application of ASPCAP (§6.5) to
the spectra. To ensure that the high level data can be reproduced by the community, ingredients that are crucial
for the stellar parameter and elemental abundance determinations — such as the adopted model atmospheres
(Mészáros et al. 2012), line list (Shetrone et al. 2015),
and grids of synthetic spectra (Zamora et al. 2015) —
are also available.
8.2. Data Releases
The data products from the three year APOGEE
survey have been included in two SDSS-III data re-
Fig. 33.— Same as Fig. 31, but for the distribution of measured
λ 15,272 Å DIB strength (Zasowski et al. 2015). As with Fig.
31, the plotted points correspond to the projected locations of the
stars against which the DIB absorption was observed. Only stars
from DR12 having |ZGC | < 500 pc are shown. (Background image
credit, R. Hurt, NASA/JPL-Caltech.)
leases (DRs). In DR10 (announced July 2013), calibrated APOGEE spectra, radial velocities, stellar parameters, and a limited set of elemental abundances
were made available for 57,454 stars (Ahn et al. 2014).
The second public data release, DR12 (made available
in January 2015) includes the data described in § 8.1
for all ∼136, 000 primary science targets in the SDSSIII/APOGEE sample (Alam et al. 2015; Holtzman et al.
2015). In addition, radial velocities for the ∼ 17,000 hot
stars22 used as telluric standards, as well as spectra and
derived parameters for approximately 900 bright stars
observed using the APOGEE feed from the NMSU 1-m
telescope (§5.3.3) are also included in DR12.
8.3. Data Access
APOGEE data can be accessed in a number of different ways through the www.sdss.org website. The
SDSS-III Catalog Archive Server (CAS) contains the high
level APOGEE data products, namely radial velocities,
radial velocity dispersion, Teff , log g, [M/H], and elemental abundances, as well as information relevant to
target selection, including, e.g., coordinates, 2MASS,
WISE, Spitzer/IRAC, and Washington+DDO51 photometry, proper motions, where available, and assumed
interstellar extinction. These data can be accessed via
low level SQL scripts, which allow users to select subsamples according to suitable criteria in the CasJobs link.
Lower level data products, including the visit and combined spectra of sources searchable via standard, basic or
advanced online forms, can be accessed with the Science
22 This number includes both survey-quality and commissioning
data.
40
APOGEE
Archive Server (SAS). The SAS also provides access to
the directory tree containing the full data set (for expert
users), as well as a summary FITS file containing all of
the catalog information listed above. The SkyServer offers another way of interfacing with the data, through
a “quick look” tool that makes possible non-interactive
visualization of images and spectra of sources that are
searchable by position and ID, via an online form. Finally, both the SAS and SkyServer link to a web tool
that allows for a quick assessment of data and model
quality, through interactive viewing of the (final) observed spectra overplotted with the best fit-fitting synthetic spectrum.
8.4. Documentation
The primary source of in depth information for users
of APOGEE data are the technical papers listed in Table 1. Additional, often less detailed, information can
be obtained in the webpages associated with each data
release. Users of APOGEE are strongly urged to peruse
those webpages or technical papers for a complete understanding of the data quality, limitations and caveats that
are specific to each data release (Ahn et al. 2014; Holtzman et al. 2015). Users of high level data products should
pay particular attention to the documentation pertaining to ASPCAP (Mészáros et al. 2012; Mészáros et al.
2013; Smith et al. 2013; Zamora et al. 2015; Garcı́a Pérez
et al. 2015; Shetrone et al. 2015), where the limitations
and known systematics in the APOGEE stellar parameters and elemental abundances are described. On the
other hand, users of APOGEE spectra will be interested
on information about possible systematics induced by instrument features and reduction procedures (Wilson et
al. 2015; Nidever et al. 2015). A good grasp of the possible biases introduced by APOGEE’s target selection
procedure and field placement strategy (Zasowski et al.
2013) is encouraged for those exploring global properties,
trends and correlations for stars across the Galaxy, and
for those comparing these empirical results to chemical
and/or chemo-dynamical models of stellar populations.
9. FUTURE EFFORTS
9.1. Software Development and Future Data Releases
As part of APOGEE-2 (§9.2), we will continue to develop our data reduction and analysis capability. In
addition, it is expected that our understanding of the
atomic and molecular physics related to the relevant
line transitions will continue to evolve. Therefore, to
maintain consistency across the integrated APOGEE1 and APOGEE-2 efforts, future APOGEE data releases will be expected to include updated analyses of
the APOGEE-1 data.
9.2. APOGEE-2 in SDSS-IV
In 2012 the APOGEE team proposed to continue with
an APOGEE-2 program in Sloan Digital Sky Survey IV.
Beyond an immediate continuation of operations on the
Sloan Telescope, a key feature of this proposal is the extension of the APOGEE-2 program into the Southern
Hemisphere, in a collaboration with the Observatories
of the Carnegie Institution for Science (OCIS) to use
the du Pont 2.5-m telescope for this purpose. The du
Pont is very much a forerunner of the Sloan Telescope,
having been designed with a wide field-of-view (Babcock
1977) and with a heritage in fiber plugplate spectroscopy
(Shectman et al. 1996) that influenced the subsequent
design of the SDSS system. APOGEE-2 was approved
as an SDSS-IV program in 2012, and APOGEE-2 began observing at APO in September 2014. The current
instrument at APO is the same as that described in §3
with one important modification in that the blue detector
with“superpersistence” has been replaced with a cosmetically cleaner array free of this problem.
While initially it was unclear whether APOGEE-2
would operate with two spectrographs working on the
Sloan and du Pont Telescopes in parallel or whether the
existing spectrograph would be moved to the du Pont after an initial 2 year campaign on the Sloan Telescope, by
November 2014 complete funding was secured to build
a second APOGEE spectrograph to make parallel operations possible. With the active participation of OCIS,
Chilean universities, and other SDSS-IV collaborators,
the southern infrastructure development, including the
creation of a scaled-down version of the Sloan fiber plugplate system (cartridges, plugging station, mapping system, cartridge transport and loading system), is underway, as is the construction of an infrastructure “mock-up
and training facility” in a special laboratory at the University of La Serena, Chile. The second APOGEE spectrograph, which is planned as a near duplicate of the
original with only minor alterations (e.g., to include seismic mitigation and to incorporate various other “lessons
learned”), is currently planned to begin operations on
the du Pont in mid-2015.
When it is necessary to distinguish it from the
APOGEE-2 survey, the original “APOGEE” survey will
in the future be referred to as “APOGEE-1”. Henceforth the term “APOGEE” will generally be intended to
refer to the combined APOGEE-1 and APOGEE-2 surveys, which together will provide a fully comprehensive,
all-sky view of the Milky Way.
APPENDIX
A: H-BAND LINE MAPS FOR INDIVIDUAL MOLECULES AND ATOMS
Winnowing to the desired APOGEE wavelength range initially involved inspection of the Hinkle et al. (1995) atlas
of the infrared spectrum of Arcturus (§2.2), followed by use of synthetic spectra, to assess the potentially useful atomic
lines. Figure 34 shows a telluric absorption spectrum as well as examples of these element line maps that guided the
selection of the specific wavelengths within the H-band used for APOGEE.
B: CALCULATIONS USED IN ESTIMATES OF THE APOGEE S/N REQUIREMENTS
To quantify the survey S/N requirements we performed the following exercise: A series of H-band spectra for RGB
stars with Tef f = 4000 K and log g = 1, with [Fe/H] = −2, −1 were calculated. For each case, we computed two
Majewski et al.
41
Fig. 34.— Line maps of specific molecules and elements having lines/bands expressed in the H-band. Generally, these are C, N, O, Na,
Mg, Al, Si, S, K, Ca, Ti, V, Cr, Mn, Fe, Co and Ni. This is a useful subset of elements with which to probe most types of nucleosynthesis
and chemical evolutions. The vertical lines in each plot indicate the ranges of the strong telluric absorption features near 1.6µm.
42
APOGEE
spectra, one with scaled-solar metal abundances (F (0.0)), and a second one in which the abundance of a particular
element was increased by 0.1 dex (F (0.1)). From these, we derived the minimum S/N required as the inverse of
min |Fi (0.1)/Fi (0.0) − 1|, where the index i runs through all the pixels in the spectra. The elements modeled were:
C, N, O, Na, Mg, Al, Si, S, K, Ca, Ti, V, Mn, Fe, and Ni, and they are listed in Table 3 from the most to least
challenging, based on this analysis.
Table 3 summarizes some of these S/N per (0.15Å) pixel requirements for 0.1 dex abundance accuracies from the
spectral synthesis described for three resolutions spanning the nominal range originally considered for APOGEE,
R = 14990, 21414 and 29979 (as shown in Fig. 3, more resolutions than this were sampled), and for stars with [Fe/H]
= −2.0, −1.0 and 0.0. From these numbers, one can see that the S/N requirements are higher for lower resolution
and lower metallicity, which is associated with the difficulty of measuring variations in poorly resolved and weak lines,
respectively. According to this initial estimate, for the required abundance accuracy to be reached for all top priority
elements from §2.2, a S/N of at least ∼ 100-150/pixel must be achieved, depending on the spectral resolution of the
APOGEE spectrograph.
TABLE 3
Required S/N for detection of 0.1 dex abundance variations
R=
[Fe/H]=
Element
Na
S
V
K
Mn
Ni
Ca
Al
Si
N
Ti
Mg
Fe
C
O
15k
-2.0
21k
-2.0
30k
-2.0
15k
-1.0
21k
-1.0
30k
-1.0
15k
0.0
21k
0.0
30k
0.0
3648.0
1498.4
2089.3
697.5
260.0
142.0
126.1
66.2
51.0
199.7
154.1
46.4
57.1
56.2
34.5
2673.7
1067.2
1504.7
505.6
184.9
101.6
89.5
47.2
35.2
147.3
110.0
33.1
41.6
40.4
24.5
2050.3
802.0
1124.0
384.4
136.2
76.7
66.0
35.1
25.3
113.4
81.8
24.7
31.2
29.9
18.1
430.6
232.4
231.0
105.6
73.0
64.9
60.2
59.6
53.4
52.5
51.7
48.9
38.9
19.5
18.8
309.8
167.2
164.4
75.3
50.9
45.7
42.7
41.8
38.6
41.7
36.5
36.7
34.3
14.8
14.6
230.0
125.9
121.4
56.6
36.9
34.3
31.5
30.4
28.6
32.2
26.7
27.7
25.5
11.3
10.8
78.7
144.0
61.0
65.0
64.8
55.3
57.7
60.3
43.1
25.4
53.7
34.7
25.9
10.2
11.9
56.0
104.8
42.4
44.6
46.9
46.4
41.0
42.1
35.7
21.4
38.9
26.4
21.3
8.3
9.1
41.4
81.6
30.5
33.1
34.9
35.4
30.3
31.5
29.6
18.1
29.3
20.4
18.4
6.8
7.4
Priority
medium
medium
lower
medium
medium
top
top
top
top
top
medium
top
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top
The analysis above did not account for the throughput variations expected along the APOGEE spectra, nor did
it allow for the fact that for some elements, more lines can be used in the analysis than others. The latter effect is
particularly important, as more lines lead to a relative decrease in the S/N requirement. To include those effects in
the estimates, we defined the following metric, for each element X:
2
N X
Fi (0.1)
F C(X) =
− 1 × ti
(B1)
Fi (0.0)
i=1
where the expected spectrograph throughput,as estimated a priori by the hardware team and normalized to 1.600 µm,
is given by ti . These computations were performed for synthetic spectra simulated at the expected APOGEE sampling
and resolution, including their dependence on wavelength, as provided by the hardware team.
In most cases, one finds for the ratio spectrum that F (0.1)/F (0.0) < 1 due to strengthened line absorption, but
for some elements and some wavelengths we find F (0.1)/F (0.0) > 1 due to interactions between species through
molecular dissociation equilibrium (e.g., an increase in C when C/O < 1 will cause increased CO strengths but
reduced OH aborption).
To avoid lots of pixels with very small changes from influencing the result, a series of cutoffs were imposed, setting
the fractional changes to zero if they were smaller (in absolute value) than 0.005, 0.01, 0.015, 0.02, 0.025. Obviously
the larger of these numbers are fairly extreme; if one ignores all lines that change the flux by less than 2-2.5%, there
are some elements that become entirely lost. The estimated required S/N is then is given by
√
S/N = 1/ F C.
(B2)
Because the synthetic spectra are sampled at half the pixel size (i.e., as combined from two dithered
√ exposures), these
are S/N required in half the total observing time. This can be seen as the required S/N being 2 times larger. Note
this exercise only takes into consideration the impact of elemental abundance on line opacities. Therefore, the results
are somewhat inaccurate for those elements that affect continuum opacity, which is dominated by H− and therefore
fairly sensitive to the abundances of important electron donors.
Majewski et al.
43
The formal results are shown in Table 4. Note that S/N = ∞ means that the element is undoable, i.e., no signal
above the cutoff is detected, which means S/N → ∞. From these numbers, one can see that the overall required S/N
is much lower than those from Table 3. This is because the metric in Equation B1 is roughly proportional to the
number of pixels that are sensitive to a given abundance, whereas the metric used to generate the numbers in Table 3
is sensitive to the pixels contained in a few prominent features only. That explains why C, N, and O require such low
S/N , due to the many thousands of CN, CO, and OH transitions that overlap in the H-band.
Interestingly, the dependence of the required S/N on [Fe/H] is much stronger than in Table 3. This is probably due
to the combined effect of sensitivity per line getting lower and lines vanishing as one goes towards lower metallicity.
The latter effect does not affect the numbers in Table 3, which are only based on the single most sensitive feature.
Even though these exercises provided a first assessment of the S/N needs of the survey, the conclusions from these
tests were relatively limited, since important effects such as line blending and limitations of models to reproduce real
spectra were not considered. Another factor ignored in this analysis was the availability of continuum points either for
equivalent width measurements or to guide the comparison with synthetic spectra. At lower temperatures and higher
metallicity, continuum points are expected to be fewer, posing a stronger requirement on the minimum S/N needed to
determine the continuum accurately. These issues make it very difficult for one to make definitive a priori estimates
of both the overall sensitivity of the spectra to abundance variations and the effects of line blending.
A primary conclusion obtained from this early analysis was that a precise estimate of the S/N mandated by the
abundance accuracy requirement of the APOGEE survey depends on variables whose effects could not be simulated
accurately enough at the time. However, it is clear that the ideal S/N is nicely bracketed by the two extremes resulting
from the exercises above, being most likely closer to the numbers in Table 3, given that the metric in Equation B1
tends to strongly overestimate the contribution by lines that are either too weak or cannot be resolved. Therefore,
prudence dictated a conservative approach in this case, and therefore we stipulated a minimum S/N requirement of
100/pixel, which is closer to the numbers provided in Table 3 and was expected to meet the abundance accuracy
requirements for at least all of the top priority elements.
TABLE 4
Required S/N for detection of 0.1 dex abundance variations
cutoff at 0.005
S/N/pixel
[F e/H]
Na
S
V
K
Mn
Ni
Ca
Al
Si
N
Ti
Mg
Fe
C
O
cutoff at 0.02
S/N/pixel
[F e/H]
Na
S
V
K
Mn
Ni
Ca
Al
Si
N
Ti
Mg
Fe
C
O
0
28.8
42.8
15.0
28.1
12.1
7.3
9.8
16.0
2.6
1.6
8.0
1.3
1.2
0.6
1.2
-1
∞
120.9
94.8
53.7
25.2
13.7
12.3
16.5
5.8
4.4
13.3
1.9
1.9
1.7
2.0
-2
∞
∞
∞
∞
276.9
58.4
38.6
24.6
10.8
96.5
55.0
4.1
6.9
8.0
3.1
Priority
medium
medium
lower
medium
medium
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top
top
top
top
medium
top
top
top
top
0
63.0
∞
21.5
45.5
21.8
10.7
12.9
27.6
6.9
2.1
11.2
4.8
1.7
0.7
1.4
-1
∞
∞
∞
∞
∞
25.8
14.3
20.2
8.8
18.2
16.2
8.3
3.5
2.4
2.4
-2
∞
∞
∞
∞
∞
∞
∞
35.7
15.2
∞
∞
11.9
12.7
∞
3.9
Priority
medium
medium
lower
medium
medium
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44
APOGEE
C: SIMULATIONS OF THE SENSITIVITY OF CADENCED APOGEE OBSERVATIONS TO BINARY STARS
To assess the cadencing requirements to optimize the sensitivity of APOGEE observations to the presence of binary
stars a suite of simulations of APOGEE observations of parent distributions of stellar populations with binaries was
performed. Binary systems were generated for three representative lines of sight in the APOGEE survey, at Galactic
coordinates of (l, b) = (0, 2.5), (45, 2.5), and (90, 0) degrees. The distribution of primary masses were generated using
the Trilegal model (Girardi et al. 2005). For each line of sight, we generated 300 binary systems and ran 1000 simulated
APOGEE surveys to observe these stars for each tested cadence. The period distribution of the binaries were adopted
from Duquennoy & Mayor (1991, “DM91” hereafter), with a cut-off at log P (days) < 0. Note that the Griffin (1985)
data on red giants presented in DM91 generally follow this period distribution, but with a deficiency at low log P . This
difference suggests that the simulations may have slightly overestimated the fraction of short-period binaries. We did
compensate somewhat by discarding physically impossible binary systems with the simple constraint that the stellar
radius cannot exceed the orbital separation, but we did not consider more sophisticated schemes such as the plausibility
of systems that would be undergoing (or have undergone) tidal interactions. The eccentricity distribution also follows
that in DM91, which includes circularization of orbits having P < 100 days. (These simulations pre-dated the Duchêne
& Kraus 2013 finding that the DM91 eccentricity distribution is a much poorer fit to observed eccentricities of binaries
than a simple uniform distribution of eccentricities.) The adopted distribution of secondary mass ratios is uniform
between 0.1 and 1.0. The orientation of the binary orbital axis is isotropic, and the longitude of periastron is uniformly
distributed. The time of periastron passage was randomized uniformly between zero and the length of the period.
To simulate the observations, we defined a 3-visit observing cadence of O1 , O2 , and O3 days, where the time of the
first visit O1 is always 0 and O2 and O3 are the times of the second and third visit observation with respect to the
first observation. To avoid perfect integer spacing of the simulated observations, we added random offsets to O1 , O2 ,
and O3 . Once the observation times and orbital parameters were defined for each binary system, we used the IDL
code “helio rv” in the IDL Astronomy User’s Library to calculate the radial velocity at each observation date and
then added measurement noise drawn from a Gaussian distribution with σ of 0.5 km s−1 . We then calculated the
average RV of each binary system. Since we adopted 0 km s−1 for the systemic RV of each system, this average RV
corresponds to ∆RV , the difference between the measured and true velocity of the system. Binaries with large ∆RV
will have the largest detrimental effect on kinematical measurements of the stellar populaitons.
We explored cadences (O1 , O2 , O3 ) over a number of possible baselines spanning a week (0, 1, 7.1), one month (0,
7.2, 30.4), three months (0, 15.2, 91.2), and one year (0, 30.4, 365.25). The detectability of each binary is defined
as the maximum RV difference between the three RV observations. The simulations showed that in the case of three
observations, the longer the baseline, the greater number of binaries that are identified and, more importantly, the
fewer binaries with large ∆RV that are missed. However, the improvement in the number of large ∆RV identified
between one cadence and the next longest cadence diminished as the baseline increased. We also explored the effects
of the timing of the second observation in the one month cadence by varying O2 between 1 and 5 days and found that
there was little change in results.
Fig. 35.— Normalized cumulative distribution functions of the fraction of stars having a given APOGEE-measured radial velocity offset
from the true systemic radial velocity for the four different observing cadences described in the text. Note that these are shown only for the
binaries that are not flagged as a “likely binary” using the >4 km s−1 pairwise-difference criterion described in §2.8. In the lefthand plots
the normalizations are to the total number of binaries missed for that cadence. In the righthand plot, the distributions are normalized to
the total number of simulated binaries; thus, the difference in the maximum CDF-value between each cadence corresponds to the different
fractions of “missed binaries”.
Majewski et al.
45
D: SIMULATIONS OF THE EXPECTED GALACTIC DISTRIBUTION OF APOGEE TARGETS
Section 4.1.1 describes the main philosophy that was adopted for the APOGEE field targeting plan, while §4.1.2
describes the four major phases through which the targeting plan evolved to the final targeting configuration (§4.1.3).
This evolution was guided by application of Galactic stellar population models — namely the Trilegal (Girardi et al.
2005) and Besançon (Robin et al. 2003) Galaxy models — to multiple strawman field placement designs for each of
the three principal field star survey regions: disk (|b| ∼ 0◦ ), bulge (|l| < 20◦ and |b| < 20◦ ), and halo (|b| > 20◦ ).
This modeling was essential to the task of optimizing not only the specific locations of fields but also the cohort
distributions, and color and magnitude limits/numbers of visits employed. Some of the specific issues these modeling
efforts addressed were, e.g., ensuring that each Galactic component was well sampled across the greatest possible
distance ranges, that the stellar samples were optimized to target predominantly giant stars so as to make the largest
distances most accessible, and that each major Galactic population would be amply represented within APOGEE.
Examples of this work and earlier targeting plans are provided in this Appendix to demonstrate how some trade-offs
were evaluated, and how vestiges of earlier plans, modified later, can be found in the final APOGEE samples.
Figure 36 gives some idea of the evolution of the disk targeting strategy, and reflects several static as well as changing
considerations during the survey planning stages. Early field placement plans were constructed under the assumption
that 75% of APOGEE time would be conducted in conjunction with the MARVELS survey, with only 25% of the
time with APOGEE only. Because MARVELS sought a total of 30 approximately one hour visits to build its time
series database, the early APOGEE targeting plans had a greater fraction of long, many-visit fields, and was thus
expected to probe relatively fewer lines of sight, but with more depth per direction (e.g., “Disk Strawman 1” and
“Disk Strawman 2” in Figure 36). Because APOGEE was expected to start in the middle of the second of three 2-year
campaigns of MARVELS fields, deep APOGEE fields were designed around 10-hour fields, where APOGEE would
“join on” to fields already started by MARVELS, and 30-hour fields, which would be initiated for both APOGEE and
MARVELS simultaneously.23
Starting from the initial plan to very systematically and symmetrically survey the disk while coordinating coobserving with the MARVELS program (e.g., Disk Strawman 1 in Figure 36), the field plan for exploring the Galactic
disk was then rearranged in response to (a) greater expected observing time in winter due both to the longer nights
as well as how grey time was divided among SDSS-III surveys (resulting, e.g., in the larger coverage of the Galactic
anticenter seen in models Disk Strawman 2 and later plans); (b) the desire to increase the number of expected stars
from the Intermediate Population II “thick disk”, which was achieved not only by altering the latitude distribution of
fields but moving the location of the deep fields from the mid-plane to ever higher latitudes and tweaking the cohort
distributions (for example, Disk Strawman 3 increased the number of thick disk stars with |ZGC | > 1.5 kpc by a factor
of 20 and 4 compared to Disk Strawman 1 and 2, respectively, and these numbers were further increased by even
later models); and (c) the desire to include more calibration cluster fields (e.g., motivating Disk Strawman 5 and 6).
The eventual addition of many more 3-hour fields due to the MARVELS descope and further adjustments in response
to the addition of observations of the Kepler field are not reflected in the Figure 36 designs, but evident in the final
targeting plan (Fig. 10).
Figure 37 illustrates a similar evolution in the design of the bulge field coverage. Starting from simple rectilinear
distributions (e.g., “Bulge Strawman 1” and 2), the plans were altered to account for (a) changes in the disk observing
plan, because fields focused on observing the inner disk compete for the same observing windows as the bulge fields
(e.g., Bulge Strawman 2 and later); (b) the introduction of fields sampling the core of the Sgr dSph galaxy (Bulge
Strawman 2 and later); (c) optimization of the limited amount of bulge accessibility from APO to make sure that
APOGEE sufficiently explored key structural axes (major, minor, diagonal) of the bulge (e.g., Bulge Strawman 3 and
later); and (d) exploitation of APOGEE’s ability to probe highly obscured fields close to the Galactic center that are
less accessible to other spectroscopic surveys, which motivated a tighter concentration of the APOGEE fields around
the center (e.g., Bulge Strawman 5).
Less effort was invested in modeling halo targeting, because it was realized early on that a large fraction of the available relevant observing hours would already be needed to probe globular clusters and known halo streams. Eventually,
additional simple “picket fence” distributions of predominantly 3-hour fields were added to these deep halo fields along
b = 30◦ , 45◦ , 60◦ , and 75◦ (see Figure 10).
The final adopted field plan (Figure 10) descends, after several additional alterations, from the sum of the Disk
Strawman 6 and Bulge Strawman 5 plans, along with the Halo field strategy discussed above. Many of the ensuing
alterations in field positions were in response to further reductions in the MARVELS program, which allowed additional
APOGEE fields to be included. Further tweaking of field positions and cohort distributions came as a direct result of
further modeling to optimize the representation of halo and thick disk as well as the overall number of giant stars in
the survey (the latter aided by the inclusion of Washington+DDO51 photometry in high latitude fields). Figures 38a
and 38b illustrate some of the results of such modeling. Completing the final field distribution shown in Figure 10
was not only enabled, but further amended mid-survey, by the addition of both the twilight observing (§5.3.1) and
dark time observing (§5.3.2) campaigns, and modified further, of course, by prevailing weather conditions during the
APOGEE survey.
We note that throughout the modeling efforts conducted to shape the APOGEE targeting plan large variations
23 The 30-/10-hour plan was later modified to a 24-/12-hour
plan. In initial planning all “visits” would be kept to less than 60
minutes to limit the amount of MARVELS fringe broadening from
geocentric velocity changes over the exposure.
APOGEE
30
46
30
−30
−10
10
Disk Strawman 1
30
−30
−10
10
Disk Strawman 2
−10
−30
30
10
Disk Strawman 4
30
−30
−10
Galactic latitude
10
Disk Strawman 3
30
−30
−10
10
Disk Strawman 5
−30
−10
10
Disk Strawman 6
250
200
150
100
50
0
Galactic longitude
Bulge Strawman 2
Bulge Strawman 3
Bulge Strawman 4
Bulge Strawman 5
−10
0
10
Bulge Strawman 1
−20
Galactic latitude
20
Fig. 36.— Evolution of the design of the disk field distribution is seen from the earliest plan, “Disk Strawman 1”, to the nearly final disk
plan, “Disk Strawman 6”. Green circles represent 3-hour fields, red circles are 10- or 12-hour fields, and black circles are 24- or 30-hour
fields.
20
15
10
5
0
−5
−10
20
15
10
5
0
−5
−10
20
15
10
5
0
−5
−10
20
15
10
5
0
−5
−10
20
15
10
5
0
−5
−10
Galactic longitude
Fig. 37.— Various strategies tested for the sampling of the bulge, from the earliest notions (“Bulge Strawman 1”) to that eventually
adopted (“Bulge Strawman 6”).
in expected distributions were seen when comparing the results of the TRILEGAL and Besançon models. These
differences in results were, in part, due to differences in the adopted prescriptions for the thick disk structure as well
as the model for reddening between the codes. While tweaking of model parameters could bring the results of the two
models into better agreement and make more consistent projections, systematic discrepancies of one sort or another
Majewski et al.
47
in the results of the two modeling codes typically remained. Such discrepancies between the models illustrate the lack
of strong observational constraints, particularly in the Galactic mid-plane, that can be used to calibrate them — a
problem that can now be at least partly remedied by application of APOGEE results.
80
60
40
20
Thin
Thin disk
disk
Thick disk
Halo
Bulge
Galactic latitude
0
−20
−40
a
80
60
40
20
Giants
Giants
Dwarfs
0
−20
−40
b
360
330
300
270
240
210
180
150
120
90
60
30
0
Galactic longitude
Fig. 38.— Example of Trilegal modeling of the expected distributions of of different properties of sampled stars. Each field position
is represented by a pie chart describing target distributions of Galactic component membership (panel a) and evolutionary stage (panel
b). In both panels, symbol size is proportional to number of visits. (a) Expected distributions of stars from each of the major Galactic
stellar populations on a field-by-field basis. This same distribution is shown in an alternative, Cartesian format in Figures 11 and 12.
According to the simulation, thin disk (bulge) fields would be expected to be dominated by thin disk (bulge) stars. However, in halo fields
the modeled contribution by thin disk stars is overestimated in this particular simulation, for several reasons. Many halo fields were placed
on known overdensities (which increases the relative halo contribution) and globular clusters, where targets were selected to maximize
cluster membership; these strategies are not taken into account in the Trilegal simulation, which is based on a Galactic model composed of
symmetrical thin and thick disks, halo, and bulge. Moreover, the simulation does not include the effect of the additional halo field selection
criterion based on Washington+DDO photometry (§4.2.2), which was designed to minimize (predominantly disk) dwarf contamination,
and, indeed, was motivated by model results like that shown here. (b) Example of Trilegal modeling of the expected giant/dwarf ratio on a
field-by-field basis, for the final, approved APOGEE plan (after MARVELS descope). This model shows that the disk fields were expected
to be dominated by giants (as borne out by the survey); on the other hand, the modeled dwarf/giant ratio in halo fields is much higher
than found in the actual survey, because of the additional implemented strategies explained above.
E: LABORATORY ASTROPHYSICS EFFORTS AND DEVELOPMENT OF THE APOGEE LINE LIST
Physical data (line identifications, wavelengths, transition probabilities, excitation potentials, damping constants)
for line transitions in near infrared stellar spectra are not as mature as those for optical spectra. Because such
data are critical inputs to the ASPCAP processing of APOGEE spectra, a significant effort was put into canvassing
the literature and collating previously published data, whether theoretically derived or empirically measured in the
laboratory or through astrophysical observations. In addition, it was found necessary to collaborate with laboratory
atomic physicists (primarily at the University of Wisconsin and Imperial College, London) to supplement and improve
48
APOGEE
the line list database. To ensure consistency across these multi-sourced data and reduce their uncertainties, the
catalogs of data were used to generate synthetic spectra which were then compared to very high resolution, Fourier
Transform Spectroscopy data on well known stars: µ Leo, β And, δ Oph, Arcturus and the Sun (Smith et al. 2013;
Shetrone et al. 2015) to create improved “astrophysical” line lists. The result of this enterprise has been the creation
of catalogs of data for as many as 134,000 atomic and molecular features, meticulously checked against the cataloged
and APOGEE-observed (§5.3.3) spectra of the Sun and Arcturus (Shetrone et al. 2015).
We thank Whitney Richardson for help with Figure 13.
S.R.M. acknowledges support from National Science Foundation grant AST-1109178.
R.P.S. acknowledges support from Gemini Observatory, which is operated by the Association of Universities for
Research in Astronomy, Inc., on behalf of the international Gemini partnership of Argentina, Australia, Brazil, Canada,
Chile, and the United States of America.
D.A.G.H. and O.Z. acknowledge support provided by the Spanish Ministry of Economy and Competitiveness under
grant AYA-2011-27754. S.Mathur acknowledges support from the NASA grant NNX12AE17G.
Sz.M. has been supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.
Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the
National Science Foundation, and the U.S. Department of Energy Office of Science. The SDSS-III web site is
http://www.sdss3.org/.
SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III
Collaboration including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory,
University of Cambridge, Carnegie Mellon University, University of Florida, the French Participation Group, the
German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre
Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck
Institute for Astrophysics, New Mexico State University, New York University, Ohio State University, Pennsylvania
State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of
Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University.
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