Äîêóìåíò âçÿò èç êýøà ïîèñêîâîé ìàøèíû. Àäðåñ îðèãèíàëüíîãî äîêóìåíòà : http://www.atnf.csiro.au/people/Ray.Norris/papers/n253.pdf Äàòà èçìåíåíèÿ: Thu Jun 21 02:15:27 2012 Äàòà èíäåêñèðîâàíèÿ: Fri Feb 28 08:02:01 2014 Êîäèðîâêà: Ïîèñêîâûå ñëîâà: zodiacal light

A Preprint typ eset using L TEX style emulateap j v. 08/22/09

THE SPITZER EXTRAGALACTIC REPRESENTATIVE VOLUME SURVEY (SERVS): SURVEY DEFINITION AND GOALS
J.-C. Mauduit1 , M. Lacy2 , D. Farrah3 , J.A. Surace1 , M. Jarvis4 , S. Oliver3 , C. Maraston5 , M. Vaccari6,7 , L. ´ Marchetti6 , G. Zeimann8 , E.A. Gonzales-Solares9 , J. Pforr5,10 , A.O. Petric1 , B. Henriques2 , P.A. Thomas2 , J. Afonso11,12 , A. Rettura13 , G. Wilson13 , J.T. Falder4 , J.E. Geach14 , M. Huynh15 , R.P. Norris16 , N. Seymour16 , G.T. Richards17 , S.A. Stanford8,18 , D.M. Alexander19 , R.H. Becker8,18 , P.N. Best20 , L. Bizzocchi11,12 , D. Bonfield4 , N. Castro21 , A. Cava21 , S. Chapman9 , N. Christopher22 , D.L. Clements23 , G. Covone,24 , N. Dubois3 , J.S. Dunlop20 , E. Dyke4 , A. Edge25 , H.C. Ferguson26 , S. Foucaud27 , A. Franceschini6 , R.R. Gal28 , J.K. Grant29 , M. Grossi11,12 , E. Hatziminaoglou30 , S. Hickey4 , J.A. Hodge31 , J.-S. Huang31 , R.J. Ivison20 , M. Kim1 , O. LeFevre32 , M. Lehnert33 , C.J. Lonsdale1 , L.M. Lubin8 , R.J. McLure20 , H. Messias11,12 , A. Mart´ z-Sansigre5,22 , ine A.M.J. Mortier20 , D.M. Nielsen34 , M. Ouchi35 , G. Parish4 , I. Perez-Fournon21 , M. Pierre36 , S. Rawlings22 , A. Readhead37 , S.E. Ridgway38 , D. Rigopoulou22 , A.K. Romer2 , I.G. Rosebloom2 , H.J.A. Rottgering39 , M. Rowan-Robinson23 , A. Sajina40 , C.J. Simpson41 , I. Smail25 , G.K. Squires1 , J.A. Stevens4 , R. Taylor29 , M. Trichas23 , T. Urrutia42 , E. van Kampen29 , A. Verma22 , C.K. Xu1

arXiv:1206.4060v1 [astro-ph.CO] 18 Jun 2012

ABSTRACT We present the Spitzer Extragalactic Representative Volume Survey (SERVS), an 18 deg2 mediumdeep survey at 3.6 and 4.5 µm with the post-cryogenic Spitzer Space Telescope to 2 µJy (AB = 23.1) depth of five highly observed astronomical fields (ELAIS-N1, ELAIS-S1, Lockman Hole, Chandra Deep Field South and XMM-LSS). SERVS is designed to enable the study of galaxy evolution as a function of environment from z 5 to the present day, and is the first extragalactic survey both large > enough and deep enough to put rare ob jects such as luminous quasars and galaxy clusters at z 1 into their cosmological context. SERVS is designed to overlap with several key surveys at optical, nearthrough far-infrared, submillimeter and radio wavelengths to provide an unprecedented view of the formation and evolution of massive galaxies. In this paper, we discuss the SERVS survey design, the data processing flow from image reduction and mosaicing to catalogs, as well as coverage of ancillary data from other surveys in the SERVS fields. We also highlight a variety of early science results from the survey. Subject headings: Astrophysical data, surveys

1 Infrared Pro cessing and Analysis Center/Spitzer Science Center, California Institute of Technology, Mail Code 220-6, Pasadena, CA 91125, USA 2 National Radio Astronomy Observatory, 520 Edgemont Road, Charlottesville, VA 22903, USA 3 Department of Physics and Astronomy, University of Sussex, Falmer, Brighton, BN1 9QH, UK 4 Center for Astrophysics Research, University of Hertfordshire, Hatfield, AL10 9AB, UK 5 Institute of Cosmology and Gravitation, University of Portsmouth, Dennis Sciama Building, Burnaby Road, Portsmouth, PO1 3FX, UK 6 Department of Astronomy, vic. Osservatorio 3, 35122, Padova, Italy 7 Astrophysics Group, Physics Department, University of the Western Cap e, Private Bag X17, 7535, Bellville, Cape Town, South Africa 8 Department of Physics, University of California, One Shields Ave., Davis, CA95616, USA 9 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK 10 National Optical Astronomy Observatory (NOAO), 950 North Cherry Avenue, Tuscon, AZ 85719, USA 11 Observat´rio Astron´ o omico de Lisb oa, Faculdade de Ci^ encias, Universidade de Lisboa, Tapada da Ajuda, 1349-018 Lisbon, Portugal 12 Centro de Astronomia e Astrof´sica da Universidade de Lisi boa, Tapada da Ajuda, 1349-018 Lisbon, Portugal 13 Department of Physics and Astronomy, University of California-Riverside, 900 University Ave., Riverside, CA 92521, USA 14 Department of Physics, McGill University, Ernest Rutherford Building, 3600 rue University, Montr´ Qu´ ec H3A 2T8, Canada eal, eb 15 International Centre for Radio Astronomy Research, University of Western Australia, M468, 35 Stirling Hwy, Crawley WA 6009, Australia

16 CSIRO Astronomy & Space Science, PO Box 76, Epping, NSW, 1710, Australia 17 Department of Physics, Drexel University, 3141 Chesnut Street, Philadelphia, PA 19014, USA 18 IGPP, Lawrence Livermore National Lab oratory, 7000 East Ave., Livermore, CA94550, USA 19 Department of Physics, University of Durham, South Road, Durham, DH1 3LE, UK 20 Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, UK 21 Institutio de Astrof´sica de Canarias, C/V´a L´ i i actea s/n, 38200, La Laguna, Tenerife, Spain 22 Oxford Astrophysics, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK 23 Astrophysics Group, Blackett Lab oratory, Imp erial College, Prince Consort Road, London, SW7 2BW, UK 24 Dipartimento di Scienze Fisiche, Universit` Federico I I and Isa tituto Nazionale di Fisica Nucleare, Sez. di Napoli, Complesso Universitario di Monte S. Angelo, Via Cintia, Ed. 6, I-80126 Nap oli, Italy 25 Institute for Computational Cosmology, Durham University, South Road, Durham, DH1 3LE, UK 26 Space Telescop e Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA 27 Scho ol of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK 28 Institute for Astronomy, University of Hawaii, 2680 Wo o dlawn Drive, Honolulu, HI 96822, USA 29 Institute for Space Imaging Science, University of Calgary, AB T2N 1N4, Canada 30 Europ ean Southern Observatory, Karl-Schwartzschild-Str. 2, 85748, Garching, Germany 31 Max-Planck Institute for Astronomy, Konigstuhl 17, 69177, Heidelberg, Germany 32 Lab oratoire d'Astrophysique de Marseille, Traverse du Siphon, B.P.8, 13376 Marseille Cedex 12, France


2
1. INTRODUCTION

Mauduit et al. South (CDFS) and XMM-large-scale structure (XMMLSS). The five SERVS fields are centered on or close to those of corresponding fields surveyed by the shallower Spitzer Wide-area Infrared Extragalactic Survey (SWIRE; Lonsdale et al. 2003), and overlap with several other ma jor surveys covering wavelengths from the X-ray to the radio. Of particular importance is nearinfrared data, as these allow accurate photometric redshifts to be obtained for high redshifts (van Dokkum et al. 2006; Brammer et al. 2008; Ilbert et al. 2009; Cardamone et al. 2010): SERVS overlaps exactly with the 12 deg2 of the VISTA Deep Extragalactic Observations (VISTA VIDEO, Jarvis et al. 2012, in prep.) survey (Z, Y , J, H & Ks bands) in the South, and is covered by the UKIRT Infrared Deep Sky Survey (UKIDSS DXS, Lawrence et al. 2007) survey (J , K ) in the North. SERVS also has good overlap with the Herschel Multi-tiered Extragalactic Survey (HerMES, Oliver et al. 2012) in the far-infrared, which covers the SWIRE and other Spitzer survey fields, with deeper subfields within many of the SERVS fields. Sampling a volume of 0.8Gpc3 from redshifts 1 to 5, the survey is large enough to contain significant numbers of rare ob jects, such as luminous quasars, Ultraluminous Infrared Galaxies (ULIRGs), radio galaxies and galaxy clusters, while still being deep enough to find L galaxies out to z 5 (see for example Falder et al. 2011, as well as Capak et al. 2011 who find two galaxies in the z = 5.3 cluster bright enough to be detected by SERVS at 4.5µm.) For comparison, the largest structures seen in the Millennium simulation at z 1 are of the order of 100 Mpc (Springel et al. 2005), which subtends 3 degrees at that redshift, so each SERVS field samples a wide range of environments. By combining the five different fields of SERVS, the survey effectively averages over large-scale structure, and presents a representative picture of the average properties of galaxies in the high redshift Universe. Spitzer observations of the five SERVS fields are presented in detail in Section 2. Image processing is detailed in Section 3, focusing on the mosaicing process and uniformity of coverage. Section 4 presents the extracted SERVS catalogs, as well as an assessment of overall data quality, detection limits and expected number counts. Section 5 gives an overview of the ancillary data available at different wavelength in the five fields. Preliminary science results and science goals are described in Section 6. A summary of the SERVS data at hand is provided in Section 7.
2. SPITZER OBSERVATIONS 2.1. Selection of fields

Progress in extragalactic astronomy has been greatly enhanced by deep surveys such as the Great Observatories Origins Deep Survey (GOODS, Dickinson et al. 2003), the Cosmic Evolution Survey (COSMOS, Sanders et al. 2007), the Galaxy Mass Assembly ultradeep Spectroscopic Survey (GMASS, Cimatti et al. 2008), the HST Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS, Grogin et al. 2011), that have allowed us to study the evolution of galaxies from the earliest cosmic epochs. However, a limitation of such surveys is the relatively small volumes probed, even at high redshifts: for example, Ilbert et al. (2006) find noticeable field-to-field variations in redshift distributions in the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS1 ) in fields of 0.7 - 0.9deg2 . Until lately, the combination of depth and area required to map a large volume ( 1Gpc3 ) of the high redshift Universe at near-infrared wavelengths, where the redshifted emission from stars in distant galaxies peaks, has been prohibitively expensive in telescope time. Two recent developments have now made this regime accessible. On the ground, the availability of wide-field nearinfrared cameras has greatly improved the effectiveness of ground-based near-infrared surveys in the 1 - 2.5 µm wavelength range. In space, the exhaustion of the cryogenic coolant of the Spitzer Space Telescope opened up an opportunity to pursue large near-IR surveys using the two shortest wavelength channels (IRAC1 [3.6] and IRAC2 [4.5]) of the Infrared Array Camera (IRAC, Fazio et al. 2004) in the post-cryogenic or "warm" mission that were much larger than feasible during the cryogenic mission. The Spitzer Extragalactic Representative Volume Survey (SERVS), a Spitzer "Exploration Science" program, stems from these two developments. SERVS is designed to open up a medium-depth, medium-area part of parameter space in the near-infrared (see Figure 1), covering 18 deg2 to 2 µJy in the Spitzer [3.6] and [4.5] bands. These observations required 1400hr of telescope time and covered five highly observed astronomical fields: ELAIS-N1 (hereafter EN1), ELAIS-S1 (ES1), Lockman Hole (Lockman), Chandra Deep Field
33 Lab oratoire d'Etudes des Galaxies, Etoiles, Physique et Instrumentation GEPI, UMR8111, Observatoire de Paris, Meudon, 92195, France 34 Astronomy Department, University of Wisconsin, Madison, 475 North Charter Street, Madison, WI 53711, USA 35 Observatories of the Carnegie Institute of Washington, 813 Santa Barbara Street, Pasadena, CA 91101, USA 36 CEA, Saclay, F-91191 Gif-sur-Yvette, France 37 Astronomy Department, California Institute of Technology, Mail Code 247-17, 1200 East California Boulevard, Pasadena, CA 91125, USA 38 Cerro Tololo Interamerican Observatory, Colina El Pino s/n, Casilla 603, La Serena, Chile 39 Leiden Observatory, Leiden University, Oort Geb ouw, PO Box 9513, 2300 RA Leiden, The Netherlands 40 Department of Physics and Astronomy, Haverford College, Haverford, PA, 19041, USA 41 Astrophysics Research Institute, Liverp o ol John Mo ores University, Twelve Quays House, Egerton Wharf, Birkenhead CH41 1LD 42 Leibniz Institute for Astrophysics, An der Sternwarte 16, 14482 Potsdam, Germany 1 www.cfht.hawaii.edu/Science/CFHTLS

SERVS consists of five fields located near the centers of corresponding SWIRE fields: EN1, ES1, Lockman, XMM-LSS and CDFS. The SWIRE fields are in regions with low infrared backgrounds (Lonsdale et al. 2003), making them ideal for follow-up at far-infrared wavelengths. The SERVS fields were selected to have good overlap with current and proposed surveys in other wavebands within the SWIRE fields (see Section 1), to cover both northern and southern hemispheres, and to have a range in Right Ascension allowing both flexible follow-up


SERVS: survey definition and goals

3

Fig. 1.-- Area versus depth for SERVS compared to other surveys at wavelengths of 3.6µm (left panel ) and 4.5µm (right panel ). For consistency, the depth shown is the 5 limiting flux for point sources, excluding confusion noise (pp as described in Section 4.2), calculated from the Spitzer performance estimation to ol (http://ssc.spitzer.caltech.edu/warmmission/propkit/pet/sensp et /index.html) in each case. The surveys are (from left to right): GOODS, the Spitzer follow-up to the CANDELS HST survey (Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey, Grogin et al. 2011), the Spitzer Extragalactic Deep Survey (SEDS, Program identifier - hereafter PID - 60022, 61040, 61041, 61042, 61043, P.I. G. Fazio), the Spitzer IRAC/MUSYC Public Legacy in E-CDFS (SIMPLE) survey (Spitzer, PID 20708), the Spitzer Ultra Deep Survey (SpUDS, PID 40021, P.I. J.S. Dunlop), S-COSMOS, the Spitzer Deep Wide-Field Survey (SDWFS, Ashby et al. 2009), the Spitzer-HETDEX Exploratory Large Area (SHELA, PID 80100, P.I. C. Papovich) Survey, SWIRE, the SPT-Spitzer Deep Field (SSDF, PID 80096, P.I. S. Stanford) and the Wide-Field Infrared Explorer (WISE, Wright et al. 2010).

TABLE 1 The geometry of the SERVS fields Field Name EN1 ES1 Lockman CDFS XMM-LSS


Single-band catalogs extend b eyond vertices.

Field Center RA, Dec (J2000) 16:10:00, +54:30 00:37:48, -44:00 10:49:12, +58:07 03:32:19, -28:06 02:20:00, -04:48

Field PA (deg) 350 0 328 0 0

Field Area (deg2 ) 2.0 3 4.0 4.5 4.5

Vertices of the area covered by b oth [3.6] & [4.5] (deg) (244.2,54.2) (243.1,55.4) (240.9,54.8) (241.7,53.6) (10.5,-44.9) (10.4,-42.9) (8.4,-43.0) (8.4,-45.1) (165.0,57.4) (161.7,59.8) (159.3,59.0) (162.7,56.4) (54.4,-27.1) (51.8,-27.0) (51.7,-28.9) (54.4,-28.9) (37.2,-5.4) (37.0,-3.9) (33.9,-4.1) (34.3,-5.7)

with ground-based telescopes and good scheduling opportunities for Spitzer. Field geometry and observation details are given in Table 1 & Table 2. The observed SERVS mosaics are shown in Figures 2 through 6, together with the coverage of significant overlapping surveys (see Section 5 for an exhaustive list of all ancillary data in and near the SERVS fields). A small fraction of the SERVS area was already covered by other deep surveys with IRAC, such as the Spitzer IRAC/MUSYC Public Legacy in E-CDFS survey (SIMPLE, PID 20708; PI P. van Dokkum; Damen et al. 2009) in the CDFS field and the Spitzer Ultra Deep Survey (SpUDS, PID 40021; PI J.S. Dunlop) in XMM-LSS. In order to minimize the total required telescope time, these particular areas were not observed. A selection of the IRAC [3.6] and [4.5] data from these surveys (both of which also use the 100 s frametime) are therefore subsequently added into the final SERVS mosaics to attain an approximately uniform overall depth (see details in

Section 3.3). In addition to this pre-existing Spitzer data, there are also two smaller deep fields located in the SERVS area: AORID2 4402688 in Lockman (PID 64; P.I. Fazio) and the overlapping pointings of AORIDs 6005016 (PID 196, P.I. Dickinson) & 10092288 (PID 3407, P.I. Yan) in EN1. We deliberately re-imaged them as part of SERVS so that the data taken during the post-cryogenic period could be compared to the data taken earlier in the mission, and their small size made tiling around them very inefficient.
2.2. Design of observations

The design of the SERVS observations trade-offs to ensure efficient use of the tel filling of the fixed field geometries, and ible scheduling. The SERVS depth was

reflected several escope, accurate reasonably flexselected so that

2 An individual Spitzer observation sequence is an Astronomical Observation Request, or AOR


4

Mauduit et al.

Fig. 2.-- The [3.6] SERVS mosaic image of the EN1 field. Surveys of comparable size are shown here, such as HerMES level 5 (in red, see Oliver et al. 2012 for details), CFHT H-band (dark blue ), UKIDSS J & K-bands (light blue ), the Chandra X-ray survey (green ), SWIRE IRAC (dashed magenta ) and SWIRE MIPS (dashed orange ). Surveys encompassing the entire SERVS field such as HerMES Level 6 and the GMRT survey at 610 MHz are not shown here. More details about ancillary data coverage can b e found in Section 5.

Fig. 3.-- The [3.6] SERVS mosaic image of the ES1 field. Surveys of comparable size are shown here, such as the HerMES­ VIDEO field (in red ), the ATCA/ATLAS radio survey (dark blue ), the VIDEO survey (light blue ), the deep ES1-XMM field of Feruglio et al. (2008) (magenta ), SWIRE IRAC (dashed orange ) and SWIRE MIPS (dashed green ). Surveys encompassing the entire field such as HerMES Level 6 are not shown here. More details about ancillary data coverage can b e found in Section 5.

the confusion level just became significant; attempts to make it much deeper would require better ancillary data (e.g. GOODS) reaching in the confusion noise (the rate at which depth is achieved no longer decreases as the square root of exposure time. Within the constraints of the call for proposals, at this depth, SERVS is the largest area that can easily be surveyed and that had matching ancillary data. The depth of SERVS allows us to detect all massive (> 1011 M ) galaxies out to z 4 (see section 4.3), essentially the entire range of redshift over which they are seen. SERVS can thus trace the evolution of these ob jects from their formation until the present epoch. As a consequence of those factors, the survey covers 18 deg2 and reaches down to 2 µJy in the Spitzer [3.6] and [4.5] bands. Each field was observed in two distinct epochs, with the difference in time between the two epochs ranging from a few days to several months3 . This allows to reject asteroids, and also gives a better photometric accuracy by ensuring that most ob jects appear in very different places on the array in the two sets of observations. It arises from both a half array offset in array coordinated between the two epochs and the fact that the time difference between the execution of each epoch results in a difference in the field rotation, and hence a different
3 When the scheduling gap was in months, the AORs were redesigned at the time of the observations in order to maintain proper alignment of the tiles.

Fig. 4.-- The [3.6] SERVS mosaic image of the Lockman field. Only surveys of comparable sizes are shown here. Superp osed are the HerMES Level 5 (in red ) and Level 3 (magenta & orange ), the Owen/Wilkes deep VLA (orange ), the UKIDSS J, K coverage (cyan ). The Chandra survey is displayed in green and the GMRT survey in blue. SWIRE IRAC & MIPS are shown as dashed dark magenta and dashed dark green. Surveys encompassing the entire field such as HerMES Level 5 are not shown here. More details ab out ancillary data coverage can b e found in Section 5.


SERVS: survey definition and goals

5

Fig. 7.-- A detail of the data coverage in the EN1 field at 3.6 µm. The image is 12 across. The cyan lines indicate the array edges of the individual input images and the greyscale underneath shows the mosaic coverage depth, ranging from 12 to 35 frames. The nonuniform depth of coverage seen above is reflected in the cumulative distribution function of Fig 12.

Fig. 5.-- The [3.6] SERVS mosaic image of the CDFS field. Shown here are the HerMES Level 2 pointing (in red ), the ECDFS/MUSYC survey (orange ), the SIMPLE & GOODS surveys (magenta and green respectively). The VIDEO p ointing is shown in cyan and the ATLAS radio survey in blue. SWIRE IRAC & MIPS are shown as dashed dark magenta and dashed dark green. Surveys encompassing the entire field such as HerMES Level 5 are not shown here. More details about ancillary data coverage can b e found in Section 5.

Fig. 6.-- The [3.6] SERVS mosaic image of the XMM-LSS field. The two HerMES Level 4 & 3 pointings are shown in red and orange. The VIDEO pointing is featured in cyan and VVDS in magenta. The Eastern fields correspond to the SpuDS (blue ), SEDS (green ) and CANDELS (light grey ). XMM-LSS is shown in (dark green ) and extends b eyond the SERVS CDFS field limits. SWIRE IRAC is shown as dashed green and SWIRE MIPS as dashed brown. Surveys encompassing the entire field such as HerMES Level 6 are not shown here. More details about ancillary data coverage can b e found in Section 5.

map grid for the two different epochs (Figure 7 shows the coverage of the two different epochs of observation in the EN1 field). Towards the end of the IRAC Warm Instrument Characterization (IWIC), several tests were performed on variations of the SERVS AORs (Astronomical Observation Request) to establish which observation strategy was optimal. Three strategies were tested, all using the small cycling dither pattern, which allows for good coverage whilst ensuring that ob jects are shifted by a minimum of several arcseconds between observations. The three strategies considered were: (1) two epochs of three dithered 200s frames, (2) two epochs of three dithered pairs of repeated 100s frames, and (3) two epochs of six 100s frames. In theory, strategy (1) is the most efficient and should result in a lower read noise contribution. However, in practice, artifacts from bright stars were strong, reducing the effective area, and the radiation hit (hereafter radhit) numbers were high (each array receives approximately 1.5 hits per second, each affecting on average two pixels, as detailed in the IRAC instrument handbook4 ), resulting in a few radhits leaking through into the final mosaic. There was also no measurable improvement in noise level compared to the other two options, which used 100s frames. Option (2) was almost as efficient as option (1), as only a fraction of a second was added to the overheads (the 200s frames have a longer readout time than the 100s frames), but image persistence effects were significant. Option (3) was therefore adopted, resulting in a very robust survey at the expense of only 3% of extra observing time. The performance of the IRAC camera (both optically, in terms of PSF and array distortion, and in terms of sensitivity) was similar to cryogenic performance, hence the survey design was not modified because of array temperature changes. The sensitivity of the [3.6] band was affected at the 7% level by a change in the array bias between the taking of the early and later SERVS fields (see details of the calibration issues in Section 3.2), but this
4 The IRAC instrument handbo ok can b e found http://irsa.ipac.caltech.edu/data/SPITZER/docs/irac/ iracinstrumenthandbook/

at


6

Mauduit et al.
TABLE 2 Observing log and IRAC Instrument settings for the SERVS fields Field and ep och EN1 ep och 1 EN1 ep och 2 ES1 ep och 1 ES1 ep och 2 Lockman ep och 1 Lockman ep och 2 CDFS ep och 1 CDFS ep och 2 XMM-LSS epoch 1 XMM-LSS epoch 2


Dates observed 2009-07-28 2009-08-02 2009-08-06 2009-08-14 2009-12-10 2009-12-25 2010-11-01 2009-10-13 2010-10-01 2011-02-19 to to to to to to to to to to 2009-08-01 2009-08-05 2009-08-11 2009-08-18 2009-12-21 2010-01-04 2010-11-13 2009-10-28 2010-10-18 2011-03-06

IRAC Campaign(s) PC1 PC1 PC1 PC2 PC10,PC11 PC11,PC12 PC33,PC34 PC6,PC7 PC31,PC32 PC42

Spitzer ID 61050 61050 61051 61051 61053 61053 61052 61052 60024 60024

Array T (K) 31 31 31 29 28.7 28.7 28.7 28.7 28.7 28.7

[3.6] bias (mV) 450 450 450 450 500 500 500 500 500 500

[4.5] bias (mV) 450 450 450 450 500 500 500 500 500 500

The array temp eratures were allowed to float in campaign PC2. Ep o ch 2 of CDFS was observed b efore ep o ch 1, which proved to b e unschedulable in its originally planned slot.

variation was not seen as significant enough to warrant a change in survey strategy. The mapping strategy used the small cycling dither pattern, which ensured full coverage with our map spacing of 280 . However each epoch of a SERVS field takes long enough to observe that the field rotation changes significantly between the start and end of a single epoch of observations. Therefore it needed to be robust against the 7 degree field rotation between AORs expected in an 10 day window in most SERVS fields. To allow for this, and to allow for accurate filling out of fixed field geometries, the SERVS AORs were kept relatively small (3 â 3 maps of 5 â 5 frames) and spaced close enough to ensure overlap for the largest expected field rotation. The small AORs also had the advantage of being easier to schedule, allowing the placement of downlinks and the insertion of short non-SERVS observations. The total observing time for EN1, ES1, Lockman, CDFS and XMM-LSS was 153.4h, 231.8h, 354.5h, 319.1h 323.2h, respectively. The mean integration time per pixel of the resulting SERVS mosaics is close to the design depth at 1200s. There are, however, both regions of significantly deeper data where AORs and map dithers overlap, and shallower areas, particularly around the edges, or where one epoch is affected by scattered light from a field star. Uniformity of coverage of the five SERVS fields is discussed in details in Section 3.3.
3. DATA PROCESSING

(SSC). These images have been dark subtracted, flat fielded, and have had astrometric and photometric calibration applied. A pipeline7 originally used for processing SWIRE data was improved and applied to the SERVS data to further clean the frames of artifacts. Specifically, this pipeline fixed an artifact called "column pul ldown"8 found near bright stars, and also corrected inter-frame bias offsets by setting the background equal to that of a COBE-based model of the zodiacal background (the dominant background at these wavelengths). Due to the inability to use the IRAC shutter2 , all IRAC data suffer from a variable instrument bias level known as the "first-frame effect" which varies depending on the recent detector history. Thus no measurement of the true infrared background, nor of any spatial structure within the background larger than the array size of 5 arcminutes, is possible.

SERVS data is available from the Spitzer Heritage Archive5 . SERVS mosaics (image, coverage, uncertaintly & mask mosaics) and catalogs, including ancillary data at other wavelengths taken as part of SERVS will be made available to the community during the summer of 2012, ultimately through the Infrared Science Archive (IRSA). Catalogs containing the full dataset of ancillary data will be described in detail in Vaccari et al. 2012., in prep.
3.1. Image post-processing and mosaics

Fig. 8.-- Left : An example of a SERVS reduced single 100s exp osure frame at [3.6]. The frame size is 5 â 5 . Right : the final coadded [3.6] image.

The data were coadded (see Figure 8) using the Mopex9 package available from the SSC (parameters used are listed in Appendix B). All the data from a single field were coadded onto a single frame; the two different wavelengths are repro jected to the same astrometric projection so that their pixels align one-to-one. The data are
http://irsa.ipac.caltech.edu/data/SPITZER/SWIRE/ The column pul ldown effect, which manifests in the slow read direction (columns) of the detectors at 3.6 and 4.5µm, is a depression in the zero-level of the column. 9 Mopex and its asso ciated do cumentation can b e obtained at http://irsa.ipac.caltech.edu/data/SPITZER/docs/ dataanalysistools/tools/mopex/
8 7

Data processing begins with the Basic Calibrated Data (BCD) image, produced by the Spitzer Science Center6
5 6

http://sha.ipac.caltech.edu/applications/Spitzer/SHA/ http://ssc.spitzer.caltech.edu/


SERVS: survey definition and goals repro jected with a linear interpolation onto a pixel scale of 0.6 , providing marginal sampling at 3.6 µm. The multiple dithers allow at least some recovery from the severe undersampling of the IRAC camera at these wavelengths. The depth reached by the SERVS observations can easily be put in perspective when comparing SWIRE and SERVS cutouts of a similar region of sky (left & right respectively in Figure 9). With a depth of 3.7 µJy at [3.6] and 7.4 µJy at [4.5], SWIRE is limited to z 1.5 for L galaxies, whereas SERVS can detect these galaxies up to z 4 (see also Figure 17).

7

is typically collected on timescales of years. The Spitzer cryogenic mission, as well as the nominal warm mission, are extremely well-calibrated. However, during this transition period only a small amount of calibration data could be taken, as conditions were constantly changing. The emphSpitzer Science Center provided an initial calibration, which was reliable to a few percent. Photometry from SERVS was compared to that of SWIRE on an ob ject-by-ob ject basis, and small multiplicative offsets at the few percent level were found. These calibration errors were fixed at the catalog level (see Section 4) by applying multiplicative factors derived from comparison between sources detected both in SWIRE and SERVS. Calibration is therefore the same as that of the SWIRE data.
3.3. Uniformity of coverage Certain areas of the original SERVS footprint were already covered by several previous Spitzer surveys, such as SpUDS for XMM-LSS and SIMPLE/GOODS for CDFS (see Section 2.1 for details). These regions were deliberately avoided during the SERVS observation campaign to save observation time. Since the optical properties of the camera did not change between the cryogenic and warm mission and since all the Spitzer data was combined using the same Mopex pipeline, previous survey frames were subsequently merged with the SERVS data at coaddition and patched onto the final mosaics. Given the higher depth of the previously existing smaller Spitzer surveys, it was always possible to reasonably match the depths of the SERVS observations in these areas by selecting the right number of single frames to coadd. In addition, since the archival data is only a minimal fraction of the survey ( 12 % of XMM-LSS and 3 % of CDFS, none in the other fields), these minor differences in coverage between archival and SERVS data are not an issue. The selection of archival data was focused on filling the missing areas as best as possible and thus not refined to be necessarily chosen from different dates of observations, hence some contamination by transient sources on timescales of hours is possible. Some artifacts such as muxbleed 10 were present in the cryogenic data and not in SERVS, however after processing, all artifacts were removed and did not impact the mosaicing. A grid extending over the missing data was set to match the SERVS frame centers (spacing, orientation) as closely as possible. The closest SpUDS (or SIMPLE/GOODS) frames to these grid centers were then automatically selected and a subsample of those was chosen to best cover the area and match the SERVS depth (the left panel of Figure 11 shows the XMM-LSS field frame selection process as an example). The resulting coverage map for the XMMLSS field is shown in the right panel of Figure 11 as an example. Coverage is thus not completely uniform throughout the fields, though averaging 1400 s of exposure time over all five fields (see Figure 12). By design some overlap between AORs was allowed and some areas in a field may have a higher coverage (e.g. at the AOR intersections), for example totalling up to 4700 s in Lockman. Al10 The muxbleed effect app ears as a series of bright pixels along the fast read direction (horizontal in array coordinates), and which may extend the entire width of the array (5 ).

Fig. 9.-- A cutout of the EN1 field at [3.6] (top panel ) & [4.5] (bottom panel ), as imaged by SWIRE (left ) & SERVS (right ). The difference in depth b etween the two surveys can clearly be seen here.

Large mosaics containing all the SERVS epochs of observation in each field were created from the coadded images, along with the uncertainty, coverage and mask images (see Figure 10). The resulting mosaics cover areas of 2, 3, 4, 4.5 and 4.5 deg2 for EN1, ES1, Lockman, CDFS and XMM-LSS, respectively (see Table 1 for details about the geometry of the five fields).
3.2. Calibration issues

The initial observations (all of EN1 and the first half of ES1) were made before the IRAC detectors were stabilized at their current operating temperature of 28.7K, which is now the norm for the Spitzer post-cryogenic (i.e. "warm") mission (see Table 2). Instead, these data were taken at a controlled temperature of 31K. The second half of ES1 was taken during a time when the detector was cooling from 31K to the final temperature of 28.7K, and was not under active temperature control. In addition, during this time period the detector biases for both arrays were adjusted. These changes in temperature and detector operating parameters resulted in changes to both the instrument calibration and its noise properties. These were only measureable in the [3.6] band, resulting in an 7% increase in the noise in EN1 and ES1 at [3.6] compared to the remainder of the survey. IRAC is calibrated based on dedicated calibration observations collected during science operations. This data


8

Mauduit et al.

Fig. 10.-- SERVS final set of mosaics for the EN1 field at 3.6µm (top left : image mosaic, top right : coverage mosaic, bottom left : uncertainty mosaic, bottom right : mask mosaic).

though great care was taken to minimize variations in the coverage throughout a field, the exposure time can rise up to 5000 s (e.g. in XMM-LSS) when the added ancillary data intersects both SERVS epochs (mostly around the edges of the SERVS/ancillary data, as is obvious in the right panel of Figure 11). However these areas of higher coverage represent a very small portion of the fields and do not have an impact on the overall uniformity, given the already intrisic non-uniform nature of the SERVS coverage due to the original tiling design. The higher depth of the observations at the frames overlaps or due to archival data overlapping the SERVS frames do not result in any impact to the survey reliability or completeness when a minimum flux selection based on the SERVS data is used.
3.4. Image masks

TABLE 3 Lookup table for the mask image bit values Bit numb er BIT00 = BIT01 = BIT02 = BIT03 = BIT04 = BIT05 = BIT06 = BIT07 = BIT08 = Bit value 1 2 4 8 16 32 64 128 512 Flag Overall data quality Set if pixel contains radhit Set if optical ghost present Set if stray light present Set if saturation donut Set if pixel contains latent image Set if pixel is saturated Set if column pulldown present Set if bright star is present

Notes: Flags are describ ed in more detail in the text in Section 3.4

Bright source artifacts (straylight, column pulldown, latents) are a significant problem with Spitzer data. Most of these artifacts are discussed in more detail in the IRAC instrument handbook4 , available from the SSC, as well as in Surace et al. (2004) and Surace et al. (2005)7 . In

order to flag for these artifacts, mask images are created for each five fields. Flags bit values attributed to each significant artifacts are listed in Table 3, as well as in the headers of the mask FITS files. In addition to these known Spitzer image artifacts, saturated stars are common across the deep and wide SERVS fields and thus need to be flagged appropriately.


SERVS: survey definition and goals

9

Fig. 11.-- Left : XMM-LSS single frame centers, showing ep och 1 in cyan and epo ch 2 in blue crosses (see Table 2). A grid extrap olating the XMM-LSS ep och2 geometry was overlaid onto the SpUDS field frames (in green open circles ) and used to select the relevant SpUDS frames to co-add (magenta solid points ) to complete the XMM-LSS field. Right : Coverage map in equatorial coordinates of the XMM-LSS field at [3.6]. Note the addition of the SpUDS data b etween 35 34 & -5.7 -4.7 to patch the XMM-LSS field. The rotation in tiling induced by the choice of two different epo chs of observation (of six 100 s frames each, as discussed in Section 2.2) is also clearly visible here. Coverage is not completely uniform throughout the field and data can be deeper, especially at the overlap of the SERVS (warm mission) & SpUDS (cryogenic) surveys (see Section 3.3 for details).

Fig. 12.-- Cumulative distribution function of the coverage maps for all five fields, in terms of exposure time (in seconds). The curve represents the fraction of pixels with that integration time or lesser.

Fig. 13.-- Bright star mask cutout of a p ortion of the Lockman field. Black circles show the masked regions around the crossidentified 2MASS bright stars centers (red crosses ) in the field. Radii are prop ortional to 2MASS K-band brightness and shown in Table 4.

Indeed very bright stars do not have reliably extracted fluxes in SERVS, and may occasionally be split into multiple fainter sources, or are saturated, so no flux can be measured accurately; erroneous detections in the wings of the PSF can also cause artifacts. As a result, a safe radius has to be set and flagged around those bright stars. Luckily, any ob ject triggering bright star artifacts in the SERVS data is easily detected by the 2MASS survey (the Two Micron All Sky Survey, Skrutskie et al. 2006), which has reliable fluxes even for very bright ob jects. The positions and K-band magnitudes of bright stars are downloaded from the 2MASS Point Source Catalog (PSC) catalog in the Vizier database11 . Magnitudes are thus converted into radii according to Table 4. The radii are
11

taken from SWIRE (Surace et al. 2004) and increased to take into account the deeper exposures of SERVS. To reduce memory requirements on such large images, subsets of the mosaics were cut around each bright star, flagged and then re-embedded onto the final mask mosaic. The resulting bright star flag masks a circular region around each bright star in the fields. A part of the Lockman field bright star mask is shown in Figure 13.
4. CATALOGS 4.1. Catalogs extraction and calibration

The Vizier database is available at http://vizier.u-strasbg.fr/

Catalogs were made using SExtractor (Bertin & Arnouts 1996). Two sets of catalogs were produced for each field (the parameters used in the source extraction are given in Appendix B). The first set is based on extrac-


10
TABLE 4 Bright Object flagging for the mask images 2MASS­K mag. range > 12.0 10.0 - 12.0 8.0 - 10.0 7 .5 - 8 .0 6 .5 - 7 .5 5 .0 - 6 .5 < 5 .0 radius ( ) 0 15 20 30 45 60 120

Mauduit et al.
TABLE 5 Aperture sizes and corrections Aperture number ap1 ap2 ap3 ap4 ap5 Aperture radii (arcsec) 1.4 1.9 2.9 4.1 5.8 [3.6] correction (arcsec) 0.585 0.736 0.87 0.92 0.96 [4.5] correction (arcsec) 0.569 0.716 0.87 0.905 0.95

Notes: Ap erture sizes and corrections were derived for SWIRE Surace et al. (2004). More details can b e found therein.

Notes: The radii are taken from SWIRE (Surace et al. 2004) and increased to take into account the deep er exp osures of SERVS.

tions from the [3.6] and [4.5] bands, separately. A second set is then created when the two catalogs are merged, and only detections common to both are retained. This results in a high reliability catalog which is used to combine with other data sets. We recommend that for rare ob ject searches the second set, high reliability catalogs, be used. The single band catalog may be used for bandmerging with other data (e.g. UKIDSS/DXS data), or for statistical studies. Sextractor aperture fluxes are computed within radii of 1.4 , 1.9 , 2.9 , 4.1 and 5.8 and corrected using the aperture correction factors derived for SWIRE DR2/3 by Surace et al. (2004) and reported in Table 5. The IRAC instrument has a calibration tied to standard stars as measured in a fiducial 12" radius aperture. This aperture is non-ideal for faint-source extraction, so the fluxes are measured in smaller apertures and suitable aperture corrections are applied. A fraction of the SERVS data was taken prior to the final temperature stabilization of IRAC, and prior to the selection of the final array biases (affecting the EN1 and ES1 fields, see Table 2). During this "floating temperature" period, the IRAC calibration drifted, with changes to both the overall gain and the detector linearization, as discussed Section 3.2. As a result, the images have overall calibration errors at the level of a few percent. In addition, the other fields show small, but noticeable calibration differences compared to the cryogenic [3.6] SWIRE data. These calibration errors were fixed at the catalog level by applying multiplicative factors derived from comparison between sources detected both in SWIRE and SERVS (f[3.6]SWIRE /f[3.6]SERVS = 1.07 for EN1 & ES1 and 1.02 for Lockman, CDFS and XMM-LSS; at [4.5] the correction factors are very close to unity, and no corrections were applied). SERVS calibration is thus tied to that of the SWIRE data.
4.2. Number counts and completeness Simple number counts were derived from the extracted catalogs. The SERVS source counts for the XMMLSS field is provided as an example in the left part of Figure 14 (the remaining four fields are shown in Appendix A). Several features are visible in this plot. Fundamentally, the observed counts present as a power-law. This is shallow at the bight end, primarily due to the presence of bright stars. There is a known break in the power-law index near 100 µJy (Glazebrook et al. 1994). At the faintest levels, the turnover is a result of the increasing incompleteness of the survey. It is clear that the

SERVS completeness level catastrophically drops near 2­ 3 µJy. As a sanity check, a sample of bright stars (SERVS fluxes within 0.3 < f < 2 mJy and stellarity index > 0.95) was selected. Cross-identifying with the 2MASS catalog, K-band measurements were used to compare the deviation of SERVS colors with respect to the 2MASS ones. A color-color plot of K2MASS - M3.6µm (Vega) versus M3.6µm (Vega) - M4.5µm (Vega) for the XMM-LSS field, shown in the right part of Figure 14, confirms that SERVS is consistent with 2MASS. SERVS detects 100, 000 sources per square degree and, with 40 beams per source, approaches the classical definition of where source confusion becomes important. The effective depth of SERVS is thus affected by confusion noise, which makes the definition of survey depth dependent on the experiment one wishes to carry out. For a detection experiment on point-source ob jects detected in another waveband with a well-determined position (positional uncertainty much lower than the SERVS beam of 2 ), the appropriate number, denoted pp , is determined from the pixel-to-pixel variance in a source-free region of a single BCD (assuming it scales as the square root of the coverage and that extraction is carried out by source fitting; i.e. assuming 7.0 and 7.2 noise pixels in [3.6] and [4.5], respectively). Another measure of the noise, which includes some contribution from confusion noise, is obtained from "empty aperture" measurements, where the standard deviation of fluxes in ob ject-free apertures in the final mosaic is measured directly. For our fields, this measurement was made in 3.8 diameter apertures (SWIRE aperture 2), and is denoted ap . SWIRE aperture 2 is recommended by the SWIRE team as the most stable aperture for photometry since most faint IRAC sources are slightly resolved at the 1 - 2 level. Survey depths for the various measurements are summarized in Table 6. Finally, for survey work, the completeness limits at 50 and 80% (Sc50 and Sc80 ), give a good indication of the depths that are usable for global survey properties such as source counts. Following the techniques of Chary et al. (2004) and Lacy et al. (2005), we simulated 10,000 model galaxies, distributed them in the reduced mosaics, and extracted them using the same pipeline that created the catalogs. The recovery rate of the model galaxies was used as our completeness indicator and suggests that 50% completeness is reached at a flux density of 2 - 3 µJy in the single band catalogs at both [3.6] and [4.5], due to a combination of signal-to-noise and source confusion (see Figure 15). More details of the completeness and reliability of the SERVS catalogs will be pre-


SERVS: survey definition and goals

11

TABLE 6 Approximate survey depth and completeness in the SERVS fields Measurement 5 pp 5 ap S50c S80c


[3.6] value (µJy) 1.3 1.9 4 . 0 , 3 .0 7, 5

[4.5] value (µJy) 1.5 2.2 3 .5 , 3 .5 5, 5

The two completeness levels shown detection catalog and the single-band Notes: EN1 and ES1 are ab out 7% [3.6] due to different detector settings (Table 2).

corresp ond to the the two-band catalog. n oisier th an th e oth er fie ld s in used early in the warm mission

Fig. 15.-- Completeness plots for Lo ckman Hole at [3.6] shown in green, [4.5] shown in blue, and the reliable dual-band detection catalogs shown in black. Using the techniques of Chary et al. (2004) and Lacy et al. (2005), 10,000 mo del galaxies were simulated and placed in the mosaics. The sources were extracted using the same pip eline as the catalogs and the completeness was estimated as the recovery rate of the simulated mo del galaxies. Fig. 14.-- Top : SERVS numb er counts versus flux at [3.6] (black histogram) and [4.5] (red histogram) for the XMM-LSS field. Grey dashed lines show the selection of sources used in the right plot (with fluxes as 0.3 < f < 2mJy). Bottom : Color-color plot showing K2MASS - M3.6µm (Vega) versus M3.6µm (Vega) - M4.5µm (Vega) for sources within the flux range defined by the grey dashed lines above, plus a cut in stellarity index > 0.95 and the existence of a 2MASS K-band measurement as an additional constraint. Red dashed lines help pinpoint the location of the (0,0) point in this diagram. Similar plots for the EN1, ES1, Lockman & CFDS fields are shown in App endix A.

sented by Vaccari et al. (2012, in prep.).
4.3. Expected detection limits & redshift distribution

The SERVS pro ject uses semi-analytic models extensively, both to make testable predictions of the properties of SERVS galaxies, and to inform our follow-up strategies in wavebands other than the near-infrared (e.g. Figure 18). Two different semi-analytic models are currently being used. The first set is based on the Guo et al. (2011) version of the Munich semi-analytic model for which light-

cones12 were created and fully described in Henriques et al. (2012). The lightcones contain a wide range of photometric bands that cover the UV to near-infrared region of the spectra and allow an exact match to the observed selection criteria. They also include a choice between fluxes computed using the Bruzual & Charlot (2003) or the Maraston (2005) stellar populations. The latest have been shown to reconciled the predicted K band rest-frame luminosity function with observations at high redshift (Henriques et al. 2011, Henriques et al. 2012), for which IRAC data - such as those obtained with SERVS - have been essential. The second set, from van Kampen et al. (2012a), includes both the effects of halo-halo and galaxy-galaxy mergers, and uses GRASIL (Silva et al. 1998) to predict spectral energy distributions (SEDs) from the optical to submillimeter. The expected redshift distribution is derived from the simulations and shown in Figure 16. A lower flux limit of 2 µJy (AB magnitude of m[3.6] < 23.1) for the SERVS
12 These lightcones are publicly available at http://www.mpagarching.mpg.de/millennium


12

Mauduit et al.

Fig. 16.-- SERVS expected normalized galaxy counts at the detection limit of 2 µJy (or m[3.6] < 23.1). The two histograms corresp ond to flux limits applied to the [3.6] band either using the Maraston (2005) or Bruzual & Charlot (2003) stellar population mo dels (see Section 4.3).

survey was used here. The expected redshift distribution of the SERVS galaxies peaks around z 1 and extends to redshifts of z 3, with a small fraction of ob jects detected up to z 4 (for basic comparison purposes, the SWIRE photometric redshift distribution can be found in Rowan-Robinson et al. 2008; at the current time, the SERVS photometric redshift distribution has not been derived but will be presented by Pforr et al. 2012b, in prep., as discussed in Section 5.7). Galaxies detected in SERVS will therefore span the epochs where galaxies gain the vast ma jority of their stellar mass. Indeed, Brown et al. (2007) and Cool et al. (2008) estimate that L galaxies roughly double in mass between z = 0 and z 1. In addition, van Dokkum et al. (2010) recently showed that about half the mass of any given large galaxy is added between z = 0 and z = 2, by comparing galaxy samples at constant number densities. SERVS will be able to extend such studies out to higher redshifts with good statistics. In order to roughly estimate the luminosity of the faintest galaxy SERVS will be able to detect in the IRAC [3.6] band as a function of redshift, three model SEDs were used: a starburst similar to M82, a 250 Myr stellar population and a 5 Gyr stellar population from Maraston (2005). Figure 17 shows the luminosity of the faintest detectable source from 0.1 µm to 1 mm as a function of redshift, if this source was represented by one of the three SED models we used. SERVS might be able to detect L galaxies up to z 4 and 0.1L out to z 1. The luminosities of the Maraston SEDs can easily be converted to stellar masses by multiplying the luminosity of the 5 Gyr and 250 Myr stellar population models by 1.15865 and 0.00133 respectively. SERVS will ensure the derivation of robust stellar masses because it includes the restframe near-infrared out to high redshifts. This coverage is essential to break the degeneracy between star formation history and dust reddening (Maraston et al. 2006). Photometric redshifts for SERVS galaxies are also being derived (see section 5.7).

Fig. 17.-- Top panel: SERVS faintest galaxy to be detected, in units of solar luminosities (integrated luminosity from the optical to the far-IR: 0.1 µm­1 mm) at a given redshift. Three Sp ectral Energy Distribution (SED) models are considered here and shown in the bottom panel. The three SEDs consist of an M82-like starburst galaxy (in blue ), a 250 Myr stellar population (magenta ) and a 5 Gyr stellar p opulation (red ), scaled to the same 3.6µm restframe. SERVS can detect L galaxies out to z 4, and 0.1L out to z 1. At redshift 5, a starburst galaxy of luminosity 1012 L could p otentially b e detected.

5. ANCILLARY DATA

Overlaps with existing and surveys in progress are listed in Table 7. Here we provide a more detailed description of some of the most significant (in terms of their overlaps with SERVS) of these surveys.
5.1. Optical surveys A number of optical surveys overlap one or more of the SERVS fields. Among the most significant are the ESO/Spitzer Imaging Survey (ESIS) in ES1 (Berta et al. 2006, 2008) which reaches depths of 25, 25, 24.5, 23.2 (Vega) in B, V , R & I , respectively, the Canada France Hawaii Telescope Legacy Survey (CFHTLS; both deep and wide pointings in u ,g , r,i & z in the XMM-LSS field reaching depths of 28.7, 28.9, 28.5, 28.4, 27.0 and 26.4, 26.6, 25.9, 25.5, 24.8 AB magnitudes, respectively), and ancillary SWIRE data in Lockman, EN1 and CDFS in a variety of depths and filters, but typically reaching at least r = 24.5 (Gonzalez-Solares et al. 2010; Surace et al. 2010). The Spitzer Adaptation of the Red Sequence Cluster Survey (SpARCS13 , Muzzin et al. 2009, Wilson et al. 2009, DeGroot et al., 2012, in prep, Muzzin et al., 2012a, in prep) has imaged the entire SWIRE area (excluding the XMM-LSS field which is covered by the CFHTLS) in the z filter using Megacam on the Canada France Hawaii Telescope (CHFT) or the Mosaic camera on the Blanco Telescope at Cerro Tololo Interamerican Obser13

http://www.faculty.ucr.edu/gillianw/SpARCS


SERVS: survey definition and goals
Surveys at other wavelengths covering Field Name EN1 X-ray data N04
1 >

13

TABLE 7 10% of a SERVS field (all-sky surveys excepted). Near-IR Data DXS5 Mid-IR Data SWIRE
6

Optical Data SWIRE/INT2 SDSS3 SpARCS4 ESIS12 VOICE13 , SpARCS4 SWIRE/KPNO+INT2 SDSS3 SpARCS4 SWIRE/CTIO22 GaBoDS23 VVDS24 VOICE13 , SpARCS4 CFHTLS30 VVDS24 SXDS34

Far-IR/ submm Data HerMES7 L5 S2CLS8 HerMES7 L6 HerMES7 L3,L5 S2CLS8 HerMES7 L2,L5 LABOCA/LESS26

Radio Data G08a9 Gr1010 ATLAS
15

ES1 Lockman

F0811 W09
16

VIDEO

14

SWIRE SWIRE

6 6

DXS5

CDFS

CDFS21

VIDEO

14

SWIRE6 SIMPLE25

OM0817 G08b18 ,G10 I0920 ATLAS15 M0827

19

XMM-LSS

XMM-LSS SXDS29

28

VVDS31 UDS5 VIDEO14 DXS5

SWIRE6 SpUDS25

HerMES7 L5 S2CLS8

VVDS-VLA S0633

32

Notes: [1] Chandra prop osal 6900602 (P.I. Nandra); [2] Gonzalez-Solares et al. (2011); [3] Abaza jian et al. (2009); [4] DeGro ot et ´ al. 2012, in prep. [5] Lawrence et al. (2007); [6] Lonsdale et al. (2003), Surace et al. (2005)7 ; [7] Oliver et al. (2012); [8] http://www.jach.hawaii.edu/JCMT/surveys/Cosmology.html; [9] Garn et al. (2008a); [10] Grant et al. (2010) [11] Feruglio et al. (2008); [12] Berta et al. (2006, 2008); [13] P.I. G. Covone & M. Vaccari, http://p eople.na.infn.it/covone/voice/voice.html [14] Jarvis et al. (2012), in prep; [15] Norris et al. (2006), Middelb erg et al. (2008); [16] Wilkes et al. (2009); [17] Owen & Morrison (2008); [18] Garn et al. (2008b); [19] Garn et al. (2010); [20] Ibar et al. (2009); [21] Lehmer et al. (2005); [22] http://www.astro.caltech.edu/bsiana/cdfs opt; [23] Garching-Bo chum Deep Survey, Hildebrandt et al. (2006); [24] Le F`vre et al. (2005); [25] Damen et al. (2009), irsa.ipac.caltech.edu/data/SPITZER/do cs/spitzermission/observingprograms/legacy/; e [26] Survey of the CDFS with the Large Ap ex Bolometer Camera (LABOCA), Weiú et al. (2009); [27] Miller et al. (2008); [28] Pierre et al. (2007); [29] Ueda et al. (2008); [30] www.cfht.hawaii.edu/Science/CFHTLS; [31] Iovino et al. (2005); Temp orin et al. (2008); [32] Bondi et al. (2007); [33] Simpson et al. (2006); [34] Furusawa et al. (2008);

vatory (CTIO). The Megacam observations reach a mean depth of 24.2 AB magnitudes, and the Mosaic camera observations reach a mean depth of 24.0 AB magnitudes. The SpARCS collaboration has spectroscopically confirmed 15 clusters at z 1 (Muzzin et al. 2009, Wilson et al. 2009, Demarco et al. 2010, Muzzin et al. 2011, Muzzin et al., 2012b, in prep, Wilson et al., 2012a, in prep), and is in the process of carrying out detailed multipassband and spectroscopic follow-up studies (Rettura et al., 2012, in prep, Lidman et al., 2012, in prep, Noble et al., 2012, in prep, Ellingson et al., 2012, in prep, Wilson et al., 2012b, in prep). Started in October 2011, the VST/VOICE survey (P.I. G. Covone & M. Vaccari) is surveying the CDFS and ES1 fields in u, g , r, i aiming at reaching AB 26 at 5 . Optical spectroscopy has thus far been confined to small regions of SERVS - such as the recently completed PRIMUS (PRIsm MUlti-ob ject Survey, Coil et al. 2011) survey, covering parts of the ES1, CDFS & XMM-LSS fields - or to specific types of ob jects. The largest spectroscopic survey is the VVDS, which has 10 000 spectroscopic redshifts for field galaxies with 17.5 < IAB < 24 in their XMM-LSS and CDFS subfields (Le F`vre et al. e 2005). In addition, spectra of AGN (Active Galactic Nuclei) and quasars, now totaling several hundred ob jects selected by various techniques, have been obtained in the SERVS fields by Lacy et al. (2007) , Trichas et al. (2010) and Lacy et al. (2012, in prep).
5.2. Ground-based near-infrared surveys

One of several ma jor surveys to be carried out by the Visible and Infrared Survey Telescope for Astronomy (VISTA) is the five filter near-infrared VISTA Deep Extragalactic Observations (VIDEO) survey (Jarvis et al. 2012, in prep.), which will cover 12 deg2 to AB magnitudes of 25.7, 24.6, 24.5, 24.0, 23.5 in Z, Y , J, H and Ks filters. The ES1, XMM-LSS and CDFS SERVS fields are designed to exactly overlap their corresponding VIDEO fields. The combination of VIDEO and SERVS will be a particularly potent tool for the study of galaxy evolution at high redshifts. The Deep eXtragalactic Survey (DXS) is part of the UKIRT Infrared Deep Sky Survey (UKIDSS, Lawrence et al. 2007), and will cover the Lockman and EN1 fields to 23.1 and 22.5 (AB) in J and K respectively. As of October 2011, Data Release 7 is available to the entire astronomical community. It covers parts of the EN1, Lockman and XMM-LSS fields. Data Release 9 is available to the community served by the European Southern Observatory, and includes additional data.
5.3. Mid- and far-infrared, and submil limeter surveys

The SERVS fields were designed to be contained within the SWIRE fields. This has mostly been achieved, although constraints from other surveys mean that a small fraction of SERVS lies outside of the SWIRE coverage. The HerMES14 survey is a Herschel Key Pro ject to survey most of the extragalactic Spitzer fields, including the SWIRE fields. HerMES has six levels, corresponding to increasing depths, level 6 being the shallowest.
14

http://hermes.sussex.ac.uk/


14

Mauduit et al.

Smith et al. (2012) give measured flux densities at which 50% of injected sources result in good detections at the SPIRE wavelengths of (250, 350, 500) µm ranging from (11.6, 13.2, 13.1) mJy to (25.7, 27.1, 35.8) mJy, depending on the depth of the observation, with the deeper observations being confusion limited. All of SERVS is covered to Level 6 or deeper, with significant areas as deep as Level 3 (5 limiting flux density 7 mJy at 160 µm with the PACS instrument). Full details of the HerMES survey are given in Oliver et al. (2012). The wide area component of the SCUBA-2 Cosmology Survey (S2CLS) will cover the XMM-LSS, Lockman, EN1 and CDFS fields. The survey will be performed at 850 µm to a root mean square (RMS) noise of 0.7 mJy.
5.4. Radio surveys

The Australia Telescope Large Area Survey (ATLAS; Norris et al. 2006) overlaps with much of the SERVS fields in ES1 and CDFS. The ATLAS survey will have an RMS of 10 µJy and a spatial resolution of 8 at 1.4 Ghz across both fields (Banfield et al. in prep). A preliminary release of the survey data for CDFS (Norris et al. 2006) and ES1 (Middelberg et al. 2008) has an RMS noise of 30 µJy. An image showing the overlap between the SERVS and ATLAS fields can be found in the accompanying paper by Norris et al. (2011a). Subsequent ATLAS data releases will be published shortly by Hales et al. (2012) and Banfield et al. (2012), both in preparation. The EN1 and Lockman fields have been surveyed at 610 MHz with the Giant Meter Wave Telescope (GMRT; Garn et al. 2008a,b, respectively). These surveys have a mean RMS of 60 µJy at a spatial resolution of 6 . The Very Large Array (VLA) has conducted several surveys in the SERVS fields. The Lockman field includes the deepest radio survey at 1.4 GHz to date, a single 40 â 40 pointing centered at 161.5d, +59.017d, reaching a 5 detection limit of 15 µJy near the center of the primary beam (Owen & Morrison 2008), and further deep coverage by Ibar et al. (2009). Simpson et al. (2006) have surveyed the Subaru Extragalactic Deep Survey region with the VLA to a detection limit of 100 µJy, and Bondi et al. (2007) have surveyed the VVDS field in XMM-LSS at both 610MHz with the GMRT and 1.4 GHz with the VLA to limits of 200 and 80 µJy, respectively. The Faint Images of the Radio Sky at Twenty cm (FIRST) survey (White et al. 1997) covers the Lockman, EN1 and part of the XMM-LSS survey at 1.4 GHz to a sensitivity limit of 1 mJy at a spatial resolution of 5 . Two pathfinder telescopes for the proposed Square Kilometer Array are currently under construction in the southern hemisphere. Both these telescopes will undertake continuum surveys which will cover the southern SERVS fields. The Evolutionary Map of the Universe (EMU, Norris et al. 2011b) survey with the Australian Square Kilometer Array Pathfinder (ASKAP) will cover the whole southern sky to 10 µJy RMS sensitivity at 1.4 GHz with a 10 FWHM (Full Width at Half Maximum) synthesized beam. The South African Karoo Array Telescope (MeerKAT, Jonas 2009) will conduct the MeerKAT International Giga-Hertz Tiered Extragalactic Exploration (MIGHTEE) survey, which has a strong SERVS participation. MIGHTEE has several tiers, one of which will include the southern SERVS/VIDEO

Fig. 18.-- Approximate 5 depths of SERVS and near-IR and optical surveys covering the same areas. Overplotted is the SED of a 1Gyr old stellar p opulation at z = 2 from Maraston (2005). SERVS is in red, VIDEO in green, DXS in cyan, CFHT H -band in orange and SpARCS in blue. Our target depth for optical bands imaging is shown as the magenta bar (several fields already have imaging to at least this depth). Note that no ground-based survey covers the entirety of the SERVS data, hence multiple surveys are overlaid.

fields to 1 µJy RMS at 1.4 GHz with an 5 FWHM beam. In the North, the Low Frequency Array (LOFAR, R¨ tgering et al. 2011) will target the SERVS/SWIRE ot fields for deep surveys at frequencies of 100 MHz. In the Northern hemisphere, the WODAN (Westerbork Observations of the Deep APERTIF Northern-Sky) is being proposed for Westerbork+APERTIF and will match EMU sensitivity and resolution (R¨ tgering et al. 2011). ot
5.5. X-ray surveys

The XMM-LSS field overlaps with the XMM-LSS survey (Pierre et al. 2007) and the Subaru/XMM-Newton Deep Survey (SXDS, Ueda et al. 2008). Wilkes et al. (2009) have a deep Chandra survey overlapping with the deep VLA pointing of Owen & Morrison (2008) in Lockman. In EN1, Chandra program 690062 (P.I. Nandra) covers 1deg2 of the central portion of the field.
5.6. Further ground-based data taken or to be taken by

the SERVS team Further multiwavelength ancillary data on the SERVS fields are currently being obtained with the overall goal of matching the SERVS depth in shorter wavebands. These data will be made available as part of the overall SERVS public release. Observations are concentrated on longer optical and near-IR wavebands as these are generally more useful for photometric redshift estimates at higher redshifts (van Dokkum et al. 2006; Brammer et al. 2008; Ilbert et al. 2009; Cardamone et al. 2010). In the optical, the SDSS filter set is used where possible, as the narrower bands allow higher fidelity photometric redshifts than the Johnson-Cousins system (see Figure 19). Target depth in the optical is an AB magnitude 25 and 23 in the near-infrared (the area covered by VIDEO will be significantly deeper than this). Figure 18 shows a comparison of the SERVS depth with those of the other ma jor optical/near-IR surveys planned or in progress.


SERVS: survey definition and goals Observations with SuprimeCam on the Subaru telescope have been carried out in i and z bands as part of the Gemini-Subaru (PI A. Verma) and Keck-Subaru (P.I. S.A. Stanford) time swaps. The z -band observations are concentrated in the northern fields, as VIDEO will cover the southern fields in Z . An optical imaging campaign of the ES1 and CDFS fields using the CTIO-4m Mosaic camera has been completed (in November 2009 & October 2010, with a total of 9 nights). The specific goals were (1) to complete imaging in r and i to 24.2 and 23.2 (Vega) over the whole area of the ES1 and CDFS fields and (2) to obtain deeper rand i-band data (to 25.0 and 24.0) in the center of ES1 to complement deep surveys by VISTA and warm Spitzer, and where deep data at shorter wavelengths exists from ESO. Images are currently being processed. An H -band imaging campaign, led by M. Lehnert has obtained H -band data with WIRCAM on the CFHT, to match the UKIDSS DXS J and K imaging in the EN1 field. These data are currently being analysed.
5.7. Photometric redshifts The wealth of ground-based data available in the SERVS fields in conjunction with the IRAC data obtained through the SERVS pro ject provides the ideal basis for a robust determination of photometric redshifts, stellar masses and other stellar population properties from spectral energy distribution fitting (SED). Here the photometric redshifts were computed via SED fitting using the HyperZ code (Bolzonella et al. 2000) and the Maraston (2005) stellar population templates, following the procedure outlined in Pforr, Maraston & Tonini (2012b, in prep). In brief, the best fit is determined by minimising the 2 between a large grid of population model templates (for various star formation histories, ages, metallicities, reddening, and redshifts) and the photometric data. Specifically, we use a template set with exponentially declining star formation rates, metallicities ranging from 1/5 to twice solar, a minimum age of 0.1 Gyr and a Calzetti et al. (2000) reddening law with extinction parameter AV ranging between 0 and 3 mag. This template set gives the minimum variance between photometric redshifts and the spectroscopic redshifts calibration set. By comparing to available spectroscopic redshifts using the compilation of redshifts in the SWIRE fields by Vaccari et al. (2012, in prep.), a redshift accuracy of z/(1 + z ) = 0.011 ± 0.072 is obtained for a dataset with an optimal wavelength coverage (i.e. U, g, r, i, Z, J, K, IRAC1 [3.6], IRAC2 [4.5] for the EN1 field). Due to the small15 number of SERVS ob jects with available spectroscopic redshifts, we further confirmed the redshift accuracy by cross-matching the VVDS survey in the extended Chandra Deep Field (Le F`vre et al. 2005) with e the IRAC SIMPLE survey (Damen et al. 2009), obtaining 831 matches with redshifts and 4.5 µm fluxes > 3µJy. However, the diversity inherent to the ancillary data coverage for the SERVS fields impairs the redshift determination particularly for ob jects with a narrower waveIn the EN1 field, among the 2997 extended sources with optimal wavelength coverage (resp ectively 8513 with non-optimal wavelength coverage), only 30 (resp ectively 60) have sp ectroscopic redshifts.
15

15

Fig. 19.-- Photometric redshift distribution of SERVS galaxies in EN1 (black histogram ). Photometric redshifts are obtained from SED fitting using the Hyp erZ co de and the Maraston (2005) stellar population synthesis templates. Included are only ob jects classified as extended in the optical and near-IR bands in order to exclude stars and AGN-dominated sources. Additionally the red histogram shows the subsample of sources which are detected in all filter bands (U,g,r,i,Z,J,K, IRAC1, IRAC2 for EN1) which provides the most robust photometric redshift estimates. From Pforr, Maraston & Tonini (2012b, in prep.).

length coverage. This is explored in detail in Pforr et al. (2012b, in prep.). In summary, photometric redshifts display larger scatter when the wavelength coverage used for the fitting is narrow, i.e. does not incorporate the rest-frame UV, optical or near-IR rest-frame or a combination thereof, particularly when spectral breaks such as the 4000° break and the Lyman-break are not covered A by the filter setup (see Ilbert et al. 2010 and Bolzonella et al. (2000) for studies on photometric redshift accuracies). The distribution of photometric redshifts obtained for extended16 ob jects, i.e. galaxies, in EN1 is shown in Figure 19. The red histogram highlights the most robust sample (so-called "gold " sample) with the best wavelength coverage, i.e. corresponding to the case where the ob ject is detected in all filter bands. The black histogram includes also ob jects which were detected in less photometric bands.
6. ONGOING SCIENCE WITH SERVS

In this Section, we summarize already published or ongoing work by SERVS team members to underline some of the key science goals of the SERVS survey.
16 The decision whether an ob ject is extended is based larity flags provided with the ancillary data in optical and filter bands. Sources with a point-like nature indicative of AGN were excluded since the model template we use in th do not include non-thermal emission

on stelnear-IR stars or e fitting


16
6.1. Galaxies and their environments

Mauduit et al.

Sampling 0.8Gpc3 , SERVS is large enough to contain significant numbers of ob jects while still being deep enough to find L galaxies out to z 4 (Figure 16). By combining the five different fields of SERVS, the survey effectively averages over large-scale structure, and is able to present a true picture of the average properties of galaxies in the high redshift Universe. Galaxy-galaxy correlations are being computed by van Kampen et al. (2012b) in the SERVS fields. The five large, well separated, SERVS fields enable to average out the effects of large-scale structure on such measurements. Initial results on the EN1 field show the evolution of the correlation function between high redshifts (z > 1.3) and intermediate redshifts ( 0.8) using simple [3.6] - [4.5] color cuts. SERVS is deep enough and wide enough to find field clusters at z > 2, should they exist in significant numbers. The SERVS fields lie within the SpARCS fields (see survey description in Section 5.1, DeGroot et al. 2012, in prep), The deep SERVS observations will allow both a more accurate measurement of the faint end of the luminosity function of known SpARCS clusters which fall within the SERVS footprint, and also the detection of new clusters at higher redshifts than possible with SpARCS. In addition, Geach et al. (2012, in prep.) are pursuing a cluster selection technique using photometric redshifts combined with Voronoi tessellation in an attempt to identify further, mostly lower mass, cluster candidates.
6.2. The Active Galactic Nuclei engine

The [3.6] and [4.5] bands are important diagnostics of AGN SEDs, as they are where host galaxy light and hot dust emission from the torus overlap in the SEDs of many dust obscured AGN and quasars at moderate redshifts (z 1). In unobscured, or lightly obscured ob jects, this is where the optical/UV emission from the accretion disk transitions to the hot dust emission. Petric (2010) & Petric et al. (2012, in prep.) present SEDs of AGN and quasars selected in the mid-infrared, and use SERVS data to help apportion the different sources of near-infrared light. The luminosities of the hosts themselves, if free from contamination by AGN-related light, can also be used to study the stellar masses of the host galaxies.
6.3. AGN and their environments Current models for galaxy formation indicate that AGN and quasar activity play an important role in galaxy formation (e.g. Hopkins et al. 2006), regulating the growth of their host galaxies through feedback (see for example Schawinski et al. 2007, or more recently Farrah et al. 2012, who provide new and strong evidence for AGN feedback). However, the exact nature of this feedback process is unclear. Environments in which AGN and quasars lie can indicate the masses of the dark halos they inhabit, and also how these masses depend on AGN luminosity and redshift (Farrah et al. 2004). These can illuminate models for feedback, for example, a preponderance of AGN in massive halos, accreting at relatively low rates might be an indicator that their host galaxies are no longer growing rapidly (Hopkins et al. 2007). At low redshifts (z < 0.6) the SDSS has been used to suc-

Fig. 20.-- Stacked source over-density vs radial distance for the 11 QSOs in the redshift range of 2.8 < z < 3.8. The first bin has a radius of 700 kp c and the other bins are of the same area as the first. The error bars show the Poisson error on the number counts. The dashed line shows the subtracted lo cal background level (zero level) determined from an annulus of 2Mpc2 (400 ) from the QSOs. The dotted line shows, for comparison, the global background as determined from taking the average source density in large apertures over the SERVS fields. This is the source density before being corrected for completeness. Figure from Falder et al. (2011).

cessfully perform these experiments (Padmanabhan et al. 2009). SERVS is able to take these studies to z >> 1. One particular area where SERVS is uniquely valuable is in determining the environments of high redshift AGN. Falder et al. (2011) find significant (> 4 ) overdensity of galaxies around QSOs in a redshift bin centered on z 2.0 and an (> 2 ) overdensity of galaxies around QSOs in a redshift bin centered on z 3.3 (see Figure 20). Nielsen et al. (2012, in prep.) are investigating the environments of AGN and quasars selected in the midinfrared. For the first time the environments of luminous < quasars at 0.8 < z 3 are being characterized, enabling a comparison of the environments of dust obscured and normal quasars at these redshifts.
6.4. High-z quasar searches

The unique multi-band SERVS dataset will be a valuable tool for constraining the faint end of the quasar luminosity function at high redshifts. Quasar searches have been or will be carried out in the SERVS fields using the combination of SERVS, DXS, VIDEO and SpARCS data, in addition to the SERVS CTIO and Subaru ancillary data. This large range of wavelengths allows for the rejection of many contaminants of high-z quasar searches on the basis of near-infrared photometry alone. Current estimates of the high-z quasar luminosity function (e.g. Willott et al. 2010) suggests somewhere between 3-14 quasars at 5.5 < z < 6.5 and 1-5 quasars at 6.5 < z < 7.5. The large range is due to the uncertainty


SERVS: survey definition and goals

17

masses for 12000 sources. This will be sufficient to study trends in star formation rate with stellar mass and redshift, for example, to test the idea of "downsizing" of the most actively star-forming galaxies.
7. SUMMARY

Fig. 21.-- Two representative infrared-faint radio sources (IFRS). The greyscale is the [3.6] SERVS data, and the contours are the 20 cm image, with contour levels of (1, 2, 3, 4, 5) mJy/b eam. The left hand image is a non-detection(CS0194) and the right-hand image is a candidate detection (CS0114), Figure 2. of Norris et al. (2011a).

in the faint-end slope of the quasar luminosity function, and SERVS will be able to constrain this well.
6.5. Infrared-faint radio sources

Norris et al. (2006) describe the discovery of infraredfaint radio sources (IFRS), a population of radio sources with host galaxy fluxes well below the limit of the SWIRE survey. Huynh et al. (2010) used deep IRAC data to place even more stringent limits on them, and concluded that these are most likely radio-loud AGN with faint host galaxies, but the sample to date is small. Norris et al. (2011a) present an initial study of this hitherto unsuspected population with SERVS, including stacking of ob jects that are too faint to be detected, even in SERVS, and which may represent a very high redshift radio-loud galaxies, possibly suffering from significant dust extinction (see Figure 21). Using a combination of SERVS data and GMRT/VLA radio observations of the Lockman Hole at 610 MHz and 1.4 GHz, Afonso et al. (2011) study a sample of Ultra Steep Spectrum (USS) radio sources and suggest the likely existence of higher redshifts among the sub-mJy USS population, raising the possibility that the high efficiency of the USS technique for the selection of high redshift sources remains even at the sub-mJy level.
6.6. Obscured star formation Although SERVS cannot be used as a direct indicator of obscured star formation on its own, overlap with surveys by SCUBA-2 (S2CLS) and Herschel (HerMES) will allow more reliable source identification than possible using shorter wavelength data, and better characterization of any extinction of the stellar light, as well as the stellar mass of the galaxy. Based on recent simulations of Herschel observations, we expect to detect 700 unconfused sources per deg2 in 3 Herschel bands (FernandezConde et al. 2008). At the SERVS depths, we expect to detect > 95% of these sources in both IRAC bands, and thus, with the aid of our ancillary optical and nearinfrared data, obtain photometric redshifts and stellar

The Spitzer Extragalactic Representative Survey (SERVS) is designed to open up a medium-depth, medium area part of parameter space in the nearinfrared, covering 18 deg2 to 2 µJy in the Spitzer [3.6] and [4.5] bands in five highly observed astronomical fields (EN1, ES1, Lockman Hole, CDFS and XMM-LSS). The five SERVS fields are centered on or close to those of corresponding fields surveyed by the shallower SWIRE fields, and overlap with several other ma jor surveys covering wavelengths from the X-ray to the radio. Of particular importance is near-infrared data, as these allow accurate photometric redshifts to be obtained. SERVS overlaps exactly with the 12 deg2 of the VISTA VIDEO survey in the South, and is covered by the UKIDSS DXS survey in the North. SERVS also has good overlap with HerMES in the far-infrared, which covers SWIRE and other Spitzer survey fields, with deeper subfields within many of the SERVS fields. Sampling 0.8Gpc3 and redshifts from 1 to 5, the survey is large enough to contain significant numbers of rare ob jects, such as luminous quasars, ULIRGs, radio galaxies and galaxy clusters, while still being deep enough to find L galaxies out to z 4. In this paper, we have described the Spitzer observations, the data processing as well as the wealth of ancillary data available in the fields covered by SERVS. Mosaics and catalogs will be made available to the community in the summer of 2012 through the Infrared Science Archive (IRSA).

8. ACKNOWLEDGEMENTS

This work is based on observations made with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Support for this work was provided by NASA through an award issued by JPL/Caltech. J.A., H.M., M.G. and L.B. gratefully acknowledge support from the Science and Technology Foundation (FCT, Portugal) through the research grant PTDC/FIS/100170/2008 and the Fellowships SFRH/BD/31338/2006 (HM) and SFRH/BPD/62966/2009 (L.B.). G.W. gratefully acknowledges support from NSF grant AST-0909198. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint pro ject of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation.


18

Mauduit et al.
APPENDIX

NUMBER COUNTS FOR THE EN1, ES1, LOCKMAN & CDFS FIELDS

Number counts were derived for the five fields. Source colors were also compared to 2MASS (see details in Section 4.2). We present here the four remaining fields: EN1, ES1, Lockman and CDFS. All of them show consistent number count features and completeness levels. SERVS bright stars colors for all of the fields are coherent with 2MASS.

Fig. A.1.-- Left side : SERVS numb er counts versus flux at [3.6] and [4.5] for the EN1 (top ) and ES1 (bottom ) fields. Grey dashed lines show the selection of sources used in the b ottom plot (with fluxes as 0.3 < f < 2mJy). Right side : Color-color plot showing K2MASS - M3.6µm (Vega) versus M3.6µm (Vega) - M4.5µm (Vega) for sources within the flux range defined by the grey dashed lines ab ove, plus a cut in stellarity index > 0.95 and the existence of a 2MASS K-band measurement as an additional constraint. Red dashed lines help pinp oint the lo cation of the (0,0) p oint in this diagram.


SERVS: survey definition and goals

19

Fig. A.2.-- Left side : SERVS number counts versus flux at [3.6] and [4.5] for the Lockman (top ) and CDFS (bottom ) fields. Grey dashed lines show the selection of sources used in the b ottom plot (with fluxes as 0.3 < f < 2mJy). Right side : Color-color plot showing K2MASS - M3.6µm (Vega) versus M3.6µm (Vega) - M4.5µm (Vega) for sources within the flux range defined by the grey dashed lines ab ove, plus a cut in stellarity index > 0.95 and the existence of a 2MASS K-band measurement as an additional constraint. Red dashed lines help pinp oint the lo cation of the (0,0) p oint in this diagram.

SEXTRACTOR & MOPEX PARAMETERS USED FOR THE IMAGE PROCESSING AND THE CATALOGS EXTRACTION

Mopex was used to process and coadd the raw images and Sextractor to extract information on the sources within. In Table A.1 we list the core parameters used in both.


20

Mauduit et al.
TABLE A.1 Values of the more important Mopex & SExtractor parameters Program (module) Mopex (DETECT) Mopex (DETECT) Mopex (DETECT) Mopex (MOSAICINT) Mopex (MOSAICDUALOUTLIER) Mopex (MOSAICDUALOUTLIER) Mopex (MOSAICOUTLIER) Mopex (MOSAICOUTLIER) Mopex (MOSAICOUTLIER) Mopex (MOSAICOUTLIER) Mopex (MOSAICRMASK) Mopex (MOSAICRMASK) SExtractor SExtractor SExtractor SExtractor SExtractor SExtractor SExtractor SExtractor SExtractor SExtractor SExtractor Parameter Detection Max Area Detection Min Area Detection Threshold INTERP METHOD MIN OUTL IMAGE MIN OUTL FRAC THRESH OPTION BOTTOM THRESHOLD TOP THRESHOLD MIN PIX NUM MIN COVERAGE MAX COVERAGE DETECT MINAREA DETECT THRESH ANALYSIS THRESH FILTER DEBLEND NTHRESH DEBLEND MINCONT SEEING FWHM BACK SIZE BACK FILTERSIZE BACKPHOTO TYPE WEIGHT TYPE Value 100 0 4 1 2 0.51 1 0 0 3 4 100 5.0 0.4 0.4 Default 64 0.005 2 .0 32 5 LOCAL MAP WEIGHT



Notes: Similar values can be found in Lacy et al. (2005) and Ashby et al. (2009) Using coverage map as inverse variance weight map

REFERENCES Abaza jian, K. N., et al. 2009, ApJS, 182, 543 Afonso, J., et al. 2011, ArXiv e-prints Ashby, M. L. N., et al. 2009, ApJ, 701, 428 Berta, S., et al. 2006, A&A, 451, 881 --. 2008, A&A, 488, 533 Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 Bolzonella, M., Miralles, J.-M., & Pell´ R. 2000, A&A, 363, 476 o, Bondi, M., et al. 2007, A&A, 463, 519 Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503 Brown, M. J. I., Dey, A., Jannuzi, B. T., Brand, K., Benson, A. J., Brodwin, M., Croton, D. J., & Eisenhardt, P. R. 2007, ApJ, 654, 858 Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000 Calzetti, D., Armus, L., Bohlin, R. C., Kinney, A. L., Koornneef, J., & Storchi-Bergmann, T. 2000, ApJ, 533, 682 Capak, P. L., et al. 2011, Nature, 470, 233 Cardamone, C. N., et al. 2010, ApJS, 189, 270 Chary, R., et al. 2004, ApJS, 154, 80 Cimatti, A., et al. 2008, A&A, 482, 21 Coil, A. L., et al. 2011, ApJ, 741, 8 Cool, R. J., et al. 2008, ApJ, 682, 919 Damen, M., Labb´ I., Franx, M., van Dokkum, P. G., Taylor, e, E. N., & Gawiser, E. J. 2009, ApJ, 690, 937 Demarco, R., et al. 2010, ApJ, 711, 1185 Dickinson, M., Giavalisco, M., & GOODS Team. 2003, in The Mass of Galaxies at Low and High Redshift, ed. R. Bender & A. Renzini, 324 Falder, J. T., et al. 2011, ApJ, 735, 123 Farrah, D., Geach, J., Fox, M., Serjeant, S., Oliver, S., Verma, A., Kaviani, A., & Rowan-Robinson, M. 2004, MNRAS, 349, 518 Farrah, D., et al. 2012, ApJ, 745, 178 Fazio, G. G., et al. 2004, ApJS, 154, 10 Fernandez-Conde, N., Lagache, G., Puget, J.-L., & Dole, H. 2008, A&A, 481, 885 Feruglio, C., et al. 2008, A&A, 488, 417 Furusawa, H., et al. 2008, ApJS, 176, 1 Garn, T., Green, D. A., Riley, J. M., & Alexander, P. 2008a, MNRAS, 383, 75 --. 2008b, MNRAS, 387, 1037 Garn, T. S., Green, D. A., Riley, J. M., & Alexander, P. 2010, Bulletin of the Astronomical Society of India, 38, 103 Glazebro ok, K., Peaco ck, J. A., Collins, C. A., & Miller, L. 1994, MNRAS, 266, 65 Gonz´ alez-Solares, E. A., et al. 2011, MNRAS, 416, 927 Grant, J. K., Taylor, A. R., Stil, J. M., Landecker, T. L., Kothes, R., Ransom, R. R., & Scott, D. 2010, ApJ, 714, 1689 Grogin, N. A., et al. 2011, ApJS, 197, 35 Guo, Q., et al. 2011, MNRAS, 413, 101 Henriques, B., Maraston, C., Monaco, P., Fontanot, F., Menci, N., De Lucia, G., & Tonini, C. 2011, MNRAS, 907 Hildebrandt, H., et al. 2006, A&A, 452, 1121 Hopkins, P. F., Hernquist, L., Cox, T. J., Di Matteo, T., Robertson, B., & Springel, V. 2006, ApJS, 163, 1 Hopkins, P. F., Lidz, A., Hernquist, L., Coil, A. L., Myers, A. D., Cox, T. J., & Sp ergel, D. N. 2007, ApJ, 662, 110 Huynh, M. T., Norris, R. P., Siana, B., & Middelberg, E. 2010, ApJ, 710, 698 Ibar, E., Ivison, R. J., Biggs, A. D., Lal, D. V., Best, P. N., & Green, D. A. 2009, MNRAS, 397, 281 Ilb ert, O., et al. 2006, A&A, 453, 809 --. 2009, ApJ, 690, 1236 Iovino, A., et al. 2005, A&A, 442, 423 Jonas, J. 2009, in Proceedings of the IEEE, 1522­1530 Lacy, M., Canalizo, G., Rawlings, S., Sa jina, A., Storrie-Lombardi, L., Armus, L., Marleau, F. R., & Muzzin, A. 2005, Mem. So c. Astron. Italiana, 76, 154 Lacy, M., Petric, A. O., Sa jina, A., Canalizo, G., Storrie-Lombardi, L. J., Armus, L., Fadda, D., & Marleau, F. R. 2007, AJ, 133, 186 Lawrence, A., et al. 2007, MNRAS, 379, 1599 Le F` e, O., et al. 2005, A&A, 439, 845 evr Lehmer, B. D., et al. 2005, ApJS, 161, 21 Lonsdale, C. J., et al. 2003, PASP, 115, 897 Maraston, C. 2005, MNRAS, 362, 799


SERVS: survey definition and goals
Maraston, C., Daddi, E., Renzini, A., Cimatti, A., Dickinson, M., Pap ovich, C., Pasquali, A., & Pirzkal, N. 2006, ApJ, 652, 85 Middelberg, E., et al. 2008, AJ, 135, 1276 Miller, N. A., Fomalont, E. B., Kellermann, K. I., Mainieri, V., Norman, C., Padovani, P., Rosati, P., & Tozzi, P. 2008, ApJS, 179, 114 Muzzin, A., et al. 2009, ApJ, 698, 1934 --. 2011, ArXiv e-prints Norris, R. P., et al. 2006, AJ, 132, 2409 --. 2011a, ApJ, 736, 55 --. 2011b, PASA, 28, 215 Oliver, S. J., et al. 2012, ArXiv e-prints Owen, F. N., & Morrison, G. E. 2008, AJ, 136, 1889 Padmanabhan, N., White, M., Norb erg, P., & Porciani, C. 2009, MNRAS, 397, 1862 Petric, A. 2010, in American Institute of Physics Conference Series, Vol. 1240, American Institute of Physics Conference Series, ed. V. P. Debattista & C. C. Pop escu, 76­79 Pierre, M., et al. 2007, MNRAS, 382, 279 R¨ ottgering, H., et al. 2011, Journal of Astrophysics and Astronomy, 32, 557 Rowan-Robinson, M., et al. 2008, MNRAS, 386, 697 Sanders, D. B., et al. 2007, ApJS, 172, 86 Schawinski, K., Thomas, D., Sarzi, M., Maraston, C., Kavira j, S., Joo, S.-J., Yi, S. K., & Silk, J. 2007, MNRAS, 382, 1415

21

Silva, L., Granato, G. L., Bressan, A., & Danese, L. 1998, ApJ, 509, 103 Simpson, C., et al. 2006, MNRAS, 372, 741 Skrutskie, M. F., et al. 2006, AJ, 131, 1163 Smith, A. J., et al. 2012, MNRAS, 419, 377 Springel, V., et al. 2005, Nature, 435, 629 Surace, J. A., Shup e, D. L., Fang, F., Evans, T., Alexov, A., Frayer, D., Lonsdale, C. J., & SWIRE Team. 2005, in Bulletin of the American Astronomical Society, Vol. 37, American Astronomical Society Meeting Abstracts, 1246 Surace, J. A., et al. 2004, VizieR Online Data Catalog, 2255, 0 Temporin, S., et al. 2008, A&A, 482, 81 Trichas, M., et al. 2010, MNRAS, 405, 2243 Ueda, Y., et al. 2008, ApJS, 179, 124 van Dokkum, P. G., et al. 2006, ApJ, 638, L59 --. 2010, ApJ, 709, 1018 Weiú, A., et al. 2009, ApJ, 707, 1201 White, R. L., Becker, R. H., Helfand, D. J., & Gregg, M. D. 1997, ApJ, 475, 479 Wilkes, B. J., et al. 2009, ApJS, 185, 433 Wilson, G., et al. 2009, ApJ, 698, 1943 Wright, E. L., et al. 2010, AJ, 140, 1868