Euclid preparation : XVII. Cosmic Dawn Survey : Spitzer Space Telescope observations of the Euclid deep fields and calibration fields

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Euclid preparation : XVII. Cosmic Dawn Survey : Spitzer Space Telescope observations of the Euclid deep fields and calibration fields © Euclid Collaboration 2022 Published version Euclid Collaboration Euclid Collaboration. (2022). Euclid preparation : XVII. Cosmic Dawn Survey : Spitzer Space Telescope observations of the Euclid deep fields and calibration fields. Astronomy and Astrophysics, 658, Article A126. https://doi.org/10.1051/0004-6361/202142361 2022 A&A 658, A126 (2022) https://doi.org/10.1051/0004-6361/202142361 c Euclid Collaboration 2022 Astronomy & Astrophysics Euclid preparation XVII. Cosmic Dawn Survey: Spitzer Space Telescope observations of the Euclid deep fields and calibration fields Euclid Collaboration: A. Moneti1, H. J. McCracken1,2, M. Shuntov1, O. B. Kauffmann3, P. Capak4, I. Davidzon4, O. Ilbert3, C. Scarlata5, S. Toft6,7, J. Weaver4, R. Chary8, J. Cuby3, A.L. Faisst9, D. C. Masters9, C. McPartland4,10,11, B. Mobasher10, D. B. Sanders11, R. Scaramella12,13, D. Stern14, I. Szapudi11, H. Teplitz8, L. Zalesky11, A. Amara15, N. Auricchio16, C. Bodendorf17, D. Bonino18, E. Branchini19,20, S. Brau-Nogue21, M. Brescia22, J. Brinchmann23,24, V. Capobianco18, C. Carbone25, J. Carretero26,27, F. J. Castander28,29, M. Castellano13, S. Cavuoti22,30,31, A. Cimatti32,33, R. Cledassou34,35, G. Congedo36, C. J. Conselice37, L. Conversi38,39, Y. Copin40, L. Corcione18, A. Costille3, M. Cropper41, A. Da Silva42,43, H. Degaudenzi44, M. Douspis45, F. Dubath44, C. A. J. Duncan46, X. Dupac39, S. Dusini47, S. Farrens48, S. Ferriol40, P. Fosalba28,29, M. Frailis49, E. Franceschi16, M. Fumana25, B. Garilli25, B. Gillis36, C. Giocoli50,51, B. R. Granett52, A. Grazian53, F. Grupp17,54, S. V. H. Haugan55, H. Hoekstra56, W. Holmes14, F. Hormuth57,58, P. Hudelot1, K. Jahnke58, S. Kermiche59, A. Kiessling14, M. Kilbinger48, T. Kitching41, R. Kohley39, M. Kümmel54, M. Kunz60, H. Kurki-Suonio61, S. Ligori18, P. B. Lilje55, I. Lloro62, E. Maiorano16, O. Mansutti49, O. Marggraf63, K. Markovic14, F. Marulli16,64,65, R. Massey66, S. Maurogordato67, M. Meneghetti9,16,65, E. Merlin13, G. Meylan68, M. Moresco16,64, L. Moscardini16,64,65, E. Munari49, S. M. Niemi69, C. Padilla27, S. Paltani44, F. Pasian49, K. Pedersen70, S. Pires48, M. Poncet35, L. Popa71, L. Pozzetti16, F. Raison17, R. Rebolo72,73, J. Rhodes14, H. Rix58, M. Roncarelli16,64, E. Rossetti64, R. Saglia17,54, P. Schneider63, A. Secroun59, G. Seidel58, S. Serrano28,29, C. Sirignano47,74, G. Sirri65, L. Stanco47, P. Tallada-Crespí26,75, A. N. Taylor36, I. Tereno42,76, R. Toledo-Moreo77, F. Torradeflot26,75, Y. Wang8, N. Welikala36, J. Weller17,54, G. Zamorani16, J. Zoubian59, S. Andreon52, S. Bardelli16, S. Camera18,78,79, J. Graciá-Carpio17, E. Medinaceli50, S. Mei80, G. Polenta81, E. Romelli49, F. Sureau48, M. Tenti65, T. Vassallo54, A. Zacchei49, E. Zucca16, C. Baccigalupi49,82,83,84, A. Balaguera-Antolínez72,73, F. Bernardeau85, A. Biviano49,82, M. Bolzonella16, E. Bozzo44, C. Burigana86,87,88, R. Cabanac21, A. Cappi16,67, C. S. Carvalho76, S. Casas48, G. Castignani16,64, C. Colodro-Conde73, J. Coupon44, H. M. Courtois89, D. Di Ferdinando65, M. Farina90, F. Finelli86,91, P. Flose-Reimberg1, S. Fotopoulou92, S. Galeotta49, K. Ganga80, J. Garcia-Bellido93, E. Gaztanaga28,29, G. Gozaliasl94,95, I. Hook96, B. Joachimi97, V. Kansal48, E. Keihanen95, C. C. Kirkpatrick61, V. Lindholm95,98, G. Mainetti99, D. Maino25,100,101, R. Maoli13,102, M. Martinelli93, N. Martinet3, M. Maturi103,104, R. B. Metcalf64,91, G. Morgante16, N. Morisset44, A. Nucita105,106, L. Patrizii65, D. Potter107, A. Renzi47,74, G. Riccio22, A. G. Sánchez17, D. Sapone108, M. Schirmer58, M. Schultheis67, V. Scottez1, E. Sefusatti49,82,84, R. Teyssier107, O. Tubio73, I. Tutusaus28,29, J. Valiviita98,109, M. Viel49,82,83,84, and H. Hildebrandt110 (Affiliations can be found after the references) Received 4 October 2021 /Accepted 21 November 2021 ABSTRACT We present a new infrared survey covering the three Euclid deep fields and four other Euclid calibration fields using Spitzer Space Telescope’s Infrared Array Camera (IRAC). We combined these new observations with all relevant IRAC archival data of these fields in order to produce the deepest possible mosaics of these regions. In total, these observations represent nearly 11 % of the total Spitzer Space Telescope mission time. The resulting mosaics cover a total of approximately 71.5 deg2in the 3.6 and 4.5 µm bands, and approximately 21.8 deg2in the 5.8 and 8 µm bands. They reach at least 24 AB magnitude (measured to 5σ, in a 200 .5 aperture) in the 3.6 µm band and up to ∼5 mag deeper in the deepest regions. The astrometry is tied to the Gaia astrometric reference system, and the typical astrometric uncertainty for sources with 16 <[3.6] <19 is .000 .15. The photometric calibration is in excellent agreement with previous WISE measurements. We extracted source number counts from the 3.6 µm band mosaics, and they are in excellent agreement with previous measurements. Given that the Spitzer Space Telescope has now been decommissioned, these mosaics are likely to be the definitive reduction of these IRAC data. This survey therefore represents an essential first step in assembling multi-wavelength data on the Euclid deep fields, which are set to become some of the premier fields for extragalactic astronomy in the 2020s. Key words. cosmology: observations – large-scale structure of Universe – dark energy – dark matter – Galaxy: formation – surveys Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A126, page 1 of 15 A&A 658, A126 (2022) 1. Introduction The Euclid mission will survey 15 000 deg2of the extragalactic sky to investigate the nature of dark energy and dark matter and to study the formation and evolution of galaxies (Laureijs et al. 2011). To this end, Euclid will obtain high resolution and high signal-to-noise imaging of a billion galaxies in a broad optical filter to measure their shapes, and in three near-infrared (NIR) filters to measure their colours. It will also obtain high signal-tonoise NIR spectroscopy of about thirty million of these galaxies to measure abundances and redshifts. Additionally, photometric redshifts will be determined by combining the Euclid data with optical photometry from external surveys. To reach the required precision on cosmological parameters and satisfy the stringent mission requirements on completeness and spectroscopic purity and to shape noise bias, Euclid must also obtain observations with a 40 times longer exposure per pixel than the main survey over regions covering at least 40 deg2. To this end, three ‘deep’ fields were selected by the Euclid Consortium. They are described in detail in Scaramella et al. (in prep.), which gives references to prior studies; we give just a very brief description here. They are as follows: (1) Euclid Deep Field North (EDF-N), a roughly circular, 10 deg2region centred on the well-studied north Ecliptic pole; (2) Euclid Deep Field Fornax (EDF-F), also roughly circular and of 10 deg2, centred on the Chandra Deep Field South (CDF-S, Mauduit et al. 2012; Lacy et al. 2021), and including the GOODS-S (Giavalisco et al. 2004) and the Hubble Ultra Deep Field (HUDF, Beckwith et al. 2006); and (3) the Euclid Deep Field South (EDF-S), a pillshaped area of 20 deg2with no previous dedicated observations. In addition to these three deep fields, Euclid will observe several fields for the calibration of photometric redshifts (photo-z). These fields need to be observed to a level five times deeper than the main survey, and they are centred on some of the best studied extragalactic survey fields that already have extensive spectroscopic data: (1) the COSMOS field (Scoville et al. 2007;Sanders et al. 2007), (2) the Extended Groth Strip (EGS, Grogin et al. 2011), (3) the Hubble Deep Field North (HDF, also GOODS-N), and (4) the XMM-Large Scale Structure Survey field, which includes the Subaru XMM Deep Survey field (SXDS, Lonsdale et al. 2003;Mauduit et al. 2012;Lacy et al. 2021), and the VIMOS VLT Deep Survey (VVDS)1. While Euclid will observe these fields primarily for calibration purposes, those observations will provide an unprecedented data set to study galaxies at faint magnitudes and high redshifts. The survey efficiency of Euclid in the NIR bands is orders of magnitude greater than that of ground-based telescopes (e.g. VISTA). The Euclid deep fields alone will be 30 times larger and one magnitude deeper than the latest UltraVISTA data release (Moneti et al. 2019) covering the COSMOS field and will reach a depth of 26 mag in the Y,J, and Hfilters (5σ). In addition, Euclid carries a wide-field near-infrared grism spectrograph, the Near Infrared Spectrometer and Photometer (NISP), covering the 0.92 < λ < 1.85 µm region, which will provide multiple spectra at numerous grism orientations for more than one million sources to a line flux limit similar to 3D-HST (Brammer et al. 2012) and over an area 200 times larger than the COSMOS field (depending on the scheduling of the blue grism observations). The observations of the deep fields will result in the most complete and deepest spectroscopic coverage produced by Euclid. Such a spectroscopic data set will be unique for the reconstruc1These are now known as the “Euclid Auxiliary Fields” in Euclid terminology. tion of the galaxy environment at cosmic noon and for measuring the star formation rate from the Hαemission line intensity. The deep and wide NIR data from Euclid are also ideal for detecting significant numbers of high-redshift (7 <z<10) galaxies, as the Lyman αline is redshifted out of the optical into the NIR. However, in order to distinguish galaxy candidates from stars (primarily brown dwarfs), faint Balmer-break galaxies, and dusty star-forming galaxies at lower redshifts, which can all have similar NIR magnitudes and colours, deep optical, and mid-infrared (MIR) data are also needed (Bouwens et al. 2019; Bridge et al. 2019;Bowler et al. 2020). The Cosmic Dawn Survey (Toft et al., in prep.) aims to obtain uniform, multi-wavelength imaging of the Euclid deep and calibration fields to limits matching the Euclid data for the characterisation of high-redshift galaxies. The optical data will be provided by the Hawaii-Two-0 Subaru telescope/HyperSuprimeCam (HSC) survey (McPartland et al., in prep.) for the EDF-N and EDF-F, and likely by the Vera C. Rubin Observatory for EDF-S and EDF-F. For the COSMOS and SXDS fields, optical data are provided by the Subaru HSC Strategic programme (HSC-SSP, Aihara et al. 2011). In this paper, we present the Spitzer Space Telescope (Werner et al. 2004) component of the Cosmic Dawn Survey, which consists of (1) observing the full extent of the deep fields at 3.6 and 4.5 µm (only small parts of the EDF-N and EDF-F had already been observed with IRAC, see above), and (2) processing the new observations together with all relevant archival IRAC data, thus including data at 5.8 and 8.0 µm obtained during the cryogenic mission, and tying them to the latest Gaia astrometric reference system. In this way, we strive to produce the deepest possible and most modern MIR images (mosaics) of the deep and calibration fields. In addition to being essential for the identification of highredshift galaxies, MIR data are crucial for revealing the stellar mass content of the high-redshift Universe (which is outside the scope of Euclid core science). The Euclid data alone are not sufficient to characterise the stellar masses at z>3.5, as the Balmer break is redshifted out of the reddest band of the NISP. Without MIR data, the interpretation of spectral energy distributions (SEDs) would rely on rest-frame ultraviolet emission which is strongly affected by dust attenuation and dominated by stellar light of new-born stars. Therefore, integrated quantities like the stellar mass would be highly unreliable (Bell & de Jong 2001). Moreover, photometric redshifts would be prone to catastrophic failures resulting from the misidentification of the Lyman and Balmer breaks (e.g. Le Fèvre et al. 2015;Kauffmann et al. 2020). In summary, Spitzer Space Telescope/IRAC data are crucial for identifying the most distant objects (e.g. Bridge et al. 2019), for improving the accuracy of their photometric redshifts, and deriving their physical properties such as stellar masses, dust content, age, and star-formation rate from population synthesis models (e.g. Pérez-González et al. 2008;Caputi et al. 2015; Davidzon et al. 2017). The build-up of stellar mass, especially when confronted with the amount of matter residing in dark matter halos at high redshifts can be a highly discriminating test for galaxy formation models (Legrand et al. 2019). The extrapolation of recent work in the COSMOS field (Bowler et al. 2020) suggests that hundreds of the rarest, brightest z>7 galaxies are expected to be discovered in the Euclid deep fields. These provide unique constraints on cosmic reionisation, as the brightest galaxies form in the highest density regions of the Universe, which are expected to be the sites of the first generation of stars and galaxies, and thus of reionisation bubbles (Trac et al. 2008). A126, page 2 of 15 Euclid Collaboration: Euclid preparation. XVII. 2004.0 2006.0 2008.0 2010.0 2012.0 2014.0 2016.0 2018.0 Date of observation 0 200 400 600 800 Integration time (hr) warmcryogenic Fig. 1. Histogram of the exposure time of the data analysed here (including the few discarded observations) using bins of 30 days. Our dedicated observations began in November 2016 and comprise most of the data after that date. The red part of each bar accounts for observations in channels 3 and 4, and the blue part accounts for those in channels 1 and 2; the vertical dotted line at 2009.37 indicates the end of the cryogenic mission. No observations are made in channel 3 and 4 after the end of the cryogenic mission. 2. Observations All observations described here were made with IRAC. In brief, IRAC is a four-channel array camera on the Spitzer Space Telescope, simultaneously observing four fields slightly separated on the sky at 3.6, 4.5, 5.8, and 8.0µm, known as channels 1– 4, respectively. Spitzer Space Telescope science observations began in August 2003, but observations in channels 3 and 4 ceased once the on-board cryogen was exhausted (May 15, 2009). During the following ‘warm mission’ phase, channels 1 and 2 continued to operate until the end of operations in late January 2020, albeit with somewhat lower, but still comparable, performance. The earliest observations presented here are archival observations that were obtained in September 2003; the observations of the dedicated Capak programme began in 2017, and the ones of the dedicated Scarlata programme began in 2019. The dedicated observations continued until January 2020, shortly before the shutdown of the satellite. Figure 1shows a histogram of the integration time accumulated in bins of 30 days over the observing period. These observations account for almost 1.5 million frames, a total integration time of 34 000 h (all channels combined), and a total on-target time, omitting overheads, of just over 15 600 h, or nearly 1.8 yr, which is approximately 11% of the Spitzer Space Telescope mission time. For our dedicated observations of EDF-N, EDF-S, and EDFF we adopted a consistent observing strategy that comprises blocks of 3 ×3 maps with a step size of 31000, a large three-point dither pattern, and four repeats per position. Each block covers a 150 .1×150 .1 region with a coverage of 3 ×4×100 s exposures per pixel. The block centres are offset between passes in order to ensure uniform coverage and enable self-calibration. Each block forms an astronomical observation request (AOR), in IRAC jargon. All other data included in our processing are archival data. They were obtained with a variety of observing strategies that we did not investigate in detail and that we do not attempt to summarise here. In Appendix C, we list the programme IDs of all the observations processed; the ones of our dedicated observations are shown in bold. The combination of the archival data and our own dedicated data produces a spatially variable depth in most fields; this is discussed further in Sect. 4. All observations are summarised in Table 1, which gives, for each field and channel, the number of frames (data collection events – DCEs – in IRAC terminology) used to produce the mosaics (we note that this can be lower than the number of frames downloaded as some were discarded; see Sect. 3) together with the total observing time. For channels 1 and 2, on the left side of the table, the information is sub-divided into the cryogenic part and the warm part of the mission. 3. Processing 3.1. Pre-processing and calibration Processing begins with the Level 1 data products generated by the Spitzer Science Center via their ‘basic calibrated data’ pipeline (Lowrance et al. 2016), which were downloaded from the NASA/IPAC Infrared Science Archive (IRSA2). They have had all well-understood instrumental signatures removed, have been flux-calibrated in units of MJy sr−1, and are delivered with an uncertainty image and a mask image; they are described in detail in the IRAC Instrument Handbook3. More precisely, we begin from the ‘corrected basic calibration data’ products, which have file extensions .cbcd for the image, .cbunc for the uncertainty, and .bimsk for the mask. The files are grouped by AORs, namely sets of a few to several hundred DCEs obtained sequentially. All frames are 256 ×256 pixels, the pixels are 100 .2 wide, and the image file header contains the photometric solution and an initial astrometric solution. The processing was done region by region. A first pass over the files is used to check the headers for completeness and to discard a few incomplete AORs, which accounts for most of the differences in the number of frames listed in Table 1between channels 1 and 2 or 3 and 4. This is followed by the correction of the ‘first frame’ bias effect4. Next, the positions and magnitudes of WISE (Wright et al. 2010;Mainzer et al. 2011) and Gaia DR2 (Gaia Collaboration 2018) sources falling within the field are downloaded. The Gaia sources are first ‘projected’ to their location at the time of the observations using the Gaia 2https://irsa.ipac.caltech.edu 3https://irsa.ipac.caltech.edu/data/SPITZER/docs/ irac/iracinstrumenthandbook/home 4https://irsa.ipac.caltech.edu/data/SPITZER/docs/ irac/iracinstrumenthandbook/26/ A126, page 3 of 15 A&A 658, A126 (2022) Table 1. Valid observations. Field Ch. Cryo Warm Total Ch. Total Num Time Num Time Num Time Num Time (h) (h) (h) (h) EDF-N 1 5859 52 113 521 2380 119 380 2432 3 5856 52 EDF-N 2 5857 52 113 204 2467 119 061 2519 4 7667 50 EDF-F 1 14 299 363 105 781 2672 120 080 3035 3 14 301 363 EDF-F 2 14 299 363 105 779 2764 120 078 3127 4 29 686 352 EDF-S 1 ... ... 21 982 534 21 982 534 3 ... ... EDF-S 2 ... ... 21 982 552 21 982 552 4 ... ... COSMOS 1 7014 185 191 072 4886 198 086 5071 3 7011 185 COSMOS 2 7013 185 191 031 5052 198 044 5237 4 13 894 179 EGS 1 4673 192 44 101 551 48 774 743 3 4 672 192 EGS 2 4673 192 44 101 569 48 774 761 4 14 535 186 HDFN 1 6253 298 36 485 930 42 738 1228 3 6252 298 HDFN 2 6253 298 36 485 962 42 738 1260 4 22 496 288 XMM 1 10 264 154 98 027 2410 108 291 2564 3 10 265 154 XMM 2 10 265 154 98 030 2495 108 295 2649 4 14 321 151 Notes. Here, ‘Num’ is the number of frames used, and ‘Time’ is the total integration time, in hours, they contribute. The left part of the table is for channels 1 and 2, split between cryogenic and warm mission, the right part is for channels 3 and 4, which were used during the cryogenic mission only. We note that the EDF-S field was observed only during the warm mission. proper motions. Next, they are identified on each IRAC frame, their observed fluxes and positions are determined in each frame using the APEX software (the point-source extractor in MOPEX5) in forced-photometry mode, and the positions are used to update the astrometric solution of each frame. There are typically 30–40 Gaia DR2 sources available for each frame. In channels 1 and 2, most of them are detected and used for the astrometric correction. In the longer-wavelength channels, 3 and 4, only a few sources in total are detected and usable, but that is still sufficient to determine an astrometric solution with negligible distortion as shown in Sect. 4.2. An attempt was made to subtract bright stars in order to recover faint sources in their wings. For each AOR, a model star built from the template PSFs described in the IRAC Instrument Handbook6(see Fig. 4.9 there) is scaled to the median of the fluxes of the star measured in that AOR, and it is subtracted from each frame (of the AOR). Different templates are available for each filter and separately for the cryogenic and the warm missions. While this procedure worked quite well for moderately bright stars (which are of course the vast majority and which represent only a small loss in area), it introduced significant artefacts around the (few) very bright stars in the final mosaics. These artefacts included diffraction spikes corrected only out to a certain distance (out to where the template extends beyond the frame), other edge effects, and the subtraction of the core of bright galaxies. For these reasons, the bright-star subtraction was not performed and the bright stars are left as they are. 3.2. Stacking and image combination In the next step, we computed a median image for all frames within an AOR, which corrects for persistence in the detectors and also for any residual first-frame pattern that introduces struc5https://irsa.ipac.caltech.edu/data/SPITZER/docs/ dataanalysistools/tools/mopex/ 6https://irsa.ipac.caltech.edu/data/SPITZER/docs/ irac/iracinstrumenthandbook/19/ ture in the background. In parallel, a background map is also created by iteratively clipping objects and masking them, and finally that background is subtracted from each frame of the AOR. The final processing steps consist of resampling the background-subtracted frames onto a common grid with a scale of 000 .6 pix−1, that is, half the instrument pixel size, which covers all data in all channels and which is the same in all channels. We experimented with two MOPEX interpolation schemes to produce our final mosaics. We first tried the “drizzling” (Fruchter & Hook 2002) scheme in which the final value of the output pixels is computed by considering the contribution of each input pixel in a smaller pixel grid in the output image. This procedure has excellent noise properties (it does not suffer from correlated pixels) when many input frames are available, but with few input frames it can produce artefacts in the output images. The second, simpler approach is to compute the value of each output pixel as a linear combination of the input pixel values. Although this procedure produces correlated noise, it works reliably for all the fields considered in this work, which can have widely varying numbers of input images. Noise correlations can be estimated through simulations or by comparing sources in our drizzled and non-drizzled images. These comparisons show that the linear interpolation procedure leads to an underestimation of aperture magnitude errors by 30−40%, while the magnitudes themselves are unaffected. Next, we used MOPEX to produce an average-combined image while rejecting outliers and excluding masked regions. Outlier rejection was done using the multi-frame temporal, dual outlier, and box outlier modules from MOPEX. We used the same parameters used by the Spitzer super mosaics, which were tuned to produce balanced outlier rejection for a wide range of data types and uses. The outlier rejection methods were then combined using the MosaicRMASK module. The specific parameters used can be found in Table 2. Frames with different exposure times are combined using the exposure time weighting feature in MOPEX. This appropriately scales the uncertainties and coverage maps to match the median exposure time before carrying out the combination. A126, page 4 of 15 Euclid Collaboration: Euclid preparation. XVII. Table 2. MOPEX outlier rejection parameters. Parameter name Value Temporal outlier: (&MOSAICOUTLIERIN) MIN_PIX_NUM 3 TOP_THRESHOLD 3.0 BOTTOM_THRESHOLD 3.0 THRESH_OPTION 1 Dual Outlier: (&MOSAICDUALOUTLIERIN) MAX_OUTL_FRAC 0.5 MAX_OUTL_IMAGE 1 Box Outlier: (&MOSAICBOXOUTLIERIN) BOX_Y 3 BOX_X 3 BOX_MEDIAN_BIAS 1 Mosaic RMASK: (&MOSAICRMASKIN) BOX_MIN_COVERAGE 1.0 BOX_BOTTOM_THRESHOLD 2.0 BOX_TOP_THRESHOLD 3.0 REFINE_OUTLIER_THRESH 1 REFINE_OUTLIER 0 RM_THRESH 0.5 MIN_COVERAGE 3 MAX_COVERAGE 100 TOP_THRESHOLD 3.0 BOTTOM_THRESHOLD 3.0 The stacking pipeline also produces the following ancillary characterisation maps: (1) an uncertainty map produced by stacking the input uncertainty maps using the same shifts as for the signal stack, (2) a coverage map giving the number of frames contributing to each pixel, and (3) an exposure time map giving the total exposure time per pixel. As the exposure times are not the same for all the observing programmes, these last two maps are not simply scaled versions of each other. 3.3. Spatial variation of the PSF in the stacks The observations described here were made at many different satellite position angles (PAs), and thus when the images are stacked they must be rotated back to north upwards. This has the effect of rotating the PSF, which is fixed in the satellite’s reference frame. Since the PSF is not rotationally symmetric, due in particular to the diffraction spikes, the stacked image of a star will depend on when it was observed. As all parts of the stack were not observed at the same time (or at the same PA), the PSF varies spatially in the stack. The COSMOS field, which is near the equator, was observable only at specific times and therefore with a very restricted range of PAs; the PSF in the COSMOS stacks is thus quite homogeneous. However, in the EDF-N, which was in a continuous viewing zone, observations were obtained at many different PAs, yielding a more complicated and more spatially variable PSF. This effect is very important for PSF-based photometry: the PSF at each position of the stack has to be reconstructed by stacking the nominal PSF at the PAs of the observations at that position, as did, for example, Labbé et al. (2015) for the GOODS-South and HUDF fields, and also Weaver et al. (2021, henceforth COSMOS2020) for the production of the COSMOS2020 catalogue. The latter used the PRFmap7code by Andreas Faisst. While such 7https://github.com/cosmic-dawn/prfmap −27.78◦ −27.79◦ −27.80◦ Ch1 Latitude Ch2 53.15◦53.14◦53.13◦ −27.78◦ −27.79◦ −27.80◦ Ch3 Longitude 53.15◦53.14◦53.13◦ Ch4 Fig. 2. Detail of EDF-F mosaic in region near that of maximum exposure time, which here is the same for all four channels. Images are 200 ×200 pixels, or 20×20. Display levels are −σto +8σ, where σ is the standard deviation of the sky pixels, which is ∼0.005 MJy sr−1for channels 1 & 2, and ∼0.013 MJy sr−1for channels 3 & 4. photometry is beyond the scope of this paper, we nevertheless provide, for each stack, a table of the PAs of each frame used in the stack. For completeness, those tables also contain the frame coordinates, the MJD of the observation, and the exposure time (see Appendix Afor more details). 3.4. Products As an example of the data quality, Fig. 2shows a zoomedin image of a section of the EDF-F mosaic in the four channels near the region of maximum coverage. We do not provide figures of the full mosaics here as they would be physically too small to show anything informative other than the overall coverage. Maps of the integration time per pixel for channels 1 and 3 of all the fields are presented in Appendix B. Since channel 2 is observed together with channel 1, and similarly for channels 4 and 3, the paired channels have very similar coverage, albeit slightly shifted in position. The 10 deg2circular area of EDF-N and EDF-F and the 20 deg2pill-shaped area of EDF-S are easily seen in those figures. Also, and with the exception of EDF-S, for which there are only observations done specifically for this programme and no archival data, the integration time per pixel, and consequently the depth reached, is far from uniform, with only a small part of the total area of each field having been observed for more than a few hours. In fact, the median integration time per pixel is greater than 1 h for only two fields. Table 3gives the median and maximum pixel integration time for each field and each channel. The variation of area covered as a function of exposure time for channels 1 and 3 and for all fields is shown graphically in Fig. 3, which presents a cumulative histogram of the area covered versus exposure time. The intersection of the curve with the vertical axis thus gives the total area covered for that field and these areas are also listed in Table 4. EDF-S is the most uniformly observed field and it covers the largest area, but it is A126, page 5 of 15 A&A 658, A126 (2022) Table 3. Median and maximum pixel integration time in hours. Field ch1 ch2 ch3 ch4 COSMOS 0.51 93.7 0.50 97.1 0.38 5.1 0.38 5.5 EDF-F 1.33 199.7 1.33 149.5 0.03 47.3 0.03 54.2 EDF-N 1.47 23.4 1.56 21.3 0.04 20.4 0.04 19.6 EDF-S 0.13 0.5 0.16 0.5 . . . . . . . . . . . . EGS 0.16 71.1 0.16 71.6 0.93 5.4 0.95 4.8 HDFN 0.16 236.2 0.16 224.4 0.13 91.2 0.13 95.2 XMM 0.31 65.9 0.33 67.1 0.04 2.0 0.04 2.0 0.01 0.1 1 10 100 Exposure time (hr) 0.1 1 10 Area covered (deg2) [Ch 1][Ch 1][Ch 1][Ch 1][Ch 1][Ch 1][Ch 1] EDFN EDFF EDFS COSMOS EGS HDFN XMM 0.01 0.1 1 10 100 Exposure time (hr) 0.1 1 10 Area covered (deg2) [Ch 3][Ch 3][Ch 3][Ch 3][Ch 3][Ch 3] EDFN EDFF COSMOS EGS HDFN XMM Fig. 3. Cumulative area coverage as a function of exposure time for channels 1 and 3, for all fields. The figures for channels 2 and 4 are similar to the ones above, as explained in the text. also the shallowest, with only 0.1 h per pixel on average, and it is also the only field with no channel 3 and 4 data. EDF-F and EDF-N reach the target coverage of 10 deg2with about 1 h of exposure time, with the latter showing deeper coverage over smaller zones. The other fields were covered by many observing programmes with different objectives and which covered specific areas to different depths. The combination of these programmes with our own yields a curve with many plateaus. Finally, there are a few small parts of the EDF-F and HDFN fields that have more than 100 h of exposure time. 3.5. Final sensitivities We estimated the sensitivities of the stacked images by measuring the flux in circular 200 .5 diameter apertures randomly placed across each image after masking the regions with detected objects using the SExtractor (Bertin & Arnouts 1996) segmentation map. The sensitivity is then computed as the standard deviation of these fluxes (3σclipped). This procedure is done in 200 ×200 pixel cells (4 arcmin2). Figure 4shows the cumulative area covered as a function of sensitivity for the channel Table 4. Location and area, in deg2, covered in each field. Field RA Dec ch1 ch2 ch3 ch4 EDF-N 17h58m66◦;36011.74 11.54 0.61 0.62 EDF-F 3h32m−28◦12010.52 11.05 7.78 7.77 EDF-S 4h05m−48◦30023.60 23.14 . . . . . . COSMOS 10h00m2◦1205.37 5.46 2.72 2.72 EGS 14h19m52◦4201.76 1.80 0.97 0.98 HDFN 12h37m62◦2400.91 0.91 0.57 0.63 XMM 2h27m−4◦36017.54 17.48 9.09 9.10 0.01 0.1 1 10 100 Area (deg2) 0.01 0.1 1 3.6 µm depth (µJy;1σ) SWIRE FLS SHELA SSDF SPIES SDWFS SERVS E/WFIRST SIMPLE S-COSMOS SpUDS E-CDFS SPLASH SEDS SMUVS EGS S-CANDELS GOODS NEP UDF EDFN EDFF EDFS COSMOS EGS HDFN XMM all Fig. 4. Sensitivity of Spitzer/IRAC channel 1 data as a function of cumulative area coverage. The coloured lines illustrate 1σdepths measured in empty 200 .5 diameter apertures in each field. The grey solid line is the total area observed to a given depth summed over different surveys. The data points indicate point-source sensitivities at 1σcompiled in Ashby et al. (2018) (we note that some of these data are included in our stacks). The circles and squares represent surveys executed during cryogenic and warm missions, respectively. 1 mosaics. We note the similarity between this figure and the top panel of Fig. 3once the latter is rotated by 90 degrees. The solid line shows our total depth, summed over all our survey fields. Also shown in the figure are the published sensitivities of the surveys that are included in our data and analyses. Generally, our measured sensitivities are consistent with literature measurements for surveys of equivalent exposure time. 4. Validation and quality control As part of our validation process, we compare photometry and astrometry of sources in our stacks with reference catalogues and also extract number counts that can be compared to previous works. 4.1. Catalogue extraction We began by extracting source catalogues from channel 1 and 2 stacks of all fields using SExtractor. We adopted the usual A126, page 6 of 15 Euclid Collaboration: Euclid preparation. XVII. Table 5. SExtractor parameters used for detection and photometry. Parameter name Value DETECT_MINAREA 5 DETECT_MAXAREA 1000000 THRESH_TYPE RELATIVE DETECT_THRESH 2 ANALYSIS_THRESH 2 FILTER_NAME gauss_2.5_5x5.conv DEBLEND_NTHRESH 32 DEBLEND_MINCONT 0.00001 BACK_SIZE 32 BACKPHOTO_THICK 32 BACK_FILTERSIZE 3 BACKPHOTO_TYPE LOCAL MAG_ZEROPOINT 21.58 PHOT_AUTOPARAMS 2.5,5.0 PIXEL_SCALE 0.60 approach of searching for objects that contain a minimum number of connected pixels above a specified noise threshold (in this case 2σ) and measuring their aperture magnitudes. In the case of our moderately deep IRAC data, where many sources are blended due to the large IRAC PSF, this approach is known to miss faint sources. However, these faint sources are not required for our quality assessment purposes and a shallower catalogue is entirely sufficient. SExtractor estimates a global background on a grid with mesh size of 32 ×32 pixels (we remind the reader that pixels are 000 .6 wide). This background is smoothed with a 5×5 pixel Gaussian kernel with a FWHM =100 .5. For each source, the flux is measured within a circular aperture of 700 diameter, and a local background is estimated within an annulus of width 32 pixels around the isophotal limits. The measured fluxes were converted from MJy/sr to AB magnitude using a zero-point of 21.58 (which accounts for a zero-magnitude flux of 3631 Jy and a pixel size of 000 .68), and the latter were converted to total magnitude using the aperture corrections given in the IRAC Instrument Handbook for the warm mission (−0.1164 and −0.1158 for channel 1 and channel 2, respectively), which covers the vast majority of the data, while the correction for the cryogenic mission differs at only the 1–2% level9. A list of relevant SExtractor parameters used for the catalogue extraction can be found in Table 5. 4.2. Astrometric and photometric validation Using the catalogues extracted above, we evaluated the astrometric accuracy of our stacked images. For each field, we crossmatched sources with magnitude 16 <[3.6] <19 within 100 of their counterparts in the Gaia DR2 catalogue. This magnitude range was adopted to ensure that only bright, non-blended sources were chosen. We now present a detailed analysis for EDF-N, but other fields are similar. Figure 5shows the difference between reference and measured coordinates (for clarity, only one point in ten is shown). The heavy blue dashed line gives the size of one pixel in the 8https://irsa.ipac.caltech.edu/data/SPITZER/docs/ irac/iracinstrumenthandbook/19/ 9https://irsa.ipac.caltech.edu/data/SPITZER/docs/ irac/calibrationfiles/ap_corr_warm/ −0.6−0.4−0.2 0.0 0.2 0.4 0.6 δRA (arcsec) −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 δDEC (arcsec) Fig. 5. Difference between the reference and the measured position, in arcseconds, of Gaia DR2 catalogue sources with 16 <[3.6] <19 total magnitudes extracted from the EDF-N channel 1 mosaic. The blue dashed lines indicate the size of one mosaic pixel. The blue dotted lines go through the origin. The shaded regions are ellipses containing 68% and 99% of all sources, respectively. For clarity, only one in ten sources is plotted. stacked image (which is half the size of the instrument pixel). Similarly (again showing only one in ten points), Fig. 6shows, for each coordinate, the difference between the reference and the measured value as a function of position along the other coordinate. The thick red dashed line shows a running median computed over a bin containing 20 points. The flatness of this line indicates that there is no significant spatial variation in astrometric precision. Considering all fields, we find that the 1σ precision (measured as the RMS of the difference between positions in our catalogue and those in Gaia DR2) is 000 .15. Furthermore, the median value is always .000 .1, with the exception of the sparsely-covered HDF-N field where it is .000 .2. These measurements demonstrate that the astrometric solutions have been correctly applied to the individual images and that the combined images are free of residuals on a scale much smaller than an individual mosaic pixel, which is more than sufficient to measure precise infrared and optical-infrared colours. Finally, we perform a simple check on the photometric calibration of our mosaics. As described previously, individual images are photometrically calibrated by the Spitzer Science Center (SSC). Following the validation procedures outlined by the SSC, we compare magnitudes of objects in our catalogues with those in the WISE survey. Because of the difference between the WISE W1 and IRAC channel 1 filter profiles, we selected objects with [3.6]−[4.5] ∼0. Figure 7shows the magnitude difference for the EDF-N field, and the agreement is excellent. Further comparisons with photometric measurements in previous COSMOS IRAC surveys can be found in the appendix of COSMOS2020. 4.3. Magnitude number counts We computed the differential number counts in channel 1 of each field using the corrected 700 aperture magnitudes. Since A126, page 7 of 15 A&A 658, A126 (2022) 266 268 270 272 274 RA (deg) −0.5 0.0 0.5 δRA (arcsec) 65 66 67 68 DEC (deg) −0.5 0.0 0.5 δDEC (arcsec) Fig. 6. Difference between the reference Gaia DR2 catalogue and the measured RA (top panel) and Dec (bottom panel) of sources in the EDFN channel 1 mosaic with 16 <[3.6] <19 total magnitudes as a function of the coordinate. The solid red line shows a running median computed in bins of 20 points, and the shaded areas indicate the regions containing 68% and 99% of all sources, respectively. 16.0 16.5 17.0 17.5 18.0 18.5 19.0 [3.6] −0.4 −0.2 0.0 0.2 0.4 [3.6]-W1 Fig. 7. Photometric comparison with WISE survey. The magnitude measured in 700 apertures for flat-spectrum objects ([3.6]−[4.5] ∼0) is compared with W1 magnitudes in the ALLWISE survey. The shaded area represents the 68% confidence interval. the IRAC PSF is too large to perform morphological source classification, we simply included all objects detected. These are shown in Fig. 8, where the red circles with uncertainties present our measurements, and the lines show the number counts from the literature; the bottom-right panel shows the mean of all fields. We compared our number counts with those presented in Ashby et al. (2013), which also surveyed many of our fields and with those computed using the new COSMOS2020 102 104 101 103 105 EDF-N EDF-F 102 104 101 103 105 Nmag −1deg−2 EDF-S COSMOS 102 104 101 103 105 HDFN EGS 15 20 25 102 104 101 103 105 XMM 15 20 25 Magnitude [3.6µm] All fields A13 Total COSMOS2020 All fields Fig. 8. Magnitude number counts in channel 1 (red circles) together with COSMOS2020 (long dashed lines) and Ashby et al. (2013, hereafter A13; short dotted green lines). Bottom right panel: mean of all fields, and the legend there applies to all panels. photometric catalogue (COSMOS2020) that we used as the reference. There is a general agreement in the number counts in all the fields with Ashby et al. (2013) and COSMOS2020 for 16 < [3.6] <22. At brighter magnitudes, the COSMOS2020 counts drop offas bright sources were not included. At fainter magnitudes, our aperture-based catalogues are confusion-limited and thus incomplete. Conversely, the COSMOS2020 catalogue, which uses a high-resolution prior for the detection and a profilefitting method for the measurement, is complete up to significantly fainter magnitudes. Counts for EDF-N are slightly higher than the other fields at bright magnitudes. To investigate this difference, we simulated a stellar catalogue of 1 deg2centred on EDF-N using TRILEGAL (Girardi et al. 2005) and compared counts from this simulated catalogue with our observations, shown in Fig. 9. At bright magnitudes, where stars are expected to outnumber galaxies, our counts are in reasonable agreement with TRILEGAL predictions, and in excellent agreement with the number counts extracted from the AllWISE (Wright et al. 2010) catalogue for this field. These comparisons indicate that the difference between EDF-N and other fields is largely due to the higher density of stellar sources in there, which is consistent with its lower Galactic latitude. A126, page 8 of 15 Euclid Collaboration: Euclid preparation. XVII. Appendix C: PID numbers Table C.1 lists the Spitzer Space Telescope programme IDs (PIDs) of all the observations processed here. The ones of the observing programmes that we planned for this work are in bold, and the others are of the other archival observations that we reprocessed. Table C.1. Spitzer Space Telescope Program IDs Field PIDs EDF-N 68 609 613 618–624 1101 1125 1188 1189 1191–1200 1317 1334 1600– 1700 1910–1949 1951 1953–1961 1963–1983 2314 3286 3329 3672 10147 11161 13153 20466 30432 40385 60046 70062 70162 80109 80113 80243 80245 90209 EDF-F 81 82 184 194 2313 11080 13058 20708 30866 40058 60022 61009 61052 70039 70145 70204 80217 EDF-S 14235 COSMOS 10159 11016 12103 13094 13104 14045 14081 14203 20070 40801 50310 61043 61060 70023 80057 80062 80134 80159 90042 EGS 8 10084 11065 11080 13118 20754 41023 60145 61042 80069 80156 80216 90180 HDFN 81 169 1304 10136 11004 11063 11080 11134 12095 13053 20218 30411 30476 40204 60122 60145 61040 61062 61063 70162 80215 XMM 181 3248 10042 11086 40021 60024 61041 61060 61061 70039 70062 80149 80156 80159 80218 90038 90175 90177 A126, page 15 of 15