The DECam Local Volume Exploration Survey Data Release 2

The DECam Local Volume Exploration Survey Data Release 2 A. Drlica-Wagner 1,2,3 , P. S. Ferguson 4 , M. Adamów 5 , M. Aguena 6 , S. Allam 1 , F. Andrade-Oliveira 7 , D. Bacon 8 , K. Bechtol 4 , E. F. Bell 9 , E. Bertin 10,11 , P. Bilaji 2,12 , S. Bocquet 13 , C. R. Bom 14 , D. Brooks 15 , D. L. Burke 16,17 , J. A. Carballo-Bello 18 , J. L. Carlin 19 , A. Carnero Rosell 6,20,21 , M. Carrasco Kind 5,22 , J. Carretero 23 , F. J. Castander 24,25 , W. Cerny 2,3 , C. Chang 2,3 , Y. Choi 26 , C. Conselice 27,28 , M. Costanzi 29,30,31 , D. Crnojević 32 , L. N. da Costa 6,33 , J. De Vicente 34 , S. Desai 35 , J. Esteves 7 , S. Everett 36 , I. Ferrero 37 , M. Fitzpatrick 38 , B. Flaugher 1 , D. Friedel 5 , J. Frieman 1,2,3 , J. García-Bellido 39 , M. Gatti 40 , E. Gaztanaga 24,25 , D. W. Gerdes 7,41 , D. Gruen 13 , R. A. Gruendl 5,22,42 , J. Gschwend 6,33 , W. G. Hartley 43 , D. Hernandez-Lang 44 , S. R. Hinton 45 , D. L. Hollowood 36 , K. Honscheid 46,47 , A. K. Hughes 48 , A. Jacques 38 , D. J. James 49 , M. D. Johnson 5 , K. Kuehn 50,51 , N. Kuropatkin 1 , O. Lahav 15 ,T.S.Li 52 , C. Lidman 53,54 , H. Lin 1 , M. March 40 , J. L. Marshall 55 , D. Martínez-Delgado 56 , C. E. Martínez-Vázquez 57,58 , P. Massana 59 , S. Mau 16,60 , M. McNanna 4 , P. Melchior 61 , F. Menanteau 5,22 , A. E. Miller 62,63 , R. Miquel 23,64 , J. J. Mohr 13,65 , R. Morgan 4 , B. Mutlu-Pakdil 2,3 , R. R. Muñoz 66 , E. H. Neilsen 1 , D. L. Nidever 38,59 , R. Nikutta 38 , J. L. Nilo Castellon 67,68 , N. E. D. Noël 69 , R. L. C. Ogando 33 , K. A. G. Olsen 38 , A. B. Pace 70 , A. Palmese 71 , F. Paz-Chinchón 5,72 , M. E. S. Pereira 73 , A. Pieres 6,33 , A. A. Plazas Malagón 92 , J. Prat 2,3 , A. H. Riley 55 , M. Rodriguez-Monroy 74 , A. K. Romer 75 , A. Roodman 16,17 , M. Sako 40 , J. D. Sakowska 69 , E. Sanchez 34 , F. J. Sánchez 1 , D. J. Sand 48 , L. Santana-Silva 76 , B. Santiago 6,77 , M. Schubnell 7 , S. Serrano 24,25 , I. Sevilla-Noarbe 34 , J. D. Simon 78 , M. Smith 79 , M. Soares-Santos 7 , G. S. Stringfellow 80 , E. Suchyta 81 , D. J. Suson 82 , C. Y. Tan 2,12 , G. Tarle 7 , K. Tavangar 3,83 , D. Thomas 8 ,C.To 46 , E. J. Tollerud 26 , M. A. Troxel 84 , D. L. Tucker 1 , T. N. Varga 13,65,85 , A. K. Vivas 58 , A. R. Walker 58 , J. Weller 13,65 , R. D. Wilkinson 75 ,J.F.Wu 26 , B. Yanny 1 , E. Zaborowski 46,47 , and A. Zenteno 58 (DELVE Collaboration, DES Collaboration, Astro Data Lab) 1 Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, USA; [email protected] 2 Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA; [email protected] 3 Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637, USA 4 Physics Department, 2320 Chamberlin Hall, University of Wisconsin-Madison, 1150 University Avenue, Madison, WI 53706-1390, USA 5 Center for Astrophysical Surveys, National Center for Supercomputing Applications, 1205 West Clark Street, Urbana, IL 61801, USA 6 Laboratório Interinstitucional de e-Astronomia—LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ—20921-400, Brazil 7 Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA 8 Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth, PO1 3FX, UK 9 Department of Astronomy, University of Michigan, 1085 South University Avenue, Ann Arbor, 48109-1107, USA 10 CNRS, UMR 7095, Institut d’Astrophysique de Paris, F-75014, Paris, France 11 Sorbonne Universités, UPMC Univ Paris 06, UMR 7095, Institut d’Astrophysique de Paris, F-75014, Paris, France 12 Department of Physics, University of Chicago, Chicago, IL 60637, USA 13 University Observatory, Faculty of Physics, Ludwig-Maximilians-Universität, Scheinerstraße 1, D-81679 Munich, Germany 14 Centro Brasileiro de Pesquisas Físicas, Rua Dr. Xavier Sigaud 150, 22290-180 Rio de Janeiro, RJ, Brazil 15 Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, UK 16 Kavli Institute for Particle Astrophysics & Cosmology, P.O. Box 2450, Stanford University, Stanford, CA 94305, USA 17 SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA 18 Instituto de Alta Investigación, Sede Esmeralda, Universidad de Tarapacá, Avenida Luis Emilio Recabarren 2477, Iquique, Chile 19 Rubin Observatory/AURA, 950 North Cherry Avenue, Tucson, AZ, 85719, USA 20 Instituto de Astrofisica de Canarias, E-38205 La Laguna, Tenerife, Spain 21 Universidad de La Laguna, Dpto. Astrofísica, E-38206 La Laguna, Tenerife, Spain 22 Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 West Green Street, Urbana, IL 61801, USA 23 Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra (Barcelona), Spain 24 Institut d’Estudis Espacials de Catalunya (IEEC), E-08034 Barcelona, Spain 25 Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, 08193 Barcelona, Spain 26 Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA 27 Jodrell Bank Center for Astrophysics, School of Physics and Astronomy, University of Manchester, Oxford Road, Manchester, M13 9PL, UK 28 University of Nottingham, School of Physics and Astronomy, Nottingham, NG7 2RD, UK 29 Astronomy Unit, Department of Physics, University of Trieste, via Tiepolo 11, I-34131 Trieste, Italy 30 INAF-Osservatorio Astronomico di Trieste, via G.B. Tiepolo 11, I-34143 Trieste, Italy 31 Institute for Fundamental Physics of the Universe, Via Beirut 2, I-34014 Trieste, Italy 32 Department of Chemistry and Physics, University of Tampa, 401 West Kennedy Boulevard, Tampa, FL 33606, USA 33 Observatório Nacional, Rua Gal. José Cristino 77, Rio de Janeiro, RJ—20921-400, Brazil 34 Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain 35 Department of Physics, IIT Hyderabad, Kandi, Telangana 502285, India 36 Santa Cruz Institute for Particle Physics, Santa Cruz, CA 95064, USA 37 Institute of Theoretical Astrophysics, University of Oslo, P.O. Box 1029 Blindern, NO-0315 Oslo, Norway 38 NSFʼs National Optical-Infrared Astronomy Research Laboratory, 950 North Cherry Avenue, Tucson, AZ 85719, USA 39 Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, E-28049 Madrid, Spain 40 Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA 41 Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA 42 National Center for Supercomputing Applications, 1205 West Clark Street, Urbana, IL 61801, USA 43 Department of Astronomy, University of Geneva, chemin d’Écogia 16, CH-1290 Versoix, Switzerland 44 Faculty of Physics, Ludwig-Maximilians-Universität, Scheinerstraße 1, D-81679 Munich, Germany The Astrophysical Journal Supplement Series, 261:38 (18pp), 2022 August https://doi.org/10.3847/1538-4365/ac78eb © 2022. The Author(s). Published by the American Astronomical Society. 1 45 School of Mathematics and Physics, University of Queensland, Brisbane, QLD 4072, Australia 46 Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA 47 Department of Physics, The Ohio State University, Columbus, OH 43210, USA 48 Department of Astronomy/Steward Observatory, 933 North Cherry Avenue, Room N204, Tucson, AZ 85721-0065, USA 49 ASTRAVEO LLC, PO Box 1668, Gloucester, MA 01931, USA 50 Australian Astronomical Optics, Macquarie University, North Ryde, NSW 2113, Australia 51 Lowell Observatory, 1400 Mars Hill Rd, Flagstaff, AZ 86001, USA 52 Department of Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto ON, M5S 3H4, Canada 53 Centre for Gravitational Astrophysics, College of Science, The Australian National University, ACT 2601, Australia 54 The Research School of Astronomy and Astrophysics, Australian National University, ACT 2601, Australia 55 George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy, and Department of Physics and Astronomy, Texas A&M University, College Station, TX 77843, USA 56 Instituto de Astrofísica de Andalucía, CSIC, E-18080 Granada, Spain 57 Gemini Observatory, NSFʼs National Optical-Infrared Astronomy Research Laboratory, 670 North A’ohoku Place, Hilo, HI 96720, USA 58 Cerro Tololo Inter-American Observatory, NSFʼs National Optical-Infrared Astronomy Research Laboratory, Casilla 603, La Serena, Chile 59 Department of Physics, Montana State University, P.O. Box 173840, Bozeman, MT 59717-3840, USA 60 Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, CA 94305, USA 61 Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544, USA 62 Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, D-14482 Potsdam, Germany 63 Institut für Physik und Astronomie, Universität Potsdam, Haus 28, Karl-Liebknecht-Straße 24/25, D-14476 Golm (Potsdam), Germany 64 Institució Catalana de Recerca i Estudis Avançats, E-08010 Barcelona, Spain 65 Max Planck Institute for Extraterrestrial Physics, Giessenbachstraße, D-85748 Garching, Germany 66 Departamento de Astronomía, Universidad de Chile, Camino El Observatorio 1515, Las Condes, Santiago, Chile 67 Departamento de Astronomía, Universidad de La Serena, Avenida Juan Cisternas 1200, La Serena, Chile 68 Dirección de Investigación y Desarrollo, Universidad de La Serena, Avenida Raúl Bitrán Nachary N. 1305, La Serena, Chile 69 Department of Physics, University of Surrey, Guildford GU2 7XH, UK 70 McWilliams Center for Cosmology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA 71 Department of Astronomy, University of California, Berkeley, 501 Campbell Hall, Berkeley, CA 94720, USA 72 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK 73 Hamburger Sternwarte, Universität Hamburg, Gojenbergsweg 112, D-21029 Hamburg, Germany 74 Laboratoire de physique des 2 infinis Irène Joliot-Curie, CNRS Université Paris-Saclay, Bât. 100, Faculté des sciences, F-91405 Orsay Cedex, France 75 Department of Physics and Astronomy, Pevensey Building, University of Sussex, Brighton, BN1 9QH, UK 76 NAT-Universidade Cruzeiro do Sul/Universidade Cidade de São Paulo, Rua Galvão Bueno, 868, 01506-000, São Paulo, SP, Brazil 77 Instituto de Física, UFRGS, Caixa Postal 15051, Porto Alegre, RS—91501-970, Brazil 78 Observatories of the Carnegie Institution for Science, 813 Santa Barbara Street, Pasadena, CA 91101, USA 79 School of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ, UK 80 Center for Astrophysics and Space Astronomy, University of Colorado, 389 UCB, Boulder, CO 80309-0389, USA 81 Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA 82 Department of Chemistry and Physics, Purdue University Northwest, Hammond, IN 46323, USA 83 Center for Computational Astrophysics, Flatiron Institute, Simons Foundation, 162 Fifth Avenue, New York, NY 10010, USA 84 Department of Physics, Duke University, Durham, NC 27708, USA 85 Excellence Cluster Origins, Boltzmannstraße 2, D-85748 Garching, Germany Received 2022 April 6; revised 2022 May 26; accepted 2022 June 7; published 2022 August 4 Abstract We present the second public data release (DR2)from the DECam Local Volume Exploration survey (DELVE). DELVE DR2 combines new DECam observations with archival DECam data from the Dark Energy Survey, the DECam Legacy Survey, and other DECam community programs. DELVE DR2 consists of ∼160,000 exposures that cover >21,000 deg 2 of the high-Galactic-latitude (|b|>10°)sky in four broadband optical/near-infrared filters (g,r,i, z). DELVE DR2 provides point-source and automatic aperture photometry for ∼2.5 billion astronomical sources with a median 5σpoint-source depth of g=24.3, r=23.9, i=23.5, and z=22.8 mag. A region of ∼17,000 deg 2 has been imaged in all four filters, providing four-band photometric measurements for ∼618 million astronomical sources. DELVE DR2 covers more than 4 times the area of the previous DELVE data release and contains roughly 5 times as many astronomical objects. DELVE DR2 is publicly available via the NOIRLab Astro Data Lab science platform. Unified Astronomy Thesaurus concepts: Catalogs (205);Surveys (1671);Local Group (929) 1. Introduction Digital sky surveys at optical/near-infrared wavelengths have revolutionized astronomy. These large, untargeted observational programs provide expansive data sets that enable unprecedented statistical studies and fortuitous discoveries across a wide range of astronomical fields. The Sloan Digital Sky Survey (SDSS; York et al. 2000),theTwoMicronAll-SkySurvey(2MASS; Skrutskieetal.2006), the Pan-STARRS1 survey (PS1; Chambers et al. 2016), and the SkyMapper Southern Sky Survey (Wolf et al. 2018)have provided an unprecedented view of the sky. However, these surveys were carried out on relatively small (2.5 m diameter)telescopes, which limited their sensitivity, especially in the Southern Hemisphere. The 570 megapixel Dark Energy Camera (DECam; Flaugher et al. 2015)on the 4 m Victor M. Blanco Telescope at Cerro Tololo in Chile is the premier optical/near-infrared survey instrument in the Southern Hemisphere. Since commissioning in 2012, DECam has been used by the Dark Energy Survey (DES; DES Collaboration 2005,2016), the DECam Legacy Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s)and the title of the work, journal citation and DOI. 2 The Astrophysical Journal Supplement Series, 261:38 (18pp), 2022 August Drlica-Wagner et al. Survey (DECaLS; Dey et al. 2019), and numerous smaller community programs. Through these programs, DECam has gradually, and somewhat unsystematically, imaged much of the southern celestial hemisphere (e.g., Nidever et al. 2021). The DECam Local Volume Exploration Survey (DELVE; DrlicaWagner et al. 2021) 86 seeks to complete contiguous DECam coverage of the southern sky by selectively observing regions of the sky that lack existing observations. The primary science goals of DELVE are to discover and characterize faint satellite galaxies and other resolved stellar systems around the Milky Way, Magellanic Clouds, and isolated Magellanic analogs in the Local Volume (Drlica-Wagner et al. 2021). The DELVE science program has already resulted in the discovery and characterization of five ultrafaint Milky Way satellites (Mau et al. 2020; Martínez-Vázquez et al. 2021; Cerny et al. 2021a,2021b,2022)and an extended study of the Jet stellar stream (Ferguson et al. 2022). Moreover, the unprecedented wide, deep DELVE data set has broad applicability to a wide range of Galactic and extragalactic science (see Drlica-Wagner et al. 2021 for examples). We present the DELVE second data release (DR2), which includes imaging from DELVE, DES, DECaLS, and other public DECam programs covering >21,000 deg 2 of sky in g,r, i, and zindividually and ∼17,000 deg 2 in all four bands (Figure 1). In comparison to the NOIRLab Source Catalog (Nidever et al. 2018,2021), the DECam data in DELVE DR2 are processed by the DES Data Management (DESDM; Morganson et al. 2018)pipeline to provide point-spread function (PSF)and automatic aperture measurements for ∼2.5 billion astronomical sources. In this paper, we describe the DELVE DR2 data set (Section 2)and data reduction pipeline (Section 3). We present studies characterizing the sky coverage, astrometry, photometric calibration, depth, and object classification of the DELVE DR2 catalog in Section 4. In Section 5we describe how the DELVE DR2 data can be accessed via the NSF’s National Optical-Infrared Astronomy Research Laboratory (NOIRLab)Astro Data Lab. Finally, we conclude in Section 6. 2. Data Set DELVE DR2 is comprised of 161,380 DECam exposures assembled from >270 DECam community programs (Appendix A). The largest contributors to the DELVE DR2 data set are DES (DES Collaboration 2021),DECaLS(Dey et al. 2019),DELVE(Drlica-Wagner et al. 2021),andthe DECam eROSITA Survey (DeROSITAS; PI Zenteno). 87 DELVE DR2 more than quadruples the sky area of DELVE DR1 by including exposures in the southern Galactic cap (b<−10°)and exposures in the northern celestial hemisphere (decl. >0°). DELVE DR2 includes data that were collected as part of the DELVE WIDE, MC, and DEEP observing programs (Drlica-Wagner et al. 2021). In addition, DELVE and DeROSITAS have continued to observe regions of the sky that lack DECam imaging to increase the coverage and uniformity of the DECam data set (see Section 3of DrlicaWagner et al. 2021). The key properties of the DELVE DR2 data set are listed in Table 1. Separate criteria were used to select input exposures in the northern Galactic cap, the southern Galactic cap, and the DES region. The northern Galactic cap data set is comprised of DECam exposures with b>10°plus an extension into the Galactic plane (b>0°)in the region of 120°<R. A. <140°to enable an extended analysis of the Jet stellar stream (Jethwa et al. 2018; Ferguson et al. 2022). Exposures in the southern Galactic cap were selected to have b<−10°, excluding exposures within the DES footprint and exposures collected by the DES program. The DES exposures reside in the southern Figure 1. DELVE DR2 covers >20,000 deg 2 in each of the g,r,i,zbands (colored regions)and ∼17,000 deg 2 in all four bands simultaneously (blue region). The ∼5000 deg 2 footprint of DES is outlined in black. These and other sky maps are shown in the equal-area McBryde–Thomas flat polar quartic projection. 86 https://delve-survey.github.io 87 http://astro.userena.cl/derositas 3 The Astrophysical Journal Supplement Series, 261:38 (18pp), 2022 August Drlica-Wagner et al. Galactic cap, but they were selected separately when defining the input to DES DR2 (DES Collaboration 2021). For each exposure, we calculate the effective depth based on the effective-exposure-time scale factor, t eff , which compares the achieved seeing, sky brightness, and extinction due to clouds relative to canonical values for the site (Neilsen et al. 2016). Exposures in the northern Galactic cap region were required to have an effective-exposure-time scale factor of t eff >0.3. The requirement on t eff was relaxed in the southern Galactic cap to avoid rejecting exposures taken close to the southern celestial pole. These exposures are observed at high airmass (()zsec 2 ~) and have a systematically worse PSF FWHM. Exposures in the southern Galactic cap were required to have t eff >0.2 and tT12 s eff exp ´>. No explicit cut was placed on the PSF FWHM in the northern Galactic cap (the cut on t eff removes exposures with very poor seeing), while a cut of FWHM <18 was applied in the southern Galactic cap. The resulting distribution of PSF FWHM and effective exposure time for the full DELVE DR2 data setareshowninFigure2. All exposures in the northern and southern Galactic caps were required to have good astrometric solutions when matched to Gaia DR2 (Gaia Collaboration 2018)by SCAMP (Bertin 2006).These criteria required >250 astrometric matches, 500 astrom 2 c<,Δ (R. A.)<150 mas, and Δ(decl.)<150 mas. We identified and removed exposures that were heavily contaminated by spurious scattered and reflected light from bright stars using the ray-tracing procedure developed by DES (Kent 2013). In addition, rare failures in the sky background estimation can cause a large number of spurious object detections. A handful of exposures suffering from this processing failure were identified as having a large fraction of unmatched objects, and they were removed from the final catalog production. DELVE DR2 includes ∼60,000 exposures collected by DES that were processed and calibrated as input into DES DR2 (DES Collaboration 2021). 88 The DES processing pipeline required t eff >0.2 for g-band exposures and t eff >0.3 for exposures taken in r,i, and z. DES applied a wavelengthdependent criterion to remove exposures with poor PSF FWHM, resulting in a maximum PSF FWHM of {172, 162, 1 56, 1 50}in g,r,i,z, respectively. Additional cuts were applied to remove exposures that were contaminated by stray or scattered light, airplanes, excessive electronic noise, and other artifacts. A full description of the DES data selection and processing criteria can be found elsewhere (Morganson et al. 2018; DES Collaboration 2018,2021). 3. Data Processing All exposures in DELVE DR2 were processed with the DESDM “Final Cut”pipeline (Morganson et al. 2018)as implemented for the processing of DES DR2 (DES Collaboration 2021). Data were reduced and detrended using seasonally averaged bias and flat images, and full-exposure sky background subtraction was performed (Bernstein et al. 2018). Figure 2. (Left)PSF FWHM distributions for DECam exposures included in DELVE DR2. (Right)Distributions of effective exposure time (t eff ×Texp)for exposures included in DELVE DR2. Table 1 DELVE DR2 Key Numbers and Data Quality Summary Survey Characteristic Band Reference griz Number of exposures 42,034 41,852 39,003 38,491 Section 2 Median PSF FWHM (arcseconds)1.24 1.10 1.02 1.00 Section 2 Sky coverage (individual bands, deg 2 )24,663 22,939 21,283 22,866 Section 4.1 Sky coverage (g,r,i,zintersection, deg 2 )16,972 Section 4.1 Astrometric repeatability (angular distance, mas)28 27 28 32 Section 4.2 Astrometric accuracy versus Gaia (angular distance, mas)22 Section 4.2 Photometric repeatability (mmag)4.9 5.0 4.5 5.4 Section 4.3 Photometric uniformity versus Gaia (mmag)7.2 Section 4.3 Absolute photometric uncertainty (mmag)20 Section 4.5 Magnitude limit (PSF, S/N=5)24.3 23.9 23.5 22.8 Section 4.6 Magnitude limit (AUTO, S/N=5)23.9 23.5 23.0 22.4 Section 4.6 Galaxy selection (EXTENDED_CLASS 2, 19 mag_auto_g22)Eff. >99%; Contam. <2% Section 4.7 Stellar selection (EXTENDED_CLASS 1, 19 mag_auto_g22)Eff. >97%; Contam. <2% Section 4.7 88 DELVE DR2 does not include the DES Y-band imaging. 4 The Astrophysical Journal Supplement Series, 261:38 (18pp), 2022 August Drlica-Wagner et al. SourceExtractor (Bertin & Arnouts 1996)and PSFEx (Bertin 2011)were used to automate source detection and photometric measurement. Astrometric calibration was performed against Gaia DR2 using SCAMP(Bertin 2006). 89 We note that DELVE DR2 does not include the production of coadded images (e.g., DES Collaboration 2018,2021); however, we expect that coadded images will be produced as part of a future DELVE data release. Photometric zero-points for each DECam CCD were derived independently for the DES exposures and the other DECam exposures included in DELVE DR2. For the DES exposures, we applied zero-points that were derived for DES DR2 using the forward global calibration module (FGCM; Burke et al. 2018). The FGCM procedure fits time-dependent atmospheric and instrumental conditions to establish an internal network of calibration stars. These calibration stars arethenusedtoiterativelyrefine the photometric calibration of exposures taken during both photometric and nonphotometric conditions. The FGCM has been demonstrated to achieve a relative photometric calibration uncertainty of ∼2 mmag when applied to the DES exposures (DES Collaboration 2021). In contrast, the non-DES exposures included in DELVE DR2 were calibrated following the simple external calibration procedure developed for DELVE DR1 (Drlica-Wagner et al. 2021).Briefly, we performed a 1″match between objects in the Final Cut catalogs for each DECam CCD and the ATLAS Refcat2 catalog (Tonry et al. 2018). ATLAS Refcat2 covers the entire sky by placing measurements from PS1 DR1 (Chambers et al. 2016), SkyMapper DR1 (Wolf et al. 2018), and several other surveys onto the PS1 g,r,i,z-bandpass system. Transformation equations from theATLASRefcat2systemtotheDECamsystemwere derived by comparing calibrated stars from DES DR1 (Appendix A of Drlica-Wagner et al. 2021). Zero-points were derived by finding the median offset required to match the DECam observations to the matched ATLAS Refcat2 observations. Zero-points derived from the DELVE processing and photometric calibration pipeline were found to agree with those derived by DES DR2 with a scatter of ∼10 mmag. While the external calibration against ATLAS Refcat2 yields asignificantly larger scatter than the FGCM, it can be quickly and easily applied to any DECam exposure. We built a multiband catalog of unique sources by combining the SourceExtractor catalogs from each individual CCD image following the procedure described in Drlica-Wagner et al. (2021). We took the set of SourceExtractor detections with flags <4, which allowed neighboring and deblended sources, and (imaflags_iso &2047)=0, which removed objects containing bad pixels within their isophotal radii (Morganson et al. 2018). We further required each detection to have a measured automatic aperture flux, a measured PSF flux, and a PSF magnitude error of <0.5 mag. We sorted SourceExtractor detections into ∼3deg 2 (nside =32)HEALPix pixels (Górski et al. 2005), and within each HEALPix pixel, we grouped detections into clusters by associating all detections within a 0 5 radius. This matching radius was chosen to be significantly larger than the astrometric uncertainty (Section 4.2), but smaller than the PSF FWHM (Figure 2). Furthermore, we identified and split pairs of closely separated objects that were observed in the same image (Drlica-Wagner et al. 2021). Each cluster of detections was associated with an object in the DELVE DR2 catalog. The astrometric position of each object was calculated as the median of the individual singleepoch measurements of the object. We track two sets of photometric quantities for each object: (1)measurements from the single exposure in each band that has the longest effective exposure time (i.e., the longest tT eff exp ´), and (2)the weighted average of the individual single-epoch measurements (these quantities are prefixed by WAVG). The weighted average and unbiased weighted standard deviation were calculated following the weighted sample prescriptions used by DES (Appendix B of DES Collaboration 2021). 90 In addition, we track cluster-level statistics such as the number of detections in each band. We follow the DES procedure to calculate the interstellar extinction from Milky Way foreground dust (DES Collaboration 2018). We compute the value of E(B−V)at the location of each catalog source by performing a bilinear interpolation in (R. A., decl.)to the maps of Schlegel et al. (1998). The reddening correction for each source in each band, A b =R b × E(B−V), is calculated using the fiducial interstellar extinction coefficients from DES DR1 (DES Collaboration 2018): R g =3.185, R r =2.140, R i =1.571, and R z =1.196. Note that, following the procedure of DES DR1, the Schlafly& Finkbeiner (2011)calibration adjustment to the Schlegel et al. (1998)maps is included in our fiducial reddening coefficients (N=0.78). The A b values are included for each object in DELVE DR2, but they are not applied to the magnitude columns by default. The list of the photometric and astrometric properties provided in DELVE DR2 can be found in Appendix B. 3.1. Improvements Relative to DELVE DR1 We have made several improvements to the pipeline described by Drlica-Wagner et al. (2021). 1. The seasonally averaged bias and flat images used for image detrending have been updated to include calibration products from the fifth and sixth years of DES observing. The final epoch of DES calibration products have been used to process all exposures taken after the end of DES data taking. 2. Images that were heavily affected by reflected or scattered light from bright stars were identified using the DES raytracing tool (Kent 2013). Objects detected on these CCDs were removed from the DELVE DR2 catalog. 3. The radius for matching sources within and across bands has been reduced from 1″to 0 5. This change was motivated by the excellent astrometric precision of the DELVE DR1 catalog (∼30mas). The change, along with improvements in the algorithm for splitting pairs of closely separated objects, reduces the number of objects that are spuriously merged. 4. Data Release DELVE DR2 is derived from DECam data covering >20,000 deg 2 in each of the g,r,i,zbands, while ∼17,000 deg 2 are jointly covered in all four bands (Figure 1). DELVE DR2 consists of a catalog of ∼2.5 billion unique astronomical 89 Associated configuration files can be found at https://github.com/delvesurvey/delve_config. 90 Note that we do not apply the “error floor”applied by DES. 5 The Astrophysical Journal Supplement Series, 261:38 (18pp), 2022 August Drlica-Wagner et al. objects, with ∼618 million objects that have measurements in all four bands. This section describes the characterization of the sky coverage, astrometry, photometry, depth, and object classification of the DELVE DR2 catalog. Summary statistics of this characterization are given in Table 1. 4.1. Sky Coverage We quantify the area covered by DELVE DR2 by pixelizing the geometry of each DECam CCD using the decasu 91 package built on healsparse. 92 This package maps the geometry of each CCD using higher-resolution nested HEALPix maps (nside =16,384; ∼166 arcsec 2 )and sums the resulting covered pixels to generate lower-resolution maps (4096; 0.74 arcmin2 =~ n side )containing the fraction of each pixel that is covered by the survey. We quantitatively estimate the covered area as the sum of the coverage fraction maps in each band independently and the intersection of the maps in all four bands (Table 1). 4.2. Astrometry We assess the internal astrometric repeatability by comparing the distributions of angular separations of individual detections of the same objects over multiple exposures. The median global astrometric spread is 29 mas across all bands and is found to be fairly consistent within each band (Table 1). Furthermore, we estimate the external astrometric accuracy by calculating the angular separation between bright stars in DELVE DR2 (16 <g<19)and sources in Gaia EDR3 (Gaia Collaboration 2021)matched within 2″(Figure 3).Wefind that the median separation between the positions measured by DELVE DR2 and Gaia EDR3 is 22 mas, which confirms that no significant astrometric offsets have been introduced by the catalog coaddition procedure. Since the DESDM astrometric calibration does not incorporate proper motions, we expect some correlation between the astrometric residuals and the median measurement epoch of each source (Figure 3). 4.3. Relative Photometric Calibration We assess the photometric repeatability in each band from the rms scatter between independent PSF magnitude measurements of bright stars. For each band, we select stars with 16 <WAVG_MAG_PSF <18 mag and calculate the median rms scatter in ∼0.2 deg 2 HEALPix pixels (nside =128).We estimate the median of the rms scatter over the entire footprint in each band. This quantity is found to be ∼5 mmag and is listed for each band in Table 1. We validate the photometric uniformity of DELVE DR2 by comparing to space-based photometry from Gaia EDR3 (Figure 4). We transform the g,r,i,zphotometry from DELVE to the Gaia Gband using a set of transformations derived for DES DR2 (Sevilla-Noarbe et al. 2021; DES Collaboration 2021). We compare the Gaia EDR3 G-band magnitude in the AB system (G Gaia )to the predicted G-band magnitude of stars in DELVE (G DELVE ). We calculate the median difference, G DELVE −G Gaia , within each nside =128 HEALPix pixel for stars with 16 <r<20 mag, 0.5 < (g−i)<1.5 mag, and Gaia G<20 mag. We plot the spatial distribution of the median difference along with histograms for the median difference within the DES region and over the full DELVE DR2 footprint in Figure 4. While the median difference within the DES footprint is zero by construction, we find a small (<1 mmag)offset between DELVE DR2 and Gaia EDR3. We estimate the photometric uniformity of DELVE DR2 as the standard deviation of the median differences across pixels, which yields a value of 7.2 mmag (Table 1). However, because the distribution of residuals is non-Gaussian (Figure 4), we also provide the 68% containment interval, which is 9.1 mmag. We find no significant magnitudedependent trends in G DELVE −G Gaia within the magnitude range that we study (16 <r<20 mag). Similar comparisons between DES DR2 and Gaia DR2 demonstrated that the nonuniformity of Gaia observations can be the dominant contributor to photometric nonuniformity estimated using this technique (Burke et al. 2018; SevillaNoarbe et al. 2021; DES Collaboration 2021). Within the DES footprint, we find that comparing to Gaia EDR3 reveals much less structure than was seen when comparing to Gaia DR2 (DES Collaboration 2021). Furthermore, it is clear that outside the DES footprint, spatial structure in the DELVE DR2 calibration dominates the nonuniformity relative to Gaia. We observe a systematic shift of ∼10 mmag relative to Gaia EDR3 at decl. =−30°, where ATLAS Refcat2 switches from using PS1 to SkyMapper (Tonry et al. 2018; Drlica-Wagner et al. 2021). It should be possible to improve the relative photometric calibration of DELVE by applying the FGCM (Burke et al. 2018). Initial tests using several thousand square degrees of the DELVE data suggest that a relative photometric uniformity of 5 mmag is possible. Figure 3. (Left)Median observational epoch for DECam observations in all bands (griz)that are used for calculating the coordinates of DELVE DR2 objects. (Right) Median astrometric offsets between DELVE DR2 objects with 16 <g<19 and Gaia EDR3 objects matched within 2″. Note that no correction has been made for the proper motions of objects, which results in higher astrometric residuals in regions dominated by recent observations (e.g., see Figure 3 of DES Collaboration 2021). 91 https://github.com/erykoff/decasu 92 https://healsparse.readthedocs.io 6 The Astrophysical Journal Supplement Series, 261:38 (18pp), 2022 August Drlica-Wagner et al. 4.4. Color Uniformity As an additional check of the color uniformity and relative photometric calibration of DELVE DR2, we perform an analysis of the stellar sequence using the g,r, and ibands (e.g., Ivezićet al. 2004; MacDonald et al. 2004; High et al. 2009; Gilbank et al. 2011; Coupon et al. 2012; Kelly et al. 2014; Drlica-Wagner et al. 2018). The stellar sequence follows a tight locus in the (g−r)versus (r−i)color–color plane, especially in the region from 0.3 <(g−r)<1.1. This region of the stellar sequence is dominated by main-sequence stars and has a small intrinsic width. This tight relation allows us to assess the calibration quality in two ways: (1)On small scales, we can probe the statistical error in color measurements by computing the width of the stellar sequence (w ⊥ ).(2)On larger angular scales, we can use variations in the location of this sequence as an estimate of systematic color uniformity. We follow the method of Ivezićet al. (2004)to measure both the width and location of the stellar sequence. Briefly, we select high-confidence stars (EXTENDED_CLASS_G=0)that are bright with g,r, and iextinction-corrected magnitudes brighter than 20 mag and extinction-corrected color 0.3 <(g−r)<1.1. We performed a linear fit on the data and derived principal components, P 1 and P 2 , where P 2 is perpendicular to the line that best fits the stellar locus. We define w ⊥ to be the 3σ-clipped rms of the distribution of stars in the P 2 direction. The location of the stellar sequence is summarized as a residual between the (r−i)color of the linear fitat(g−r)=0.7. This value is computed relative to a lowextinction (E(B−V)<0.015)empirical stellar locus computed from the DES DR2 catalog, where (r−i) DES =0.221 mag at (g−r) DES =0.7 mag. To estimate the magnitude of the statistical error on color, we split our data set into two areas. First, we analyze the DES footprint, which is covered homogeneously and has zero-points derived from FGCM. Second, we analyze the rest of the DELVE DR2 footprint where zero-points were derived from ATLAS Refcat2 (Section 3). We calculate the width of the stellar sequence, w ⊥ , using both the best single-epoch measurement (MAG_PSF)and the weighted-average catalog measurements (WAVG_MAG_PSF)for each nside =128 HEALPix pixel. The spatial distribution of w ⊥ derived from the weighted-average magnitudes can be seen in Figure 5. For the region in the DES footprint, we also compute an estimate of the relative difference in the statistical errors between each type of magnitude measurement, N eff =MAGERR_PSF 2 /WAVG_MAGERR_PSF 2 . Assuming that w ⊥ comes from the statistical uncertainty in the photometric calibration (σ stat )and intrinsic width of the stellar sequence (w ⊥,0 )added in quadrature ( w w 2stat 2,0 2 s=+ ^^ ),we can use the two measurements of w ⊥ and effective number of observations (N eff )for the WAVG measurement to solve for σ stat and w ⊥,0 . Distributions for w ⊥ in the DES region for the single measurement and WAVG measurement cases are shown on the right of Figure 5in gray. We find a median singlemeasurement (WAVG measurement)error of σ (FGCM) ∼8 mmag (σ (FGCM,WAVG) ∼3 mmag)for the region with zeropoints derived from FGCM and a median intrinsic width of the stellar locus w ⊥,0 ∼8 mmag. To estimate σ stat for the ATLAS Refcat2 calibrated region where the coverage is not as homogeneous, we use the w ⊥,0 estimate from the FGCM region. The median single-measurement (WAVG measurement)error of σ (ATLAS R2) ∼10 mmag (σ (ATLAS R2,WAVG) ∼7 mmag)for the region with zero-points derived from ATLAS Refcat2. This value of σ (ATLAS R2,WAVG) agrees with the comparison to Gaia EDR3 data in Section 4.3.Furthermore, this analysis highlights the differences in color uncertainty between the FGCM calibrated region and the ATLAS Refcat2 calibrated region. We note that variations in reddening and underlying stellar populations could cause variations in the intrinsic width of the stellar locus, and our value in the DES region of w ⊥,0 =8 mmag can be thought of as a lower limit over the rest of the sky. Therefore, the inferred σ (ATLAS R2) is an upper limit on the statistical color uncertainty in the ATLAS Refcat2 calibrated region. As described above, we use the position of the stellar locus in the (g−r)versus (r−i)plane as a probe of color uniformity in DELVE. Similar to w ⊥ , we use the results of our fit calculated for each nside =128 HEALPix pixel. The offsets between the calculated value and the DES Y6 value for each HEALPix pixel are shown in the top panel of Figure 6. Using MAG_PSF (WAVG_MAG_PSF),wefind a median rms in the (r−i)color of the linear fitat(g−r)=0.7 of 9 mmag (8 mmag)for the entire survey footprint, with a scatter between MAG_PSF and WAVG_MAG_PSF of less than 3 mmag. If we Figure 4. Median difference between the DELVE DR2 photometry transformed into the Gaia G-band, G DELVE , and the measured magnitude from Gaia EDR3, G Gaia . The spatial distribution of the median difference in each pixel is shown in the left panel (color range clipped to ±10 mmag), while the right panel shows a histogram of the pixel values. A shift in the zero-point can be seen at decl. ∼−30°, which corresponds to the boundary between the ATLAS Refcat2 use of PS1 and SkyMapper (Section 4.3). This comparison is restricted to the area with overlapping DELVE DR2 coverage in all four bands (g,r,i,z). 7 The Astrophysical Journal Supplement Series, 261:38 (18pp), 2022 August Drlica-Wagner et al. compare the DES footprint to the rest of the DELVE using MAG_PSF,wefind median rms measurements of 5 mmag and 9 mmag, respectively. It is likely that some of this scatter can be attributed to the effects of interstellar extinction and changes in the observed stellar populations across the footprint, which will shift the location of the stellar locus (see Section 2.3 of High et al. 2009). To estimate the effect of reddening on these values, we compute a median rms only for regions with E (B−V)<0.5 mag and find that our results are unchanged. This indicates that reddening systematics do not strongly contribute to the spatial structure seen in the top row of Figure 6. In order to account for shifts of the stellar locus on large spatial scales (tens of degrees)and estimate the color uniformity on scales of a few degrees, we smooth the spatial distribution of the residuals with a Gaussian kernel with a standard deviation of σ=5°and fitafifth-order polynomial. This polynomial is then subtracted from the spatial distribution, mitigating the effect of spatially dependent changes in the location of the stellar locus and highlighting systematic scatter in the color uniformity at scales of a few degrees. Using this subtracted map, we find a median rms of 4 mmag for the DES region and 7 mmag for the rest of the DELVE DR2 footprint. This can be interpreted as a lower limit on the systematic uncertainties in the color measurements of DELVE DR2. 4.5. Absolute Photometric Calibration The photometry of DELVE DR2 is tied to the AB magnitude system (Oke & Gunn 1983)via the HST CalSpec standard star C26202. Within the DES footprint, the DES FGCM zero-points are directly tied to C26202, as described in Section 4.2.2 of DES Collaboration (2021). Outside the DES footprint, the calibration is tied more indirectly to C26202 via the zero-points of the ATLAS Refcat2 transformation equations, which were adjusted to match DES DR2 (see Appendix A of DrlicaWagner et al. 2021). Due to this procedure, DELVE DR2 cannot have a better absolute calibration accuracy than DES DR2, which sets a lower limit on the statistical uncertainty of 2.2 mmag per band and a systematic uncertainty of 11 to 12 mmag per band (see Table 1 of DES Collaboration 2021). Figure 5. (Left)Spatial distribution of the measured width of the stellar locus w ⊥ using WAVG_MAG_PSF magnitudes for each nside =128 HEALPix pixel in the DELVE DR2 footprint. The DES region can be seen to have much lower values of w ⊥ indicating a smaller statistical error in these measurements. (Right)Histogram of w ⊥ values (ww 2,0 2 s=+ ^^), where w ⊥,0 ∼8 mmag. The black line shows the same data as the spatial map (WAVG_MAG_PSF magnitudes for the full footprint, a clear bimodality can be seen due to the difference in relative statistical error in measurements between the DES region calibrated with FGCM (σ (FGCM,WAVG) ∼3 mmag), and the rest of the DELVE footprint calibrated with ATLAS Refcat2 (σ (ATLAS R2,WAVG) ∼7 mmag). The gray histograms illustrate the difference in the measured width between the weighted-average (solid)and single best measurements (dotted). Figure 6. (Top)Offset in the stellar locus (r−i)color at (g−r)=0.7 fitin each nside=128 HEALPix pixel relative to the DES value of (r−i) DES =0.221 mag. Offsets in this distribution at large spatial scales are likely due to changing stellar populations. (Middle)Polynomial fit to the (r−i) offset map smoothed with a σ=5°Gaussian kernel. (Bottom)Map of residuals after the polynomial fit has been subtracted. This residual map highlights variations in the location of the stellar locus at smaller scales and is an estimate of the color uniformity. 8 The Astrophysical Journal Supplement Series, 261:38 (18pp), 2022 August Drlica-Wagner et al. The global offset seen between the PS1 and SkyMapper regions of ATLAS Refcat2 when compared to Gaia EDR3 suggests that the absolute calibration cannot be better than 10 mmag. Combining the maximum systematic uncertainty on the absolute calibration from DES DR2 and the DELVE DR2 offset relative to Gaia EDR3, we estimate that the absolute photometric accuracy of DELVE DR2 is 20 mmag. DELVE performed dedicated observations of the CalSpec standard star SDSS151421 during twilight hours in 2020. These observations were not used to set the absolute calibration of DELVE DR2, and they can instead be used to validate our estimate of the absolute calibration uncertainty. We find that the median offsets between the DELVE PSF magnitudes and the CalSpec STIS magnitudes for SDSS151421 are Δg=4.4, Δr=23.3, Δi=7.2, and Δz=1.6 mmag with a scatter of ∼6 mmag. Similar analyses performed by DES found ∼10 mmag offsets when comparing the DES photometry to several CalSpec standard stars and DA white dwarfs within the DES footprint (DES Collaboration 2021). Based on these comparisons, we maintain the stated absolute calibration accuracy of 20 mmag. 4.6. Photometric Depth The photometric depth of DELVE DR2 can be assessed in several ways. One common metric is to determine the magnitude at which a fixed signal-to-noise ratio (S/N)is achieved (e.g., Rykoff et al. 2015). The statistical magnitude uncertainty is related to the S/N calculated from the flux, F/δF, via propagation of uncertainties and Pogson’s law (Pogson 1856), ()mF F 2.5 ln 10 .1dd = Using this equation, we estimate the magnitude at which DELVE DR2 achieves S/N=5(δm≈0.2171)and S/N=10 (δm≈0.1085). We calculate these magnitude limits for pointlike sources using mag_psf and for all sources using mag_auto. For each magnitude and S/N combination, we select objects and interpolate the relation between mand median(δm)in 12 arcmin 2 ~ HEALPix pixels (nside = 1024). The resulting median magnitude limits estimated over the DELVE DR2 footprint are shown in Table 2. We show histograms of the mag_psf magnitude limit for pointlike sources at S/N=5 in the left panel of Figure 7. In the right panel of Figure 7, we show the DELVE DR2 area as a function of depth in each band. The magnitude limits as a function of location on the sky are shown in Appendix C. Due to the catalog-level coaddition process, the depth of DELVE DR2 is set by the single best exposure in any region of the sky. This means that the depth of DELVE DR2 is very similar to that of DELVE DR1 (Drlica-Wagner et al. 2021)and significantly shallower than DES DR2 even in the overlapping DES region (DES Collaboration 2021). At bright magnitudes, the DECam CCDs will saturate at g=15.2, r=15.7, i=15.8, and z=15.5 for point sources observed in a 90 s exposure with median seeing (DES Collaboration 2021). While ∼85% of the exposures included in DELVE DR2 have exposure times of 90 s, there are some regions with longer exposure times where saturation will occur at fainter magnitudes. Therefore, objects detected by SourceExtractor with the saturation flag bit set were removed from the DELVE DR2 catalog production. 4.7. Object Classification DELVE DR2 includes the SourceExtractor spread_model parameter, which can be used to separate spatially extended galaxies from pointlike stars and quasars (e.g., Desai et al. 2012). Following DES (e.g., DES Collaboration 2018,2021)and DELVE DR1 (Drlica-Wagner et al. 2021),wede fine extended_class parameters as a sum of several Boolean conditions, (( )) (( )) (( )) () g g g g g g g extended_class_ spread_model_ 3 spreaderr_model_ 0.005 spread_model_ spreaderr_model_ 0.003 spread_model_ spreaderr_model_ 0.003 . 2 = +> + +> + -> When true, each Boolean condition adds one unit to the classifier such that an extended_class value of 0 indicates high-confidence stars, 1 is likely stars, 2 is likely galaxies, and 3 is high-confidence galaxies. Objects that lack coverage in a specific band or where the spread_model fit failed are set to a sentinel value of −9. We calculate extended_class values similarly for each band; however, we recommend the use of the g-band classifier, extended_class_g, because the gband has the widest coverage and deepest limiting magnitude. In Figure 8we characterize the performance of extended_class_gas a function of magnitude by matching DELVE DR2 objects to data from the Spring equatorial field (128°R. A. 225°;−2°decl. 5°)of the Wide layer of Hyper Suprime-Cam Subaru Strategic Program Public Data Release 3 (HSC-SSP PDR3 Aihara et al. 2022). To improve uniformity, we select only overlapping regions where the S/ N=5 limiting PSF magnitude from DELVE is representative of the DELVE DR2 survey (magnitude limit of 24 <g<24.5; Appendix C). The superior image quality (i-band PSF FWHM ∼0 61)and depth (i∼26.2 mag)of the Wide layer of HSC-SSP PDR3 enable robust tests of star–galaxy separation in DELVE DR2. The matched data set covers ∼394 deg 2 and contains ∼9.6 million matched objects. Following previous analyses (DES Collaboration 2018; Table 2 DELVE DR2 Median Depth Estimates Measurement Magnitude Limit griz (mag)(mag)(mag)(mag) MAG_PSF (S/N=5)24.3 23.9 23.5 22.8 MAG_PSF (S/N=10)23.5 23.1 22.7 22.1 MAG_AUTO (S/N=5)23.9 23.5 23.0 22.4 MAG_AUTO (S/N=10)22.8 22.5 22.1 21.4 Note. The MAG_PSF depth is estimated from pointlike sources, while the MAG_AUTO depth is estimated from all DELVE DR2 sources. Both MAG_PSF and MAG_AUTO are estimated from the best exposure of each object (see Section 4.6). 9 The Astrophysical Journal Supplement Series, 261:38 (18pp), 2022 August Drlica-Wagner et al. Appendix C Depth This appendix includes sky maps showing variations in the S/N=5 depth of DELVE DR2 in the g,r,i,zbands. The S/N=5 depth was derived from the magnitude at which the median magnitude uncertainty was δm=0.2171 mag (Section 4.6). These values were derived in 12 arcmin 2 ~ HEALPix pixels (nside =1024)and are shown in Figure 10. Figure 10. Sky maps and histograms of the S/N=5 magnitude limit computed from the statistical uncertainty in MAG_PSF. Dashed vertical lines indicate the median depth quoted in Table 1. Sky maps are plotted using an equal-area McBryde–Thomas flat polar quartic projection in celestial equatorial coordinates. 16 The Astrophysical Journal Supplement Series, 261:38 (18pp), 2022 August Drlica-Wagner et al. ORCID iDs A. Drlica-Wagner https://orcid.org/0000-0001-8251-933X P. S. Ferguson https://orcid.org/0000-0001-6957-1627 M. Adamów https://orcid.org/0000-0002-6904-359X M. Aguena https://orcid.org/0000-0001-5679-6747 D. Bacon https://orcid.org/0000-0002-2562-8537 K. Bechtol https://orcid.org/0000-0001-8156-0429 E. F. Bell https://orcid.org/0000-0002-5564-9873 E. Bertin https://orcid.org/0000-0002-3602-3664 S. Bocquet https://orcid.org/0000-0002-4900-805X C. R. Bom https://orcid.org/0000-0003-4383-2969 D. Brooks https://orcid.org/0000-0002-8458-5047 J. A. Carballo-Bello https://orcid.org/0000-00023690-105X J. L. Carlin https://orcid.org/0000-0002-3936-9628 A. Carnero Rosell https://orcid.org/0000-0003-3044-5150 M. Carrasco Kind https://orcid.org/0000-0002-4802-3194 J. Carretero https://orcid.org/0000-0002-3130-0204 F. J. Castander https://orcid.org/0000-0001-7316-4573 W. Cerny https://orcid.org/0000-0003-1697-7062 C. Chang https://orcid.org/0000-0002-7887-0896 C. Conselice https://orcid.org/0000-0003-1949-7638 M. Costanzi https://orcid.org/0000-0001-8158-1449 D. Crnojevićhttps://orcid.org/0000-0002-1763-4128 J. De Vicente https://orcid.org/0000-0001-8318-6813 S. Desai https://orcid.org/0000-0002-0466-3288 M. Fitzpatrick https://orcid.org/0000-0002-9080-0751 B. Flaugher https://orcid.org/0000-0002-2367-5049 J. Frieman https://orcid.org/0000-0003-4079-3263 J. García-Bellido https://orcid.org/0000-0002-9370-8360 E. Gaztanaga https://orcid.org/0000-0001-9632-0815 D. W. Gerdes https://orcid.org/0000-0001-6942-2736 D. Gruen https://orcid.org/0000-0003-3270-7644 R. A. Gruendl https://orcid.org/0000-0002-4588-6517 J. Gschwend https://orcid.org/0000-0003-3023-8362 S. R. Hinton https://orcid.org/0000-0003-2071-9349 D. L. Hollowood https://orcid.org/0000-0002-9369-4157 K. Honscheid https://orcid.org/0000-0002-6550-2023 A. K. Hughes https://orcid.org/0000-0002-1718-0402 A. Jacques https://orcid.org/0000-0001-9631-831X D. J. James https://orcid.org/0000-0001-5160-4486 K. Kuehn https://orcid.org/0000-0003-0120-0808 N. Kuropatkin https://orcid.org/0000-0003-2511-0946 O. Lahav https://orcid.org/0000-0002-1134-9035 T. S. Li https://orcid.org/0000-0002-9110-6163 C. Lidman https://orcid.org/0000-0003-1731-0497 H. Lin https://orcid.org/0000-0002-7825-3206 J. L. Marshall https://orcid.org/0000-0003-0710-9474 D. Martínez-Delgado https://orcid.org/0000-00033835-2231 C. E. Martínez-Vázquez https://orcid.org/0000-00029144-7726 P. Massana https://orcid.org/0000-0002-8093-7471 S. Mau https://orcid.org/0000-0003-3519-4004 M. McNanna https://orcid.org/0000-0001-5435-7820 P. Melchior https://orcid.org/0000-0002-8873-5065 F. Menanteau https://orcid.org/0000-0002-1372-2534 A. E. Miller https://orcid.org/0000-0002-7483-7327 R. Miquel https://orcid.org/0000-0002-6610-4836 J. J. Mohr https://orcid.org/0000-0002-0810-5558 R. Morgan https://orcid.org/0000-0002-7016-5471 B. Mutlu-Pakdil https://orcid.org/0000-0001-9649-4815 E. H. Neilsen https://orcid.org/0000-0002-7357-0317 D. L. Nidever https://orcid.org/0000-0002-1793-3689 R. Nikutta https://orcid.org/0000-0002-7052-6900 J. L. Nilo Castellon https://orcid.org/0000-0002-8282-469X R. L. C. Ogando https://orcid.org/0000-0003-2120-1154 K. A. G. Olsen https://orcid.org/0000-0002-7134-8296 A. B. Pace https://orcid.org/0000-0002-6021-8760 A. Palmese https://orcid.org/0000-0002-6011-0530 F. Paz-Chinchón https://orcid.org/0000-0003-1339-2683 A. Pieres https://orcid.org/0000-0001-9186-6042 A. A. Plazas Malagón https://orcid.org/0000-00022598-0514 A. H. Riley https://orcid.org/0000-0001-5805-5766 A. K. Romer https://orcid.org/0000-0002-9328-879X A. Roodman https://orcid.org/0000-0001-5326-3486 M. Sako https://orcid.org/0000-0003-2764-7093 J. D. Sakowska https://orcid.org/0000-0002-1594-1466 E. Sanchez https://orcid.org/0000-0002-9646-8198 F. J. Sánchez https://orcid.org/0000-0003-3136-9532 D. J. Sand https://orcid.org/0000-0003-4102-380X L. Santana-Silva https://orcid.org/0000-0003-3402-6164 M. Schubnell https://orcid.org/0000-0001-9504-2059 I. Sevilla-Noarbe https://orcid.org/0000-0002-1831-1953 J. D. Simon https://orcid.org/0000-0002-4733-4994 M. Smith https://orcid.org/0000-0002-3321-1432 M. Soares-Santos https://orcid.org/0000-0001-6082-8529 G. S. Stringfellow https://orcid.org/0000-0003-1479-3059 E. Suchyta https://orcid.org/0000-0002-7047-9358 D. J. Suson https://orcid.org/0000-0003-2911-2025 C. Y. Tan https://orcid.org/0000-0003-0478-0473 G. 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