# The DECam Local Volume Exploration Survey Data Release 2

A. Drlica-Wagner, P.S. Ferguson, M. Adamów, M. Aguena, F.

Andrade-Oliveira, D. Bacon, K. Bechtol, E.F. Bell, E. Bertin, P. Bilaji, et al.

► To cite this version:

A. Drlica-Wagner, P.S. Ferguson, M. Adamów, M. Aguena, F. Andrade-Oliveira, et al. The DE-Cam Local Volume Exploration Survey Data Release 2. Astrophys.J.Supp., 2022, 261 (2), pp.38. 10.3847/1538-4365/ac78eb. hal-03644401

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The DECam Local Volume Exploration Survey Data Release 2

A. Drlica-Wagner¹,²,³ ID, P. S. Ferguson⁴ ID, M. Adamów⁵ ID, M. Aguena⁶ ID, S. Allam¹, F. Andrade-Oliveira⁷, D. Bacon⁸ ID, K. Bechtol⁴ ID, E. F. Bell⁹ ID, E. Bertin¹⁰,¹¹ ID, P. Bilaji²,¹², S. Bocquet¹³ ID, C. R. Bom¹⁴ ID, D. Brooks¹⁵ ID, D. L. Burke¹⁶,¹⁷, J. A. Carballo-Bello¹⁸ ID, J. L. Carlin¹⁹ ID, A. Carnero Rosell⁶,²⁰,²¹ ID, M. Carrasco Kind⁵,²² ID, J. Carretero²³ ID, F. J. Castander²⁴,²⁵ ID, W. Cerny²,³ ID, C. Chang²,³ ID, Y. Choi²⁶, C. Conselice²⁷,²⁸ ID, M. Costanzi²⁹,³⁰,³¹ ID, D. Crnojević³² ID, L. N. da Costa⁶,³³, J. De Vicente³⁴ ID, S. Desai³⁵ ID, J. Esteves⁷, S. Everett³⁶, I. Ferrero³⁷, M. Fitzpatrick³⁸ ID, B. Flaugher¹ ID, D. Friedel⁵, J. Frieman¹,²,³ ID, J. García-Bellido³⁹ ID, M. Gatti⁴⁰, E. Gaztanaga²⁴,²⁵ ID, D. W. Gerdes⁷,⁴¹ ID, D. Gruen¹³ ID, R. A. Gruendl³,²²,⁴² ID, J. Gschwend⁶,³³ ID, W. G. Hartley⁴³, D. Hernandez-Lang⁴⁴, S. R. Hinton⁴⁵ ID, D. L. Hollowood³⁶ ID, K. Honscheid⁴⁶,⁴⁷ ID, A. K. Hughes⁴⁸ ID, A. Jacques³⁸ ID, D. J. James⁴⁹ ID, M. D. Johnson⁵, K. Kuehn⁵⁰,⁵¹ ID, N. Kuropatkin¹ ID, O. Lahav¹⁵ ID, T. S. Li⁵² ID, C. Lidman⁵³,⁵⁴ ID, H. Lin¹ ID, M. March⁴⁰, J. L. Marshall⁵⁵ ID, D. Martínez-Delgado⁵⁶ ID, C. E. Martínez-Vázquez⁵⁷,⁵⁸ ID, P. Massana⁵⁹ ID, S. Mau¹⁶,⁶⁰ ID, M. McNanna⁴ ID, P. Melchior⁶¹ ID, F. Menanteau⁵,²² ID, A. E. Miller⁶²,⁶³ ID, R. Miquel²³,⁶⁴ ID, J. J. Mohr¹³,⁶⁵ ID, R. Morgan⁴ ID, B. Mutlu-Pakdil²,³ ID, R. R. Muñoz⁶⁶, E. H. Neilsen¹ ID, D. L. Nidever³⁸,⁵⁹ ID, R. Nikutta³⁸ ID, J. L. Nilo Castellon⁶⁷,⁶⁸ ID, N. E. D. Noël⁶⁹, R. L. C. Ogando³³ ID, K. A. G. Olsen³⁸ ID, A. B. Pace⁷⁰ ID, A. Palmese⁷¹ ID, F. Paz-Chinchón⁵,⁷² ID, M. E. S. Pereira⁷³, A. Pieres⁶,³³ ID, A. A. Plazas Malagón⁹² ID, J. Prat²,³, A. H. Riley⁵⁵ ID, M. Rodriguez-Monroy⁷⁴, A. K. Romer⁷⁵ ID, A. Roodman¹⁶,¹⁷ ID, M. Sako⁴⁰ ID, J. D. Sakowska⁶⁹ ID, E. Sanchez³⁴ ID, F. J. Sánchez¹ ID, D. J. Sand⁴⁸ ID, L. Santana-Silva⁷⁶ ID, B. Santiago⁶,⁷⁷, M. Schubnell⁷ ID, S. Serrano²⁴,²⁵, I. Sevilla-Noarbe³⁴ ID, J. D. Simon⁷⁸ ID, M. Smith⁷⁹ ID, M. Soares-Santos⁷ ID, G. S. Stringfellow⁸⁰ ID, E. Suchyta⁸¹ ID, D. J. Suson⁸² ID, C. Y. Tan²,¹² ID, G. Tarle⁷ ID, K. Tavangar³,⁸³, D. Thomas⁸ ID, C. To⁴⁶ ID, E. J. Tollerud²⁶ ID, M. A. Troxel⁸⁴, D. L. Tucker¹ ID, T. N. Varga¹³,⁶⁵,⁸⁵, A. K. Vivas⁵⁸ ID, A. R. Walker⁵⁸ ID, J. Weller¹³,⁶⁵ ID, R. D. Wilkinson⁷⁵, J. F. Wu²⁶ ID, B. Yanny¹ ID, E. Zaborowski⁴⁶,⁴⁷, and A. Zenteno⁵⁸ ID

(DELVE Collaboration, DES Collaboration, Astro Data Lab)

¹ Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, USA; kadrica@fnal.gov

² Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA; peter.ferguson@wisc.edu

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 Astrofísica 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 NSF's National Optical-Infrared Astronomy Research Laboratory, 950 North Cherry Avenue, Tucson, AZ 85719, USA

³⁹ Instituto de Física Teórica UAM/CSIC, Universidad Autónoma 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
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, NSF's National Optical-Infrared Astronomy Research Laboratory, 670 North A'ohoku Place, Hilo, HI 96720, USA

⁵⁸ Cerro Tololo Inter-American Observatory, NSF's 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 deg² 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 5σ point-source depth of g = 24.3, r = 23.9, i = 23.5, and z = 22.8 mag. A region of ~17,000 deg² 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), the Two Micron All-Sky Survey (2MASS;

Skrutskie et al. 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

<figure><img src="image_3.png" /></figure>

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.
<figure><img src="image_4.png" /><figcaption>**Figure 1.** DELVE DR2 covers >20,000 deg² in each of the *g*, *r*, *i*, *z* bands (colored regions) and ~17,000 deg² in all four bands simultaneously (blue region). The ~5000 deg² footprint of DES is outlined in black. These and other sky maps are shown in the equal-area McBryde-Thomas flat polar quartic projection.</figcaption></figure>

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; Drlica-Wagner et al. 2021)<fnref n="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 deg² of sky in *g*, *r*, *i*, and *z* individually and ~17,000 deg² 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 5 we 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), and the DECam eROSITA Survey (DeROSITAS; PI Zenteno).<fnref n="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 3 of Drlica-Wagner 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

<footnote n="86">⁸⁶ https://delve-survey.github.io</footnote>

<footnote n="87">⁸⁷ http://astro.userena.cl/derositas</footnote>
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 2.** (Left) PSF FWHM distributions for DECam exposures included in DELVE DR2. (Right) Distributions of effective exposure time ($t_{eff} \times T_{exp}$) for exposures included in DELVE DR2.</figcaption></figure>

<table><caption>**Table 1**

DELVE DR2 Key Numbers and Data Quality Summary</caption><thead><tr><th rowspan="2">Survey Characteristic</th><th colspan="4">Band</th><th rowspan="2">Reference</th></tr><tr><th>g</th><th>r</th><th>i</th><th>z</th></tr></thead><tbody><tr><td>Number of exposures</td><td>42,034</td><td>41,852</td><td>39,003</td><td>38,491</td><td>Section 2</td></tr><tr><td>Median PSF FWHM (arcseconds)</td><td>1.24</td><td>1.10</td><td>1.02</td><td>1.00</td><td>Section 2</td></tr><tr><td>Sky coverage (individual bands, deg<sup>2</sup>)</td><td>24,663</td><td>22,939</td><td>21,283</td><td>22,866</td><td>Section 4.1</td></tr><tr><td>Sky coverage (g, r, i, z intersection, deg<sup>2</sup>)</td><td></td><td>16,972</td><td></td><td></td><td>Section 4.1</td></tr><tr><td>Astrometric repeatability (angular distance, mas)</td><td>28</td><td>27</td><td>28</td><td>32</td><td>Section 4.2</td></tr><tr><td>Astrometric accuracy versus Gaia (angular distance, mas)</td><td></td><td>22</td><td></td><td></td><td>Section 4.2</td></tr><tr><td>Photometric repeatability (mmag)</td><td>4.9</td><td>5.0</td><td>4.5</td><td>5.4</td><td>Section 4.3</td></tr><tr><td>Photometric uniformity versus Gaia (mmag)</td><td></td><td>7.2</td><td></td><td></td><td>Section 4.3</td></tr><tr><td>Absolute photometric uncertainty (mmag)</td><td></td><td>&le;20</td><td></td><td></td><td>Section 4.5</td></tr><tr><td>Magnitude limit (PSF, S/N = 5)</td><td>24.3</td><td>23.9</td><td>23.5</td><td>22.8</td><td>Section 4.6</td></tr><tr><td>Magnitude limit (AUTO, S/N = 5)</td><td>23.9</td><td>23.5</td><td>23.0</td><td>22.4</td><td>Section 4.6</td></tr><tr><td>Galaxy selection (EXTENDED_CLASS &ge; 2, 19 &le; mag_auto_g &le; 22)</td><td></td><td>Eff. &gt; 99%; Contam. &lt; 2%</td><td></td><td></td><td>Section 4.7</td></tr><tr><td>Stellar selection (EXTENDED_CLASS &le; 1, 19 &le; mag_auto_g &le; 22)</td><td></td><td>Eff. &gt; 97%; Contam. &lt; 2%</td><td></td><td></td><td>Section 4.7</td></tr></tbody></table>

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 (sec(z) ~ 2) and have a systematically worse PSF FWHM. Exposures in the southern Galactic cap were required to have $t_{eff} > 0.2$ and $t_{eff} \times T_{exp} > 12$ s. 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 < 1"8 was applied in the southern Galactic cap. The resulting distribution of PSF FWHM and effective exposure time for the full DELVE DR2 data set are shown in Figure 2.

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, $\chi^2_{astrom} < 500$, $\Delta$ (R. A.) < 150 mas, and $\Delta$(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).<fnref n="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 wavelength-dependent criterion to remove exposures with poor PSF FWHM, resulting in a maximum PSF FWHM of {1"72, 1"62, 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).

<footnote n="88">⁸⁸ DELVE DR2 does not include the DES Y-band imaging.</footnote>
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).<fnref n="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 are then used to iteratively refine 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 the ATLAS Refcat2 system to the DECam system were 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 a significantly 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 ~3 deg² (nside = 32) HEALPix pixels (Górski et al. 2005), and within each HEALPix pixel, we grouped detections into clusters by associating all detections within a 0.0"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 single-epoch 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 $t_{eff} \times T_{exp}$), 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).<fnref n="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, $A_b = R_b \times E(B-V)$, 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 ray-tracing 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 0.0"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 deg² in each of the g, r, i, z bands, while ~17,000 deg² are jointly covered in all four bands (Figure 1). DELVE DR2 consists of a catalog of ~2.5 billion unique astronomical

<footnote n="89">⁸⁹ Associated configuration files can be found at https://github.com/delve-survey/delve_config.</footnote>

<footnote n="90">⁹⁰ Note that we do not apply the “error floor” applied by DES.</footnote>
<figure><img src="image_7.png" /><figcaption>**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).</figcaption></figure>

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<fnref n="91" /> package built on healsparse.<fnref n="92" /> This package maps the geometry of each CCD using higher-resolution nested HEALPix maps ($nside = 16,384$; ~166 arcsec²) and sums the resulting covered pixels to generate lower-resolution maps ($nside = 4096$; ~0.74 arcmin²) 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). We find 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 $16 < \text{WAVG\_MAG\_PSF} < 18$ mag and calculate the median rms scatter in ~0.2 deg² 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*, *z* photometry from DELVE to the Gaia *G* band 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 magnitude-dependent 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; Sevilla-Noarbe 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.

<footnote n="91">⁹¹ https://github.com/erykoff/decasu</footnote>

<footnote n="92">⁹² https://healsparse.readthedocs.io</footnote>
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Median difference between the DELVE DR2 photometry transformed into the Gaia G-band, $G_{\text{DELVE}}$, and the measured magnitude from Gaia EDR3, $G_{\text{Gaia}}$. 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).</figcaption></figure>

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 *i* bands
(e.g., Ivezić et 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*<sub>⊥</sub> ). (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 Ivezić et 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 *i* extinction-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 $w_{\perp}$ 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
fit at ($g-r$) = 0.7. This value is computed relative to a low-
extinction ($E(B-V)$) < 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, $w_{\perp}$, 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 $w_{\perp}$ 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, $N_{\text{eff}} = \text{MAGERR\_PSF}^2/\text{WAVG\_MAGERR\_PSF}^2$.
Assuming that $w_{\perp}$ comes from the statistical uncertainty in the
photometric calibration ($\sigma_{\text{stat}}$) and intrinsic width of the stellar
sequence ($w_{\perp,0}$) added in quadrature ($w_{\perp}^2 = \sigma_{\text{stat}}^2 + w_{\perp,0}^2$), we
can use the two measurements of $w_{\perp}$ and effective number of
observations ($N_{\text{eff}}$) for the WAVG measurement to solve for $\sigma_{\text{stat}}$
and $w_{\perp,0}$.

Distributions for $w_{\perp}$ in the DES region for the single
measurement and WAVG measurement cases are shown on the
right of Figure 5 in gray. We find a median single-
measurement (WAVG measurement) error of $\sigma_{\text{FGCM}} \sim 8$
mmag ($\sigma_{\text{FGCM,WAVG}} \sim 3$ mmag) for the region with zero-
points derived from FGCM and a median intrinsic width of
the stellar locus $w_{\perp,0} \sim 8$ mmag. To estimate $\sigma_{\text{stat}}$ for the
ATLAS Refcat2 calibrated region where the coverage is not
as homogeneous, we use the $w_{\perp,0}$ estimate from the FGCM
region. The median single-measurement (WAVG measure-
ment) error of $\sigma_{\text{ATLAS R2}} \sim 10$ mmag ($\sigma_{\text{ATLAS R2,WAVG}} \sim 7$
mmag) for the region with zero-points derived from ATLAS
Refcat2. This value of $\sigma_{\text{ATLAS R2,WAVG}}$ 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 $w_{\perp,0} = 8$ mmag can be thought of
as a lower limit over the rest of the sky. Therefore, the
inferred $\sigma_{\text{ATLAS R2}}$ 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*<sub>⊥</sub>, 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), we find a median rms in the
(*r* − *i*) color of the linear fit at (*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><img src="image_9.png" /></figure>

**Figure 5.** (Left) Spatial distribution of the measured width of the stellar locus $w_{\perp}$ 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 $w_{\perp}$ indicating a smaller statistical error in these measurements. (Right) Histogram of $w_{\perp}$ values ($w_{\perp} = \sqrt{\sigma^2 + w_{\perp,0}^2}$), where $w_{\perp,0} \sim 8$ 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 ($\sigma_{FGCM,WAVG} \sim 3$ mmag), and the rest of the DELVE footprint calibrated with ATLAS Refcat2 ($\sigma_{ATLAS R2,WAVG} \sim 7$ mmag). The gray histograms illustrate the difference in the measured width between the weighted-average (solid) and single best measurements (dotted).

compare the DES footprint to the rest of the DELVE using MAG PSF, we find 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 $\sigma = 5^\circ$ and fit a fifth-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 Drlica-Wagner 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><img src="image_10.png" /></figure>

**Figure 6.** (Top) Offset in the stellar locus $(r-i)$ color at $(g-r) = 0.7$ fit in 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 $\sigma = 5^\circ$ 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.
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 $\Delta g = 4.4$, $\Delta r = 23.3$, $\Delta i = 7.2$, and $\Delta z = 1.6$ 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, $F/\delta F$, via propagation of uncertainties and Pogson's law (Pogson 1856),

$$ \delta m = \frac{2.5}{\ln 10} \frac{\delta F}{F}. \qquad (1) $$

Using this equation, we estimate the magnitude at which DELVE DR2 achieves S/N = 5 ($\delta m \approx 0.2171$) and S/N = 10 ($\delta m \approx 0.1085$). We calculate these magnitude limits for point-like 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 $m$ and median($\delta m$) in ~12 arcmin² 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

<table><caption>**Table 2**
DELVE DR2 Median Depth Estimates</caption><thead><tr><th rowspan="2">Measurement</th><th colspan="4">Magnitude Limit</th></tr><tr><th>g<br>(mag)</th><th>r<br>(mag)</th><th>i<br>(mag)</th><th>z<br>(mag)</th></tr></thead><tbody><tr><td>MAG_PSF (S/N = 5)</td><td>24.3</td><td>23.9</td><td>23.5</td><td>22.8</td></tr><tr><td>MAG_PSF (S/N = 10)</td><td>23.5</td><td>23.1</td><td>22.7</td><td>22.1</td></tr><tr><td>MAG_AUTO (S/N = 5)</td><td>23.9</td><td>23.5</td><td>23.0</td><td>22.4</td></tr><tr><td>MAG_AUTO (S/N = 10)</td><td>22.8</td><td>22.5</td><td>22.1</td><td>21.4</td></tr></tbody></table>

**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).

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), we define extended_class parameters as a sum of several Boolean conditions,

$$ \begin{aligned} \text{extended\_class\_g} &= ((\text{spread\_model\_g} \\ & \quad + 3 \text{ spreaderr\_model\_g}) > 0.005) \\ & + ((\text{spread\_model\_g} \\ & \quad + \text{spreaderr\_model\_g}) > 0.003) \\ & + ((\text{spread\_model\_g} \\ & \quad - \text{spreaderr\_model\_g}) > 0.003). \end{aligned} \qquad (2) $$

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 g band has the widest coverage and deepest limiting magnitude.

In Figure 8 we characterize the performance of extended_class_g as 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 deg² and contains ~9.6 million matched objects. Following previous analyses (DES Collaboration 2018;
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>**Figure 7.** (Left) Distribution of PSF magnitude limits for pointlike sources at S/N = 5. The double-peaked structure in *r* band comes from the different exposure times used in DES and DECaLS. (Right) DELVE DR2 survey area in each band as a function of the limiting PSF magnitude (S/N = 5). These distributions look similar when calculated from the *mag_auto* limiting magnitude for all sources, but are shifted brighter by ~0.4 mag.</figcaption></figure>

Drlica-Wagner et al. 2021), we select pointlike sources from HSC-SSP PDR3 based on the difference between the *i*-band PSF and model magnitudes of sources,

$$ \begin{aligned} \text{hsc_stars} = & \\ & ((i\_psfflux\_mag - i\_cmodel\_mag) < 0.03) \\ & \parallel ((i\_psfflux\_mag - i\_cmodel\_mag) < 0.1) \\ & \& (i\_psfflux\_mag < 22). \end{aligned} \quad (3) $$

This scheme requires that the PSF and model magnitudes are very similar for fainter sources, while the agreement is relaxed for brighter sources. This selection results in ~7.1 million matched objects classified as galaxies and ~2.5 million matched objects classified as stars. We use these objects to evaluate the differential performance of DELVE DR2 *extended_class_g* as a function of magnitude in Figure 8. A nominal stellar sample (0 ≤ *extended_class_g* ≤ 1) contains ~621 million objects, while a nominal galaxy sample (2 ≤ *extended_class_g*) contains ~749 million objects. We report the integrated efficiency and contamination of these samples over the magnitude range 19 ≤ *mag_auto_g* ≤ 22 mag in Table 1.

The spatial number density of high-confidence stars (*extended_class_g* = 0) and high-confidence galaxies (*extended_class_g* = 3) is shown in Figure 9. The stellar density map clearly shows an increasing stellar density toward the Galactic plane, as well as the high stellar density associated with the Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC). The galaxy density map is dominated by the large-scale clustering of galaxies at high Galactic latitudes, but stellar contamination is apparent close to the Galactic bulge, LMC, and SMC. These maps have had a magnitude cut applied at *mag_auto_i* < 22 and have not been corrected for interstellar extinction, so some apparent variations in depth come from the extinction, while others come from actual variations in depth over the footprint.

## 4.8. Known Issues

1. The DESDM pipeline was designed for galaxy photometry at high Galactic latitudes. Sky subtraction and deblending suffer in regions of high stellar density. This leads to degraded photometry and object classification in these regions, most notably close to the Galactic plane and the Magellanic Clouds (Figure 9).

2. The star-galaxy classification efficiency varies over the footprint in a way that is found to correlate with imaging depth and object density. Care should be taken in regions of high density and/or spatially variable depth.

3. While the impact of scattered light from bright stars and failures in the sky background estimation have been mitigated in DELVE DR2 (Section 2), some localized, low-level catalog contamination does remain. The effects of scattered light may be further mitigated through the use of more advanced identification algorithms (e.g., Tano-glidis et al. 2022).

4. Spatial coverage maps were created at a resolution of *nside* = 16,384, corresponding to linear pixel dimensions of ~13". Thus, there are a small number of catalog objects that reside outside the coverage maps due to the slight inaccuracy at the CCD boundaries. These objects reside at the edges of the DELVE footprint and are <0.0001% of the catalog.

## 5. Data Access

Access to DELVE DR2 is provided through the Astro Data Lab (Fitzpatrick et al. 2016; Nikutta et al. 2020),<fnref n="93" /> part of the Community Science and Data Center (CSDC) hosted by NOIRLab. DELVE DR2 includes a main object table consisting of photometric measurements for ~2.5 billion objects. In addition, the Astro Data Lab has computed crossmatch tables between the DELVE DR2 catalog and catalogs from AllWISE, Gaia EDR3, NSC DR2, SDSS DR16, and unWISE DR1 (Cutri et al. 2021; Gaia Collaboration 2021; Nidever et al. 2021; Ahumada et al. 2020; Schlafly et al. 2019). These crossmatch tables and their reverse counterparts are served alongside the DELVE DR2 main object table at the Astro Data Lab (see Appendix B). The DELVE DR2 catalog data can be accessed via both a Table Access Protocol (TAP)<fnref n="94" /> service and from direct PostgreSQL queries via web-based, command-line, and programmatic query interfaces. In addition, the Astro Data Lab provides an image cutout service, built on the Simple Image Access (SIA) protocol, that can be used to access versions of the DELVE DR2 imaging data processed with the DECam Community Pipeline (Valdes et al. 2014).

<footnote n="93">⁹³ https://dataLab.noirlab.edu</footnote>

<footnote n="94">⁹⁴ http://ivoa.net/documents/TAP</footnote>
<figure><img src="image_13.png" /><figcaption>**Figure 8.** DELVE DR2 star/galaxy classification performance as a function of magnitude estimated from matched objects in the wide layer of HSC-SSP PDR3. (Left) Stellar efficiency and galaxy contamination for several stellar samples based on *extended_class_g*. (Right) Galaxy efficiency and stellar contamination as a function of magnitude for several galaxy samples based on *extended_class_g*.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Figure 9.** (Left) Stellar density map created with the *extended_class_g* = 0 (high-confidence stars) selection described in Section 4.7. (Right) Analogous galaxy counts map created with the *extended_class_G* = 3 (high-confidence galaxies) selection. The region of lower galaxy density toward the northeast of the footprint can be attributed to higher interstellar extinction, which is not corrected for in this map. Color range units are number of objects per arcmin². Both maps apply a magnitude threshold of *mag_auto_g* < 22.</figcaption></figure>

More detailed information on accessing the DELVE DR2 data can be found on the Astro Data Lab website.<fnref n="95" />

## 6. Summary

DELVE seeks to study the physics of dark matter and galaxy formation by observing resolved dwarf galaxies and stellar substructures in the Local Volume. To do so, DELVE has set out to complete contiguous deep imaging coverage of the southern high Galactic latitude sky. DELVE DR2 combines new observations with archival DECam data to cover >20,000 deg² individually in *g*, *r*, *i*, *z* and ~17,000 deg² in all four bands simultaneously. The DELVE DR2 catalog contains PSF and automatic aperture measurements for ~2.5 billion astronomical objects with a 5σ PSF depth of *g* = 24.3, *r* = 23.9, *i* = 23.5, *z* = 22.8 mag (Table 1). The DELVE DR2 data products are accessible through the NOIRLab Astro Data Lab.

As of 2022 January, DELVE has completed ~80% of its 126 nights of scheduled DECam observing. Additional DECam observations will increase the coverage, uniformity, and depth of future DELVE catalogs. Furthermore, we expect that future DELVE data releases will include products derived from image coaddition, as well as deeper targeted regions of the DELVE footprint. We anticipate that DELVE DR2 and future DELVE data releases will be a valuable resource for the community in advance of the Vera C. Rubin Observatory Legacy Survey of Space and Time.

The DELVE project is partially supported by Fermilab LDRD project L2019-011, the NASA Fermi Guest Investigator Program Cycle 9 grant 91201, and the National Science Foundation (NSF) under grant AST-2108168. This work is supported by the Fermilab Visiting Scholars Award Program from the Universities Research Association.

A.B.P. acknowledges support from NSF grant AST-1813881. J.L.C. acknowledges support from NSF grant AST-1816196. J. D.S. acknowledges support from NSF grant AST-1714873. S.R. M. acknowledges support from NSF grant AST-1909497. D.J.S. acknowledges support from NSF grants AST-1821967 and AST-1813708. D.C. acknowledges support from NSF grant AST-1814208. B.M.P. acknowledges support from the NSF Astronomy and Astrophysics Postdoctoral Fellowship under award AST-2001663. S.M. acknowledges support from the NSF Graduate Research Fellowship under grant DGE-1656518. D.M. D. acknowledges financial support from the State Agency for Research of the Spanish MCIU through the “Centre of Excellence Severo Ochoa” award for the Instituto de Astrofísica de Andalucía (SEV-2017-0709). C.P.M.B. and M.R.L.C. acknowledge support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 682115). L.S.S. acknowledges the financial support from FAPESP through the grant #2020/03301-5. J.A.C.B. acknowledges support from ANID FONDECYT Regular 1220083.

Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the

<footnote n="95">⁹⁵ https://datalab.noirlab.edu/delve</footnote>
Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo á Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungs-gemeinschaft and the Collaborating Institutions in the Dark Energy Survey.

The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l'Espai (IEEC/CSIC), the Institut de Física d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, NSF's NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium.

The DES data management system is supported by the National Science Foundation under grant Nos. AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and

306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2).

Based in part on observations at Cerro Tololo Inter-American Observatory at NSF's NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation.

This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.

This paper is based on data collected at the Subaru Telescope and retrieved from the HSC data archive system, which is operated by the Subaru Telescope and Astronomy Data Center (ADC) at NAOJ. Data analysis was in part carried out with the cooperation of Center for Computational Astrophysics (CfCA), NAOJ. We are honored and grateful for the opportunity of observing the Universe from Maunakea, which has the cultural, historical and natural significance in Hawaii.

This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.

**Facilities:** Blanco (DECam), Astro Data Lab, Gaia, Subaru (HSC).

**Software:** astropy (Astropy Collaboration 2018), fitsio,<fnref n="96" /> HEALPix (Górski et al. 2005),<fnref n="97" /> healpy (Zonca et al. 2019),<fnref n="98" /> healsparse,<fnref n="99" /> matplotlib (Hunter 2007), numpy (Harris et al. 2020), PSFEx (Bertin 2011), scipy (Virtanen et al. 2020), SCAMP(Bertin 2006), skymap,<fnref n="100" /> SourceExtractor (Bertin & Arnouts 1996).

## Appendix A
DECam Data

DELVE DR2 combines DECam observations acquired by 278 programs. These programs and the number of exposures they each contributed to DELVE DR2 are listed in Table 3.

<footnote n="96">⁹⁶ https://github.com/esheldon/fitsio</footnote>

<footnote n="97">⁹⁷ http://healpix.sourceforge.net</footnote>

<footnote n="98">⁹⁸ https://github.com/healpy/healpy</footnote>

<footnote n="99">⁹⁹ https://healsparse.readthedocs.io/en/latest/</footnote>

<footnote n="100">¹⁰⁰ https://github.com/kadrlica/skymap</footnote>
<table><caption>**Table 3**
DECam data included in DELVE DR2</caption><thead><tr><th>Prop. ID</th><th>PI</th><th>N<sub>exp</sub></th><th>Prop. ID</th><th>PI</th><th>N<sub>exp</sub></th><th>Prop. ID</th><th>PI</th><th>N<sub>exp</sub></th></tr></thead><tbody><tr><td>2012B-0001</td><td>Josh Frieman</td><td>63656</td><td>2018A-0909</td><td>Thomas H Puzia</td><td>121</td><td>2012B-0620</td><td>Jeremy Mould</td><td>23</td></tr><tr><td>2014B-0404</td><td>David Schlegel</td><td>28823</td><td>2015A-0631</td><td>Alfredo Zenteno</td><td>120</td><td>2021A-0010</td><td>Travis Rector</td><td>23</td></tr><tr><td>2019A-0305</td><td>Alex Drlica-Wagner</td><td>12459</td><td>2017B-0312</td><td>Bryan Miller</td><td>119</td><td>2019B-0080</td><td>Casey Papovich</td><td>23</td></tr><tr><td>2018A-0386</td><td>Alfredo Zenteno</td><td>3029</td><td>2019A-0265</td><td>Douglas P Finkbeiner</td><td>119</td><td>2013A-0737</td><td>Scott Sheppard</td><td>22</td></tr><tr><td>2013B-0440</td><td>David Nidever</td><td>2753</td><td>2016B-0124</td><td>Edo Berger</td><td>111</td><td>2016A-0622</td><td>Paulo Lopes</td><td>22</td></tr><tr><td>2019A-0272</td><td>Alfredo Zenteno</td><td>2452</td><td>2013B-0421</td><td>Armin Rest</td><td>107</td><td>2016A-0191</td><td>Armin Rest</td><td>22</td></tr><tr><td>2017A-0260</td><td>Marcelle Soares-Santos</td><td>2297</td><td>2020A-0058</td><td>Kathy Vivas</td><td>107</td><td>2012B-3001</td><td>Emmanuel Bertin</td><td>21</td></tr><tr><td>2021A-0149</td><td>Alfredo Zenteno</td><td>1886</td><td>2015B-0606</td><td>Katharine Lutz</td><td>106</td><td>2015A-0322</td><td>R Michael Rich</td><td>21</td></tr><tr><td>2016A-0366</td><td>Keith Bechtol</td><td>1870</td><td>2020A-0402</td><td>...</td><td>102</td><td>2019A-0240</td><td>...</td><td>20</td></tr><tr><td>2019B-0323</td><td>Alfredo Zenteno</td><td>1586</td><td>2015B-0187</td><td>Edo Berger</td><td>98</td><td>2018A-0371</td><td>Sangeeta Malhotra</td><td>20</td></tr><tr><td>2017A-0388</td><td>Alfredo Zenteno</td><td>1432</td><td>2017B-0906</td><td>Dougal Mackey</td><td>97</td><td>2012B-0625</td><td>Sarah Sweet</td><td>20</td></tr><tr><td>2018A-0242</td><td>Keith Bechtol</td><td>1423</td><td>2017A-0298</td><td>Brad Tucker</td><td>96</td><td>2014A-0496</td><td>Aren Heinze</td><td>20</td></tr><tr><td>2020A-0399</td><td>Alfredo Zenteno</td><td>1387</td><td>2018A-0159</td><td>Kathy Vivas</td><td>96</td><td>2019B-0256</td><td>Michael M Shara</td><td>20</td></tr><tr><td>2021A-0275</td><td>Armin Rest</td><td>1336</td><td>2014A-0339</td><td>Jonathan Hargis</td><td>95</td><td>2012B-0621</td><td>Loren Bruns</td><td>19</td></tr><tr><td>2018A-0273</td><td>William Dawson</td><td>1192</td><td>2014A-0622</td><td>Iraklis Konstantopoulos</td><td>92</td><td>2014B-0265</td><td>Ian Dell'Antonio</td><td>19</td></tr><tr><td>2018A-0913</td><td>Brad Tucker</td><td>1086</td><td>2020A-0910</td><td>Thomas H Puzia</td><td>91</td><td>2015B-0175</td><td>Anton Koekemoer</td><td>19</td></tr><tr><td>2013A-0741</td><td>David Schlegel</td><td>997</td><td>2018A-0380</td><td>Armin Rest</td><td>90</td><td>2013B-0627</td><td>Gastao B Lima Neto</td><td>18</td></tr><tr><td>2019A-0308</td><td>Ian Dell'Antonio</td><td>944</td><td>2019B-0403</td><td>Clara Martinez-Vazquez</td><td>87</td><td>2014A-0621</td><td>Dougal Mackey</td><td>18</td></tr><tr><td>2013A-0327</td><td>Armin Rest</td><td>900</td><td>2014A-0239</td><td>Mark Sullivan</td><td>83</td><td>2013A-9999</td><td>Alistair Walker</td><td>18</td></tr><tr><td>2014A-0624</td><td>Helmut Jerjen</td><td>817</td><td>2018B-0941</td><td>Alistair Walker</td><td>82</td><td>2014A-0634</td><td>David James</td><td>17</td></tr><tr><td>2017B-0279</td><td>Armin Rest</td><td>790</td><td>2018A-0137</td><td>Jeffrey Cooke</td><td>76</td><td>2014B-0611</td><td>Douglas P Geisler</td><td>17</td></tr><tr><td>2013A-0214</td><td>Maureen Van Den Berg</td><td>772</td><td>2014A-0429</td><td>Douglas P Finkbeiner</td><td>74</td><td>2013A-0386</td><td>Paul Thorman</td><td>16</td></tr><tr><td>2013A-0360</td><td>Anja von der Linden</td><td>737</td><td>2017B-0239</td><td>Keith Bechtol</td><td>72</td><td>2014A-0073</td><td>Mukremin Kilic</td><td>16</td></tr><tr><td>2013A-0724</td><td>Lori Allen</td><td>708</td><td>2013B-0612</td><td>Julio Chaname</td><td>71</td><td>2015A-0618</td><td>Chris Lidman</td><td>15</td></tr><tr><td>2018A-0914</td><td>Martin Makler</td><td>704</td><td>2019A-0065</td><td>Yue Shen</td><td>70</td><td>2014B-0375</td><td>Armin Rest</td><td>15</td></tr><tr><td>2015A-0608</td><td>Francisco Forster</td><td>638</td><td>2018B-0340</td><td>Herve Bouy</td><td>70</td><td>2014A-0386</td><td>Ian Dell'Antonio</td><td>15</td></tr><tr><td>2014A-0415</td><td>Anja von der Linden</td><td>604</td><td>2015A-0151</td><td>Annalisa Calamida</td><td>70</td><td>2014B-0610</td><td>Julio Chaname</td><td>14</td></tr><tr><td>2014A-0306</td><td>Xinyu Dai</td><td>559</td><td>2014A-0348</td><td>Haojing Yan</td><td>68</td><td>2012B-3005</td><td>Knut Olsen</td><td>14</td></tr><tr><td>2015A-0616</td><td>Helmut Jerjen</td><td>467</td><td>2017B-0285</td><td>Armin Rest</td><td>68</td><td>2019A-0337</td><td>David E Trilling</td><td>14</td></tr><tr><td>2016B-0909</td><td>Camila Navarrete</td><td>462</td><td>2017B-0078</td><td>Herve Bouy</td><td>68</td><td>2014B-0064</td><td>Mukremin Kilic</td><td>14</td></tr><tr><td>2013A-0614</td><td>Sarah Sweet</td><td>460</td><td>2019A-0235</td><td>...</td><td>67</td><td>2016A-0337</td><td>Genaro Suarez Castro</td><td>12</td></tr><tr><td>2016B-0301</td><td>Armin Rest</td><td>439</td><td>2018B-0905</td><td>Stree Oh</td><td>66</td><td>2017A-0951</td><td>Kathy Vivas</td><td>12</td></tr><tr><td>2019B-1014</td><td>Felipe Olivares</td><td>437</td><td>2014A-0632</td><td>Tiago Gonçalves</td><td>65</td><td>2013A-0351</td><td>Arjun Dey</td><td>12</td></tr><tr><td>2015A-0620</td><td>Ana Bonaca</td><td>430</td><td>2020A-0353</td><td>Eric Peng</td><td>65</td><td>2013B-0615</td><td>Julio Carballo-Bello</td><td>12</td></tr><tr><td>2014A-0035</td><td>Herve Bouy</td><td>427</td><td>2016A-0384</td><td>Jacqueline Cleary</td><td>64</td><td>2015A-0062</td><td>Linda French</td><td>12</td></tr><tr><td>2018B-0271</td><td>Douglas P Finkbeiner</td><td>424</td><td>2014A-0480</td><td>R Michael Rich</td><td>63</td><td>2019B-1013</td><td>Thomas H Puzia</td><td>12</td></tr><tr><td>2019A-0910</td><td>Dougal Mackey</td><td>424</td><td>2014A-0313</td><td>Kathy Vivas</td><td>62</td><td>2015A-0610</td><td>Cesar Fuentes</td><td>12</td></tr><tr><td>2015A-0110</td><td>Thomas De Boer</td><td>379</td><td>2015B-0307</td><td>Armin Rest</td><td>61</td><td>2014B-0613</td><td>Jeffrey Cooke</td><td>11</td></tr><tr><td>2014A-0270</td><td>Carl J Grillmair</td><td>363</td><td>2018A-0206</td><td>Abhijit Saha</td><td>61</td><td>2014B-0614</td><td>Iraklis Konstantopoulos</td><td>11</td></tr><tr><td>2016A-0189</td><td>Armin Rest</td><td>359</td><td>2015A-0617</td><td>David M Nataf</td><td>60</td><td>2012B-0623</td><td>Dougal Mackey</td><td>10</td></tr><tr><td>2013A-0411</td><td>David Nidever</td><td>358</td><td>2017A-0210</td><td>Alistair Walker</td><td>60</td><td>2016A-0095</td><td>Jeffrey Cooke</td><td>10</td></tr><tr><td>2016A-0618</td><td>Dougal Mackey</td><td>349</td><td>2013B-0617</td><td>Dougal Mackey</td><td>59</td><td>2016A-0951</td><td>...</td><td>10</td></tr><tr><td>2020A-0908</td><td>Felipe Olivares</td><td>339</td><td>2013A-0529</td><td>R Michael Rich</td><td>59</td><td>2015A-0175</td><td>Taran Esplin</td><td>9</td></tr><tr><td>2014A-0608</td><td>Francisco Forster</td><td>335</td><td>2014B-0193</td><td>Frederick M Walter</td><td>58</td><td>2013B-0453</td><td>Scott Sheppard</td><td>9</td></tr><tr><td>2016A-0190</td><td>Arjun Dey</td><td>333</td><td>2017B-0103</td><td>Wayne Barkhouse</td><td>58</td><td>2018B-0327</td><td>Sangeeta Malhotra</td><td>9</td></tr><tr><td>2021A-0922</td><td>Jose L Nilo Castellon</td><td>332</td><td>2019B-0042</td><td>Herve Bouy</td><td>57</td><td>2015A-0609</td><td>Julio Carballo-Bello</td><td>9</td></tr><tr><td>2020B-0241</td><td>Alfredo Zenteno</td><td>330</td><td>2014A-0613</td><td>David Rodriguez</td><td>57</td><td>2019A-0911</td><td>Jeffrey Cooke</td><td>9</td></tr><tr><td>2018A-0251</td><td>Douglas P Finkbeiner</td><td>324</td><td>2019A-0101</td><td>Patrick M Hartigan</td><td>57</td><td>2020B-0053</td><td>Dillon Brout</td><td>8</td></tr><tr><td>2018A-0276</td><td>Ian Dell'Antonio</td><td>304</td><td>2016A-0614</td><td>Thomas H Puzia</td><td>57</td><td>2017B-0330</td><td>Sangeeta Malhotra</td><td>7</td></tr><tr><td>2014A-0412</td><td>Armin Rest</td><td>303</td><td>2019B-0910</td><td>Yue Shen</td><td>55</td><td>2013A-0455</td><td>Scott Sheppard</td><td>7</td></tr><tr><td>2013A-0719</td><td>Abhijit Saha</td><td>291</td><td>2017B-0163</td><td>Prashin Jethwa</td><td>54</td><td>2012B-0416</td><td>David Nidever</td><td>7</td></tr><tr><td>2019A-0205</td><td>Daniel Goldstein</td><td>290</td><td>2013A-0612</td><td>Yun-Kyeong Sheen</td><td>53</td><td>2017B-0199</td><td>Anton Koekemoer</td><td>7</td></tr><tr><td>2018A-0215</td><td>Jeffrey Carlin</td><td>289</td><td>2017A-0913</td><td>Luidhy Santana da Silva</td><td>51</td><td>2013A-0609</td><td>Douglas P Geisler</td><td>7</td></tr><tr><td>2014A-0620</td><td>Andrew Casey</td><td>287</td><td>2014A-0610</td><td>Matthew Taylor</td><td>50</td><td>2020B-0021</td><td>Haojing Yan</td><td>7</td></tr><tr><td>2015A-0306</td><td>Eduardo Balbinot</td><td>280</td><td>2015A-0371</td><td>Armin Rest</td><td>50</td><td>2017A-0366</td><td>Sangeeta Malhotra</td><td>7</td></tr><tr><td>2014B-0244</td><td>Anja von der Linden</td><td>280</td><td>2016B-0173</td><td>Anton Koekemoer</td><td>49</td><td>2017B-0253</td><td>Jeffrey Carlin</td><td>6</td></tr><tr><td>2019B-0371</td><td>Marcelle Soares-Santos</td><td>280</td><td>2017A-0909</td><td>Jeffrey Cooke</td><td>49</td><td>2014A-0399</td><td>Christopher Johnson</td><td>6</td></tr><tr><td>2016B-0905</td><td>Helmut Jerjen</td><td>276</td><td>2015A-0615</td><td>Brendan McMonigal</td><td>49</td><td>2015B-0314</td><td>Brad Tucker</td><td>5</td></tr><tr><td>2017A-0914</td><td>Grant Tremblay</td><td>274</td><td>2017A-0308</td><td>Annalisa Calamida</td><td>48</td><td>2020A-0415</td><td>Armin Rest</td><td>5</td></tr><tr><td>2016A-0397</td><td>Anja von der Linden</td><td>263</td><td>2017A-0389</td><td>Armin Rest</td><td>48</td><td>2014A-0640</td><td>Amy Mainzer</td><td>5</td></tr><tr><td>2017A-0060</td><td>Denija Crnojevic</td><td>261</td><td>2014B-0609</td><td>Roberto R Munoz</td><td>47</td><td>2014B-0071</td><td>Sarah Sonnett</td><td>5</td></tr><tr><td>2017A-0281</td><td>Monika D Soraisam</td><td>256</td><td>2018A-0912</td><td>Attila Popping</td><td>45</td><td>2015B-0607</td><td>Jeffrey Cooke</td><td>5</td></tr><tr><td>2017A-0916</td><td>Julio Carballo-Bello</td><td>242</td><td>2021A-0246</td><td>...</td><td>44</td><td>2019B-1012</td><td>Jeffrey Cooke</td><td>5</td></tr><tr><td>2020A-0335</td><td>Lifan Wang</td><td>242</td><td>2020A-0238</td><td>Clara Martinez-Vazquez</td><td>43</td><td>2017B-0307</td><td>Scott Sheppard</td><td>4</td></tr></tbody></table>
<table><caption>**Table 3**
(Continued)</caption><thead><tr><th>Prop. ID</th><th>PI</th><th>N<sub>exp</sub></th><th>Prop. ID</th><th>PI</th><th>N<sub>exp</sub></th><th>Prop. ID</th><th>PI</th><th>N<sub>exp</sub></th></tr></thead><tbody><tr><td>2017B-0907</td><td>Ricardo Munoz</td><td>228</td><td>2019A-0325</td><td>Clara Martinez-Vazquez</td><td>43</td><td>2012B-0451</td><td>Scott Sheppard</td><td>4</td></tr><tr><td>2015A-0630</td><td>Thomas H Puzia</td><td>218</td><td>2020A-0142</td><td>Tom Shanks</td><td>42</td><td>2015A-0614</td><td>Jeffrey Cooke</td><td>4</td></tr><tr><td>2016A-0327</td><td>Douglas P Finkbeiner</td><td>216</td><td>2014B-0608</td><td>Yara Jaffe</td><td>41</td><td>2013B-0325</td><td>Kathy Vivas</td><td>4</td></tr><tr><td>2018B-0122</td><td>Armin Rest</td><td>213</td><td>2017A-0911</td><td>Ana Chies Santos</td><td>39</td><td>2020B-0288</td><td>Alexie Leauthaud</td><td>4</td></tr><tr><td>2012B-0569</td><td>Lori Allen</td><td>206</td><td>2020A-0909</td><td>Patricia Arevalo</td><td>39</td><td>2012B-0624</td><td>Aaron Robotham</td><td>4</td></tr><tr><td>2019A-0915</td><td>Jose Pena</td><td>191</td><td>2016A-0004</td><td>Ana Bonaca</td><td>38</td><td>2012B-3002</td><td>Josh Bloom</td><td>4</td></tr><tr><td>2015A-0619</td><td>Thiago Goncalves</td><td>186</td><td>2014A-0157</td><td>Andrej Favia</td><td>38</td><td>2015B-0603</td><td>Leopoldo Infante</td><td>4</td></tr><tr><td>2014A-0327</td><td>Armin Rest</td><td>183</td><td>2012B-0363</td><td>Josh Bloom</td><td>38</td><td>2015A-0177</td><td>Cristian Eduard Rusu</td><td>3</td></tr><tr><td>2018A-0059</td><td>Herve Bouy</td><td>182</td><td>2016A-0068</td><td>Thomas De Boer</td><td>38</td><td>2012B-0448</td><td>Paul Thorman</td><td>3</td></tr><tr><td>2015A-0163</td><td>Carl J Grillmair</td><td>179</td><td>2015B-0191</td><td>Sarah Rice</td><td>37</td><td>2014B-0378</td><td>Armin Rest</td><td>3</td></tr><tr><td>2018A-0911</td><td>Francisco Forster</td><td>174</td><td>2014A-0255</td><td>Anton Koekemoer</td><td>35</td><td>2013A-0613</td><td>Ricardo Munoz</td><td>3</td></tr><tr><td>2017B-0110</td><td>Edo Berger</td><td>174</td><td>2017B-0951</td><td>Kathy Vivas</td><td>35</td><td>2013A-0400</td><td>Josh Bloom</td><td>3</td></tr><tr><td>2016B-0910</td><td>Thomas H Puzia</td><td>174</td><td>2016B-0904</td><td>Igor Andreoni</td><td>33</td><td>2013A-0616</td><td>Geraint Lewis</td><td>2</td></tr><tr><td>2015A-0130</td><td>Denija Crnojevic</td><td>173</td><td>2019A-0060</td><td>Herve Bouy</td><td>33</td><td>2020A-0913</td><td>Jeremy Mould</td><td>2</td></tr><tr><td>2016B-0279</td><td>Douglas P Finkbeiner</td><td>170</td><td>2021A-0113</td><td>Melissa L Graham</td><td>33</td><td>2016A-0610</td><td>Leopoldo Infante</td><td>2</td></tr><tr><td>2013B-0614</td><td>Ricardo Munoz</td><td>167</td><td>2018A-0907</td><td>Ricardo Munoz</td><td>32</td><td>2013A-0608</td><td>Ricardo Demarco</td><td>2</td></tr><tr><td>2015A-0121</td><td>Anja von der Linden</td><td>160</td><td>2019B-1004</td><td>Julio Chaname</td><td>32</td><td>2014A-0191</td><td>Hendrik Hildebrandt</td><td>2</td></tr><tr><td>2019A-0917</td><td>Paulo Lopes</td><td>159</td><td>2012B-0506</td><td>Daniel D Kelson</td><td>32</td><td>2017B-0905</td><td>Jeremy Mould</td><td>2</td></tr><tr><td>2018A-0369</td><td>Armin Rest</td><td>156</td><td>2015A-0632</td><td>Cesar Briceno</td><td>31</td><td>2015B-0250</td><td>Jonathan Hargis</td><td>1</td></tr><tr><td>2017A-0918</td><td>Alexandra Yip</td><td>155</td><td>2013B-0531</td><td>Eric Mamajek</td><td>31</td><td>2012B-3016</td><td>Scott Sheppard</td><td>1</td></tr><tr><td>2013A-0611</td><td>Dougal Mackey</td><td>142</td><td>2018B-0904</td><td>Lee Splitter</td><td>30</td><td>2012B-0617</td><td>Robert I Hynes</td><td>1</td></tr><tr><td>2014B-0146</td><td>Mark Sullivan</td><td>141</td><td>2014A-0623</td><td>Ken Freeman</td><td>30</td><td>2013A-0610</td><td>Mario Hamuy</td><td>1</td></tr><tr><td>2014A-0256</td><td>Kathleen Eckert</td><td>136</td><td>2013A-0723</td><td>Eric Mamajek</td><td>28</td><td>2016A-0386</td><td>Sangeeta Malhotra</td><td>1</td></tr><tr><td>2015A-0205</td><td>Eric Mamajek</td><td>135</td><td>2019A-0315</td><td>Matthew Penny</td><td>28</td><td>2017A-0917</td><td>Franz Bauer</td><td>1</td></tr><tr><td>2014A-0321</td><td>Marla Geha</td><td>133</td><td>2013A-2101</td><td>Alistair Walker</td><td>28</td><td>2013B-0502</td><td>Ian Dell'Antonio</td><td>1</td></tr><tr><td>2017B-0904</td><td>Paulo Lopes</td><td>133</td><td>2015A-0107</td><td>Claudia Belardi</td><td>28</td><td>2013B-0613</td><td>Roberto R Munoz</td><td>1</td></tr><tr><td>2019A-0913</td><td>Julio Carballo-Bello</td><td>133</td><td>2013B-0438</td><td>Casey Papovich</td><td>26</td><td>2015A-0059</td><td>Sarah Sonnett</td><td>1</td></tr><tr><td>2015A-0397</td><td>Armin Rest</td><td>126</td><td>2013A-0621</td><td>Matias Gomez</td><td>25</td><td>2013A-0704</td><td>Matt A Wood</td><td>1</td></tr><tr><td>2019B-1010</td><td>Jose Pena</td><td>123</td><td>2016A-0104</td><td>Mark Sullivan</td><td>24</td><td></td><td></td><td></td></tr></tbody></table>

Note. Programs are ordered by the number of exposures contributed. The largest single contributors to the DELVE DR2 data set are DES, DECaLS and the DELVE program itself. Programs with no principal investigator (PI) listed are generally target-of-opportunity (ToO) or multi-PI programs.

## Appendix B
DELVE DR2 Tables

The DELVE DR2 catalog data are accessible through the DELVE_DR2.OBJECTS table hosted by the Astro Data Lab. This table includes the photometric properties assembled from a catalog-level co-add of the individual single-epoch measurements. The table columns are described in Table 4. In addition, crossmatches between objects in the DELVE DR2 catalog and objects within 1".5 from external catalogs are provided in individual tables:

1. DELVE_DR2.X1P5_OBJECTS_ALLWISE_SOURCE - AllWISE (Cutri et al. 2021)

2. DELVE_DR2.X1P5_OBJECTS_GAIA_EDR3_GAIA_SOURCE - Gaia EDR3 (Gaia Collaboration 2021)

3. DELVE_DR2.X1P5_OBJECTS_NSC_DR2_OBJECT - NSC DR2 (Nidever et al. 2021)

4. DELVE_DR2.X1P5_OBJECTS_SDSS_DR16_SPECOBJ - SDSS DR16 (Ahumada et al. 2020)

5. DELVE_DR2.X1P5_OBJECTS_UNWISE_DR1_OBJECT - unWISE DR1 (Schlaflay et al. 2019)

A template for the columns in these tables are described in Table 5. The schema for these tables are also described in detail on the Astro Data Lab website.
<table><caption>**Table 4**
DELVE. DR2 MAIN Table Description: 2,500,247,752 Rows; 126 Columns</caption><thead><tr><th>Column Name</th><th>Description</th><th>Columns</th></tr></thead><tbody><tr><td>QUICK_OBJECT_ID</td><td>Unique identifier for each object</td><td>1</td></tr><tr><td>RA</td><td>Right ascension derived from the median position of each detection (deg)</td><td>1</td></tr><tr><td>DEC</td><td>decl. derived from the median position of each detection (deg)</td><td>1</td></tr><tr><td>GLON</td><td>Galactic longitude derived from RA,DEC (deg)</td><td>1</td></tr><tr><td>GLAT</td><td>Galactic latitude derived from RA,DEC (deg)</td><td>1</td></tr><tr><td>ELON</td><td>Ecliptic longitude derived from RA,DEC (deg)</td><td>1</td></tr><tr><td>ELAT</td><td>Ecliptic latitude derived from RA,DEC (deg)</td><td>1</td></tr><tr><td>A_IMAGE_{G,R,I,Z}</td><td>Semi-major axis of adaptive aperture in image coordinates (pix)</td><td>4</td></tr><tr><td>B_IMAGE_{G,R,I,Z}</td><td>Semi-minor axis of adaptive aperture in image coordinates (pix)</td><td>4</td></tr><tr><td>CCDNUM_{G,R,I,Z}</td><td>CCD number for best exposure in each band</td><td>4</td></tr><tr><td>CLASS_STAR_{G,R,I,Z}</td><td>Neural-network-based star-galaxy classifier (see SourceExtractor manual for details)</td><td>4</td></tr><tr><td>EBV</td><td>E(B - V) value at the object location interpolated from the map of Schlegel et al. (1998)</td><td>1</td></tr><tr><td>EXPNUM_{G,R,I,Z}</td><td>Exposure number for best exposure in each band</td><td>4</td></tr><tr><td>EXPTIME_{G,R,I,Z}</td><td>Shutter-open exposure time for best exposure in each band</td><td>4</td></tr><tr><td>EXTENDED_CLASS_{G,R,I,Z}</td><td>Spread-model-based morphology class (see Section 4.7)</td><td>4</td></tr><tr><td>EXTINCTION_{G,R,I,Z}</td><td>–9 unknown, 0 high-confidence star, 1 likely star, 2 likely galaxy, 3 high-confidence galaxy</td><td>4</td></tr><tr><td>FLAGS_{G,R,I,Z}</td><td>Interstellar extinction calculated from Schlegel et al. (1998). Subtract these columns from the magnitude columns to correct for extinction (see Section 3).</td><td>4</td></tr><tr><td>HPX2048</td><td>SourceExtractor flags for the best detection in each band</td><td>4</td></tr><tr><td>HTM9</td><td>HEALPix index for each object in RING format at resolution nside = 2048</td><td>1</td></tr><tr><td>MAG_AUTO_{G,R,I,Z}</td><td>Automatic aperture magnitude derived from the best exposure in each band</td><td>4</td></tr><tr><td>MAGERR_AUTO_{G,R,I,Z}</td><td>Automatic aperture magnitude uncertainty derived from the best exposure in each band</td><td>4</td></tr><tr><td>MAG_PSF_{G,R,I,Z}</td><td>PSF magnitude derived from the best exposure in each band</td><td>4</td></tr><tr><td>MAGERR_PSF_{G,R,I,Z}</td><td>PSF magnitude uncertainty derived from the best exposure in each band</td><td>4</td></tr><tr><td>MJD_OBS</td><td>Median modified Julian date of the observations that were used to determine the astrometric position</td><td>1</td></tr><tr><td>NEPOCHS_{G,R,I,Z}</td><td>Number of single-epoch detections for this object</td><td>4</td></tr><tr><td>NEST4096</td><td>HEALPix index for each object in NEST format at resolution nside = 4096</td><td>1</td></tr><tr><td>RANDOM_ID</td><td>Random ID in the range 0.0 to 100.0 for subsampling</td><td>1</td></tr><tr><td>RING256</td><td>HEALPix index for each object in RING format at resolution nside = 256</td><td>1</td></tr><tr><td>SPREAD_MODEL_{G,R,I,Z}</td><td>Likelihood-based star-galaxy classifier (Desai et al. 2012)</td><td>4</td></tr><tr><td>SPREADERR_MODEL_{G,R,I,Z}</td><td>Likelihood-based star-galaxy classifier uncertainty (Desai et al. 2012)</td><td>4</td></tr><tr><td>T_EFF_{G,R,I,Z}</td><td>Effective-exposure-time scale factor for best exposure in each band (Neilsen et al. 2016)</td><td>4</td></tr><tr><td>THETA_IMAGE_{G,R,I,Z}</td><td>Position angle of automatic aperture in image coordinates (deg)</td><td>4</td></tr><tr><td>WAVG_FLAGS_{G,R,I,Z}</td><td>OR of SourceExtractor flags from all detections in each band</td><td>4</td></tr><tr><td>WAVG MAG_AUTO_{G,R,I,Z}</td><td>Weighted average of automatic aperture magnitude measurements in each band</td><td>4</td></tr><tr><td>WAVG MAGERR_AUTO_{G,R,I,Z}</td><td>Sum in quadrature of the automatic aperture magnitude uncertainties in each band</td><td>4</td></tr><tr><td>WAVG MAGRMS_AUTO_{G,R,I,Z}</td><td>Unbiased weighted standard deviation of the automatic aperture magnitude in each band</td><td>4</td></tr><tr><td>WAVG MAG_PSF_{G,R,I,Z}</td><td>Weighted average of PSF magnitude measurements in each band</td><td>4</td></tr><tr><td>WAVG MAGERR_PSF_{G,R,I,Z}</td><td>Sum in quadrature of the PSF magnitude uncertainties in each band</td><td>4</td></tr><tr><td>WAVG MAGRMS_PSF_{G,R,I,Z}</td><td>Unbiased weighted standard deviation of the PSF magnitude in each band</td><td>4</td></tr><tr><td>WAVG_SPREAD_MODEL_{G,R,I,Z}</td><td>Weighted average spread model in each band</td><td>4</td></tr><tr><td>WAVG_SPREADERR_MODEL_{G,R,I,Z}</td><td>Sum in quadrature of the spread model uncertainties in each band</td><td>4</td></tr><tr><td>WAVG_SPREADRMS_MODEL_{G,R,I,Z}</td><td>Unbiased weighted standard deviation of spread_model in each band</td><td>4</td></tr></tbody></table>

<table><caption>**Table 5**
Crossmatch Tables between DELVE DR2 and External Catalogs</caption><thead><tr><th>Column Name</th><th>Description</th><th>Columns</th></tr></thead><tbody><tr><td>DEC1</td><td>Decl. from DELVE DR2 (deg)</td><td>1</td></tr><tr><td>DEC2</td><td>Decl. from external catalog (deg)</td><td>1</td></tr><tr><td>DISTANCE</td><td>Angular separation between RA1,DEC1 and RA2,DEC2 (arcseconds)</td><td>1</td></tr><tr><td>ID1</td><td>ID in DELVE DR2 (quick_object_id)</td><td>1</td></tr><tr><td>ID2</td><td>ID in external catalog (source_id)</td><td>1</td></tr><tr><td>RA1</td><td>R.A. from DELVE DR2 (deg)</td><td>1</td></tr><tr><td>RA2</td><td>R.A. from external catalog (deg)</td><td>1</td></tr></tbody></table>
Appendix C
Depth

This appendix includes sky maps showing variations in the S/N = 5 depth of DELVE DR2 in the g, r, i, z bands. The

S/N = 5 depth was derived from the magnitude at which the median magnitude uncertainty was $\delta m = 0.2171$ mag (Section 4.6). These values were derived in $\sim 12$ arcmin$^2$ HEALPix pixels (nside = 1024) and are shown in Figure 10.

<figure><img src="image_15.png" /><figcaption>**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.</figcaption></figure>
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/0000-0002-3690-105X

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. Crnojević https://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/0000-0003-3835-2231

C. E. Martínez-Vázquez https://orcid.org/0000-0002-9144-7726

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/0000-0002-2598-0514

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. Tarle https://orcid.org/0000-0003-1704-0781

D. Thomas https://orcid.org/0000-0002-6325-5671

C. To https://orcid.org/0000-0001-7836-2261

E. J. Tollerud https://orcid.org/0000-0002-9599-310X

D. L. Tucker https://orcid.org/0000-0001-7211-5729

A. K. Vivas https://orcid.org/0000-0003-4341-6172

A. R. Walker https://orcid.org/0000-0002-7123-8943

J. Weller https://orcid.org/0000-0002-8282-2010

J. F. Wu https://orcid.org/0000-0002-5077-881X

B. Yanny https://orcid.org/0000-0002-9541-2678

A. Zenteno https://orcid.org/0000-0001-6455-9135

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