# The DECam Local Volume Exploration Survey: Overview and First Data Release

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950 N. Cherry Ave., Tucson, AZ 85719, USA  A. Scott NSF’s National Optical-
Infrared Astronomy Research Laboratory, 950 N. Cherry Ave., Tucson, AZ 85719,
USA Alex Drlica-Wagner<EMAIL_ADDRESS>
###### Abstract

The DECam Local Volume Exploration survey (DELVE) is a 126-night survey
program on the 4 m Blanco Telescope at the Cerro Tololo Inter-American
Observatory in Chile. DELVE seeks to understand the characteristics of faint
satellite galaxies and other resolved stellar substructures over a range of
environments in the Local Volume. DELVE will combine new DECam observations
with archival DECam data to cover ${\sim}\,15000\deg^{2}$ of high Galactic
latitude ($|b|>10\deg$) southern sky to a $5\sigma$ depth of $g,r,i,z\sim
23.5$ mag. In addition, DELVE will cover a region of ${\sim}\,2200\deg^{2}$
around the Magellanic Clouds to a depth of $g,r,i\sim 24.5$ mag and an area of
${\sim}\,135\deg^{2}$ around four Magellanic analogs to a depth of $g,i\sim
25.5$ mag. Here, we present an overview of the DELVE program and progress to
date. We also summarize the first DELVE public data release (DELVE DR1), which
provides point-source and automatic aperture photometry for ${\sim}\,520$
million astronomical sources covering ${\sim}\,5000\deg^{2}$ of the southern
sky to a $5\sigma$ point-source depth of $g{=}24.3$, $r{=}23.9$, $i{=}23.3$,
and $z{=}22.8$ mag. DELVE DR1 is publicly available via the NOIRLab Astro Data
Lab science platform.

Surveys – Catalogs – Dwarf galaxies – Magellanic Clouds – Local Group

††facilities: Blanco (DECam), Astro Data Lab, Gaia††software:  astropy
(Astropy Collaboration, 2018), fitsio,141414https://github.com/esheldon/fitsio
HEALPix (Górski et al., 2005),151515http://healpix.sourceforge.net healpy
(Zonca et al., 2019),161616https://github.com/healpy/healpy
healsparse,171717https://healsparse.readthedocs.io/en/latest/ matplotlib
(Hunter, 2007), numpy (Harris et al., 2020), PSFEx (Bertin, 2011), scipy
(Virtanen et al., 2020), SCAMP (Bertin, 2006),
skymap,181818https://github.com/kadrlica/skymap SourceExtractor (Bertin &
Arnouts, 1996), SWarp (Bertin et al., 2002; Bertin, 2010)

FERMILAB-PUB-21-075-AE-LDRD

##  1 Introduction

The standard model of cosmology ($\rm\Lambda CDM$) is strongly supported by
observations at large spatial scales (e.g., Planck Collaboration, 2020; DES
Collaboration, 2018b). However, this fundamental model for the growth and
evolution of our Universe remains challenging to test on scales smaller than
our Milky Way. Starting with the Sloan Digital Sky Survey (SDSS; York et al.,
2000), large digital sky surveys have revolutionized our understanding of
galaxies with stellar masses $\lesssim 10^{5}\mathrm{\,M_{\odot}}$ (see recent
reviews by McConnachie 2012 and Simon 2019). We now know that our Milky Way is
surrounded by scores (and likely hundreds) of faint galaxies, which span
orders of magnitude in luminosity (e.g., Drlica-Wagner et al., 2020, and
references therein). Discoveries of faint satellites around our nearest
galactic neighbors have begun to extend these studies beyond the Milky Way
(e.g., Martin et al., 2013; Chiboucas et al., 2013; Müller et al., 2015;
Carlin et al., 2016; Smercina et al., 2018; Crnojević et al., 2019).
Furthermore, we observe the tidal remnants of faint satellite galaxies
traversing our own Galactic halo (e.g., Ibata et al., 2001b; Belokurov et al.,
2006; Shipp et al., 2018) and the halos of other nearby galaxies (e.g., Malin
& Hadley, 1997; Ibata et al., 2001a; Martínez-Delgado et al., 2008; Mouhcine
et al., 2010). The discovery of the faintest galaxies and their remnants
represents an observational frontier for large digital sky surveys, while the
study of these systems continues to improve our understanding of $\rm\Lambda
CDM$ at the smallest observable scales.

The Dark Energy Camera (DECam; Flaugher et al., 2015), mounted on the 4 m
Blanco Telescope at Cerro Tololo Inter-American Observatory in Chile, is an
exceptional instrument for exploring the faintest stellar systems. The large
field of view ($3\deg^{2}$) and fast readout time ($27\mathrm{\,s}$) of DECam
allow it to quickly image large areas of the sky. DECam has been used by
several large survey programs, including the Dark Energy Survey (DES; DES
Collaboration, 2005, 2016), the Survey of the Magellanic Stellar History
(SMASH; Nidever et al., 2017, 2021b), and the DECam Legacy Surveys (DECaLS;
Dey et al., 2019). Furthermore, the astronomical community has used DECam for
targeted observing programs that have covered much of the remaining sky.

Here we present the DECam Local Volume Exploration survey
(DELVE),111https://delve-survey.github.io which is in the process of combining
126 nights of new DECam observations with existing public archival DECam data
to assemble contiguous multi-band coverage of the entire high Galactic
latitude ($|b|>10\deg$) southern sky (Figure 1). The DELVE program consists of
three main components: (1) DELVE-WIDE seeks to complete DECam coverage of
${\sim}\,15000\deg^{2}$ of the high-Galactic-latitude southern sky in
$g,r,i,z$; (2) DELVE-MC provides deeper contiguous coverage of $2200\deg^{2}$
in $g,r,i$ surrounding the Large and Small Magellanic Clouds (LMC and SMC);
and (3) DELVE-DEEP performs deep imaging of $135\deg^{2}$ in $g,i$ around four
Magellanic analogs in the Local Volume. Each survey component will combine new
observations with archival data that have been self-consistently processed
with state-of-the-art data management pipelines. The wide-area DELVE data are
processed with the DES Data Management pipeline (DESDM; Morganson et al.,
2018), which includes point-spread function (PSF) fitting and source modeling.
The deeper DELVE data around the Magellanic Clouds and Magellanic analogs are
processed with the multiepoch point-source fitting pipeline used by SMASH
(Nidever & Dorta, 2020) to enable deep, accurate photometry in these regions.
The DELVE data will be released on regular intervals with the first public
data release (DELVE DR1) documented here.

This paper summarizes the DELVE science program, survey design, progress to
date, and contents of the first data release. We start in Section 2 by
describing the scientific motivation for DELVE. In Section 3 we document the
survey strategy and observational progress to date. In Section 4 we describe
DELVE DR1, including the input data set, processing, validation, and data
access tools. We provide several scientific demonstrations of the DELVE data
in Section 5, and we conclude in Section 6. Throughout this paper, all
magnitudes are referenced in the AB system (Oke, 1974), and all astronomical
coordinates are provided in the Gaia-CRF2 reference frame (Gaia Collaboration,
2018b) unless explicitly noted otherwise.

Figure 1:  DELVE combines 126 nights of allocated time with public archival
data to cover the southern equatorial sky with DECam. DELVE will provide
contiguous multi-band imaging with a $5\sigma$ point-source depth of
$g,r,i,z\gtrsim 23.5$ mag over ${\sim}\,15000\deg^{2}$ (turquoise region). In
addition, a region of $2200\deg^{2}$ around the Magellanic Clouds will be
imaged to a depth of $g,r,i\geq 24.5$ mag (light-blue region). Deep fields
around four Magellanic analogs (Sextans B, NGC 300, NGC 55, and IC 5152) will
be imaged to a depth of $g,i\geq 25.5$ mag (dark-blue region). The Galactic
plane is indicated with a thick black line (dashed lines denote
$b={\pm}10\deg$), and the DES footprint is outlined in black. This figure uses
an equal-area McBryde–Thomas flat polar quartic projection in celestial
equatorial coordinates.

##  2 Scientific Motivation

The $\rm\Lambda CDM$ model predicts that galaxies like the Milky Way inhabit
large dark matter halos that grow hierarchically by merging with and/or
accreting smaller galaxies. There is ample evidence for the $\rm\Lambda CDM$
paradigm on large scales; however, small-scale tests are challenging due to
the low luminosities of the faintest galaxies that inhabit low-mass dark
matter halos. In particular, ultra-faint galaxies with stellar masses
$\lesssim 10^{5}\mathrm{\,M_{\odot}}$ have only been identified out to
distances of a few Mpc (e.g., McConnachie, 2012; Martin et al., 2013; Müller
et al., 2015; Carlin et al., 2016; Smercina et al., 2018; Crnojević et al.,
2019), while the census of the faintest dwarf galaxies (sometimes called
“hyper-faint” dwarf galaxies; Hargis et al. 2014) is incomplete even within
the Milky Way halo (e.g., Tollerud et al., 2008; Hargis et al., 2014; Kim et
al., 2018; Simon, 2019; Drlica-Wagner et al., 2020). Despite these
observational challenges, the faintest galaxies provide crucial information
about the role of environment and feedback on galaxy formation (e.g.,
Mashchenko et al., 2008; Wheeler et al., 2015, 2019; Munshi et al., 2019;
Agertz et al., 2020; Karunakaran et al., 2020), reionization and the first
stars (e.g., Bullock et al., 2000; Shapiro et al., 2004; Weisz et al., 2014a,
b; Boylan-Kolchin et al., 2015; Ishiyama et al., 2016; Weisz & Boylan-Kolchin,
2017; Tollerud & Peek, 2018; Graus et al., 2019; Katz et al., 2020), and the
nature of dark matter (e.g., Bergström et al., 1998; Spekkens et al., 2013;
Malyshev et al., 2014; Ackermann et al., 2015; Geringer-Sameth et al., 2015;
Brandt, 2016; Bullock & Boylan-Kolchin, 2017; Nadler et al., 2019, 2021).
Ultra-faint galaxies also provide a unique opportunity to study the creation
of heavy elements in some of the earliest star-forming environments (e.g.,
Frebel & Norris, 2015; Ji et al., 2016; Roederer et al., 2016).

DELVE seeks to improve our understanding of dark matter and galaxy formation
by studying the faintest satellite galaxies and their tidally disrupted
remnants in a range of environments. To accomplish this, DELVE consists of
three survey programs, each with a specific observational aim.

###  2.1 DELVE-WIDE

The DELVE-WIDE program will complete DECam coverage over the entire high
Galactic latitude ($|b|>10$) southern sky to provide a deep and accurate
census of ultra-faint satellite galaxies around the Milky Way. DELVE will
reach a photometric depth in $g,r,i,z$ that is comparable to that of the first
two years of DES. Early DELVE-WIDE data have already resulted in the discovery
of an ultra-faint satellite galaxy, Centaurus I (Mau et al., 2020). The full
DELVE-WIDE survey will enable the detection of ultra-faint satellites similar
to Centaurus I ($M_{V}=-5$ mag and
$\mu=27\mathrm{\,mag}\mathrm{\,arcsec}^{-2}$) out to the virial radius of the
Milky Way (${\sim}\,300\mathrm{\,kpc}$) with $>90\%$ efficiency (Drlica-Wagner
et al., 2020). The combined model of the Milky Way and LMC satellite galaxy
populations from Nadler et al. (2020) predicts that the DELVE footprint
(including the area covered by DES) contains $48\pm 8$ satellite galaxies that
are detectable by DELVE. Given the existing population of confirmed and
candidate satellite galaxies (Drlica-Wagner et al., 2020), this model predicts
that DELVE could discover over a dozen ultra-faint satellite galaxies.

The DELVE-WIDE program also provides exceptional sensitivity to stellar
streams, the remnants of tidally disrupted dwarf galaxies and globular
clusters. These resolved stellar structures provide insight into the formation
and evolution of the Milky Way stellar halo (e.g., Bullock & Johnston, 2005).
The composition, morphology, and orbital properties of detected structures
capture the recent accretion history of the Milky Way, including the masses,
orbits, and metallicities of recently accreted satellites (e.g., Bonaca et
al., 2021). Stellar streams also probe both the large- and small-scale
distribution of dark matter around the Milky Way: they trace the gravitational
potential of the Milky Way over a large range of radii (e.g., Johnston et al.,
1999; Bovy et al., 2016; Erkal et al., 2016b; Bonaca & Hogg, 2018), and they
offer a promising way to test dark matter clustering below the threshold of
galaxy formation (e.g., Johnston et al., 2002; Carlberg, 2013; Erkal et al.,
2016a; Banik et al., 2019).

The detection of stellar streams relies on deep, uniform coverage due to their
low surface brightnesses (${\sim}\,32\mathrm{\,mag}\mathrm{\,arcsec}^{-2}$)
and large extents on the sky (tens of degrees). Recent studies of stellar
streams have emphasized the synergy between deep photometry with DECam,
proper-motion measurements from Gaia, and radial velocities from massively
multiplexed spectroscopic instruments (e.g., Balbinot et al., 2016; Shipp et
al., 2018; Jethwa et al., 2018b; Shipp et al., 2019; Li et al., 2019, 2021;
Shipp et al., 2020; Bonaca et al., 2020). DELVE-WIDE will extend the study of
stellar streams by providing contiguous coverage across the southern
hemisphere.

DELVE-WIDE will also enable a broad range of extragalactic science due to its
wide, multi-band coverage. In particular, DELVE-WIDE will enable extended
southern-sky targeting for the Satellites Around Galactic Analogs (SAGA)
program, which seeks to study more massive and luminous companions of Milky
Way-like galaxies within 20–40$\mathrm{\,Mpc}$ (Geha et al., 2017; Mao et al.,
2021). In addition, DELVE-WIDE will enable the search for strong gravitational
lens systems, which can be used to probe the Hubble constant, dark energy, and
the small-scale structure of dark matter (e.g., Koopmans, 2005; Treu, 2010;
Oguri & Marshall, 2010; Vegetti et al., 2012; Treu et al., 2018; Gilman et
al., 2019; Wong et al., 2020). Furthermore, the DELVE-WIDE data can be used to
study galaxies and galaxy clusters in a range of environments. Several
examples of extragalactic science with DELVE-WIDE can be found in Section 5.

###  2.2 DELVE-MC

While it has long been hypothesized that the Magellanic Clouds (MCs) arrived
with their own population of dwarf companions (e.g., Lynden-Bell, 1976;
D’Onghia & Lake, 2008), the observational evidence for this model has been
strengthened by the discovery of many ultra-faint satellites surrounding the
MCs (e.g., Bechtol et al., 2015; Koposov et al., 2015; Drlica-Wagner et al.,
2015, 2016; Torrealba et al., 2018; Koposov et al., 2018; Cerny et al., 2021).
These discoveries have stimulated a flurry of interest in simulating and
modeling the satellite populations of the MCs. Simulations predict that up to
a third of the satellites around the Milky Way originated with the MCs (e.g.,
Deason et al., 2015; Wetzel et al., 2015; Jethwa et al., 2016; Dooley et al.,
2017b; Jahn et al., 2019; Nadler et al., 2020). Some studies suggest that the
MCs should host more luminous satellites than are observed (e.g., Dooley et
al., 2017b), while others suggest that the MCs should host more faint
satellites than are observed (e.g., Jahn et al., 2019). Furthermore, there is
significant observational uncertainty in associating known ultra-faint
galaxies with the MCs (Kallivayalil et al., 2018; Pardy et al., 2020; Erkal &
Belokurov, 2020; Patel et al., 2020).

One issue in determining the satellite luminosity function of the MCs comes
from the fact that the region around the MCs has only been observed by
relatively shallow contiguous surveys (e.g., Drlica-Wagner et al., 2016;
Mackey et al., 2018) and by deep surveys with low fill factors (e.g., Nidever
et al., 2017). The DELVE-MC program will provide deep, contiguous imaging of
the MCs and their surrounding environment to robustly measure the population
of faint satellites around the MCs. DELVE has already started to probe this
region with inhomogeneous early data, leading to the discovery an ultra-faint
star cluster (DELVE 2; $M_{V}=-2.1$) located 12$\mathrm{\,kpc}$ from the SMC
and 28$\mathrm{\,kpc}$ from the LMC (Cerny et al., 2021). The model of Nadler
et al. (2020) predicts that ${\sim}\,30\%$ of the ultra-faint galaxies
contained within the DELVE footprint are associated with the MCs.

The stellar masses, star formation histories, and interaction histories of the
MCs are expected to influence the properties of their satellite populations
(e.g., Jethwa et al., 2016; Dooley et al., 2017b; Jahn et al., 2019). SMASH
has used DECam to study the main bodies and periphery of the MCs with a deep,
partially filled survey (Nidever et al., 2017). Photometric metallicities from
SMASH suggest that the LMC periphery is not as metal-poor as would be expected
in a “classical” halo produced by $\rm\Lambda CDM$-style hierarchical
assembly. This observation is consistent with the hypothesis that the stellar
envelope of the LMC may be dominated by material from the outer LMC disk,
likely stirred up through a recent interaction with the SMC (Choi et al.,
2018a, b; Nidever et al., 2019). In parallel, recent observations of Gaia-
selected red giant branch (RGB) stars suggest that even more structure exists
in the periphery of the MCs (Belokurov & Erkal, 2019; Gaia Collaboration,
2021a). The deep, contiguous imaging of DELVE-MC will extend below the oldest
main-sequence turn off (MSTO) of the MCs and will be sensitive to faint
substructures that can provide clues about their interaction history (Massana
et al., 2020). Comparisons between the stellar populations in the bodies,
peripheries, and satellites of the MCs will help reconstruct the evolution of
the MCs and their satellite system as they are accreted onto the Milky Way.

DELVE-MC is also well suited to study the gravitational wake of the LMC. As
the LMC moves through the Milky Way stellar halo, it pulls stars towards
itself, creating an overdensity of stars along its past orbital path (e.g.,
Garavito-Camargo et al., 2019; Erkal et al., 2020). Recently, it has been
shown that the Pisces Overdensity (Watkins et al., 2009) matches the
properties of the expected wake (Belokurov et al., 2019). The wide coverage of
DELVE will give a more complete view of the Milky Way’s stellar halo close to
the LMC, allowing us to better map the wake of the LMC and test the effect of
dynamical friction. This, in turn, may constrain alternative dark matter
models that modify dynamical friction (e.g., Lancaster et al., 2020).

Figure 2: $B$-band absolute magnitude vs. distance for nearby galaxies
(Karachentsev et al., 2013). We highlight systems targeted by DELVE: ultra-
faint Milky Way satellites (turquoise circles), the LMC/SMC (blue diamonds),
and MC analogs in the Local Volume (navy diamonds). Also highlighted as light-
green diamonds are MC analogs with existing DECam data prior to DELVE. Little
is known about the faint dwarf galaxy population ($M_{\rm B}\gtrsim-10$)
beyond ${\sim}\,1\mathrm{\,Mpc}$, with the exception of a handful of
satellites around Milky Way-mass galaxies (vertical dashed lines). Dotted
horizontal lines denote the luminosities of the Milky Way, LMC, and SMC.

###  2.3 DELVE-DEEP

As our understanding of satellite galaxies and stellar substructures in the
Local Group has improved, searches for faint stellar systems have extended to
more distant galaxies. Within ${\sim}\,4\mathrm{\,Mpc}$, systematic imaging
searches for dwarf companions and tidal debris have been undertaken for
Centaurus A (e.g., Crnojević et al., 2016, 2019; Müller et al., 2019), M 101
(e.g., Merritt et al., 2014; Danieli et al., 2017; Bennet et al., 2019, 2020),
M 94 (Smercina et al., 2018), M 81 (Chiboucas et al., 2013; Okamoto et al.,
2015, 2019; Smercina et al., 2020), NGC 253 (e.g., Sand et al., 2014; Toloba
et al., 2016; Romanowsky et al., 2016), and 10 other (approximately) Milky
Way-mass hosts within the Local Volume (Carlsten et al., 2021, 2020). A
complementary approach has been taken by the SAGA Survey, which identifies
satellites of Milky Way-mass hosts located at 20–40$\mathrm{\,Mpc}$ via
spectroscopic verification of bright companions selected from SDSS and DECam
imaging (Geha et al., 2017; Mao et al., 2021).

The $\rm\Lambda CDM$ model predicts that the abundance of satellite galaxies
primarily depends on host halo mass (e.g., Behroozi et al., 2013; Moster et
al., 2013). However, scatter in the stellar mass–halo mass relation (e.g.,
Behroozi et al., 2013; Garrison-Kimmel et al., 2014, 2017; Munshi et al.,
2021), or the effects of reionization, tides, ram pressure stripping, and host
infall time, may be relatively more important for satellites of low-mass hosts
than they are for satellites of more massive hosts (e.g., Dooley et al.,
2017b). Indeed, recent studies have shown that environmental effects of MC-
mass hosts on their dwarf satellites are stronger than expected (e.g., Garling
et al., 2020; Carlin et al., 2019, 2021). While the discovery of ultra-faint
satellites associated with the MCs is broadly consistent with $\rm\Lambda
CDM$, interpreting the population of MC satellites in a cosmological context
is complicated by the current location of the MCs within the Milky Way’s
gravitational potential.

The DELVE-DEEP program targets nearby isolated galaxies with stellar masses
similar to those of the MCs (“MC analogs”). Observations of satellites around
isolated MC analogs will allow studies of satellite/host demographics in
environments outside the influence of a massive host. DELVE-DEEP will
complement existing DECam observations of the SMC-analog NGC 3109 (Sand et
al., 2015) and other similar surveys such as MADCASH (Carlin et al., 2016,
2019, 2021) and LBT-SONG (Davis et al., 2021). By combining results from the
DELVE-DEEP and other similar observing programs, it will be possible to
perform a statistically robust analysis of the satellite populations of MC
analogs.

DELVE-DEEP targets four isolated dwarf galaxies at 1.4–2.1$\mathrm{\,Mpc}$
(Figure 2 and Table 2.3). These galaxies were chosen as MC analogs: NGC 55 and
NGC 300 have stellar masses roughly comparable to the LMC, while Sextans B and
IC 5152 are within an order of magnitude of the SMC
(${\sim}\,0.1-0.8\,M_{\rm\star,SMC}$). NGC 55 is an irregular, barred spiral
galaxy (de Vaucouleurs et al., 1991); therefore, it closely resembles the LMC
not only in stellar mass but also in morphology. Unlike the LMC, NGC 55 is
almost edge-on, which makes it well suited for stellar content studies. NGC
300 is an almost pure disk galaxy and is similar to M33, an LMC-mass satellite
of M31. Both NGC 55 and NGC 300 belong to the nearby Sculptor Group, which is
actually an unbound filamentary structure extended along our line of sight
(Karachentsev et al., 2003). Both Sextans B and IC 5152 are irregular dwarf
galaxies. While Sextans B is a member of a very loose group of dwarf galaxies
(the NGC 3109 association; Tully et al. 2006), IC 5152 is an exceptionally
isolated object, with the nearest neighbor being NGC 55 at a distance of 800
kpc (Karachentsev et al., 2002). While all of our targets have been studied
with deep imaging observations (e.g., Tosi et al., 1991; Zijlstra & Minniti,
1999; Tanaka et al., 2011; Bellazzini et al., 2014; Hillis et al., 2016;
Rodríguez et al., 2016; Jang et al., 2020), systematic searches for dwarf
satellites have not been possible due to limited sky coverage.

DELVE-DEEP will provide sensitivity to resolved stars $\gtrsim 1.5$ mag below
the tip of the RGB of each target, enabling the detection of resolved
satellite galaxies with $M_{V}\lesssim-7$ (comparable to the brightest ultra-
faint satellites of the Milky Way). DELVE-DEEP imaging covers the halos of
each target ($r_{\rm vir}\sim 110\mathrm{\,kpc}$ for an SMC-mass galaxy; e.g.,
Dooley et al., 2017b) to provide a complete census of faint satellites with a
total area of ${\sim}\,135\deg^{2}$. According to predictions based on
$\rm\Lambda CDM$, we expect to discover 5–17 satellites with $M_{V}\lesssim-7$
around the four targets (Dooley et al., 2017a, b). In addition to searches for
faint satellites, the DELVE-DEEP data will enable detailed studies of the
target galaxies, including searches for globular clusters and measurement of
their stellar density profiles to large radii and low surface brightnesses
(e.g., Pucha et al., 2019).

Table 1: DELVE-DEEP isolated dwarf galaxy targets.

Target | R.A. | Decl. | Distance | $M_{B}$ | $M_{*}$ | $R_{110}$ | $N_{\rm sat,exp}$ | References
---|---|---|---|---|---|---|---|---
| (deg) | (deg) | (Mpc) | (mag) | ($M_{\odot}$) | (deg) |  |
NGC 55 | 3.79 | -39.22 | 2.1 | -18.4 | $3.0\times 10^{9}$ | 3.0 | 2–6 | (1)(2)(3)
NGC 300 | 13.72 | -37.68 | 2.0 | -17.9 | $2.6\times 10^{9}$ | 3.0 | 2–5 | (4)(2)(3)
Sextans B | 150.00 | 5.33 | 1.4 | -14.1 | $6.8\times 10^{7}$ | 4.4 | 0–2 | (1)(5)(6)
IC 5152 | 330.67 | -51.30 | 2.0 | -15.6 | $5.1\times 10^{8}$ | 3.2 | 1–4 | (7)(5)(8)

Note. — Stellar masses are derived from the $K$-band luminosity reported by
Karachentsev et al. (2013) assuming a $K$-band mass-to-light ratio of unity.
$R_{110}$ denotes the angular size of a 110 kpc region (roughly the virial
radius) at the distance of each target. $N_{\rm sat,exp}$ is the number of
expected satellites with $M_{\rm V}<-7$ according to Dooley et al. (2017a, b).

References. — Values come from the compilation of Karachentsev et al. (2013).
For each galaxy, we provide the original references for distance, absolute
magnitude, and $K$-band luminosity: (1) Dalcanton et al. (2009), (2) da Costa
et al. (1998), (3) Huchra et al. (2012), (4) Freedman et al. (2001), (5) de
Vaucouleurs et al. (1991), (6) Karachentsev et al. (2013), (7) Jacobs et al.
(2009), (8) Fingerhut et al. (2010)

##  3 Survey Design and Progress

DELVE began observing in 2019 February and has collected ${\sim}\,12000$ new
DECam exposures as of 2021 January. The DELVE imaging data are released via
the NOIRLab Astro Data Archive222https://astroarchive.noirlab.edu/ without any
proprietary period. Observations for the three DELVE programs are scheduled by
an automated open-source tool that optimizes for field availability, air mass,
sky brightness, and seeing.333https://github.com/kadrlica/obztak DELVE
observing has been performed on site at CTIO and from remote stations in
Tucson and at Fermilab. Remote observing from home commenced in 2020 October
due to the COVID-19 pandemic. The observational strategy and status for each
of the DELVE programs are described in more detail below.

###  3.1 DELVE-WIDE

DELVE-WIDE seeks to assemble contiguous DECam coverage over the entire
southern sky with $|b|>10\deg$ (Figure 1). Two-band photometry provides
sufficient color–magnitude information to separate old, metal-poor halo
populations from Milky Way foreground; however, additional color information
is useful for a wide range of science topics. Thus, DELVE-WIDE observes
preferentially in the $g,i$ bands and coordinates with other DECam programs to
process and assemble coverage in the $r,z$ bands (see Section 4). DELVE-WIDE
nominally performs $3\times 90\mathrm{\,s}$ dithered exposures in $g,i$ using
the same icosahedral tiling scheme used by DECaLS (Dey et al., 2019),444Based
on the scheme of Hardin, Sloane, and Smith
(http://neilsloane.com/icosahedral.codes) but with larger dithered offsets
(corresponding to roughly three times the DECam CCD dimensions). The DELVE
tiling scheme allows the entire $15000\deg^{2}$ DELVE-WIDE footprint to be
covered with three tilings in $g,i$ by ${\sim}\,43000$ DECam exposures.
However, a large fraction of the sky has already been covered by DES, DECaLS,
and other DECam programs. By preferentially targeting regions that lack
sufficiently deep DECam data, the DELVE-WIDE footprint can be contiguously
covered with 3 tilings in $g,i$ by ${\sim}\,20000$ new exposures. DELVE-WIDE
includes a fourth tiling when necessitated by observing constraints and
existing DECam coverage. As of 2021 January, DELVE-WIDE has collected 9778
exposures.

When scheduling DELVE-WIDE, we use public metadata to assess the coverage and
depth of archival DECam imaging. We calculate the effective exposure time for
each archival exposure from the shutter-open time, $T_{\rm exp}$, and the
effective exposure time scale factor, $t_{\rm eff}$, which combines the
achieved seeing, sky brightness, and extinction due to clouds to assess the
effective depth of an exposure (Neilsen et al., 2015). We create maps of the
summed effective exposure time in each band as a function of sky position at a
resolution of ${\sim}\,3\farcm 4$ (HEALPix $\texttt{nside}=1024$). Each DELVE-
WIDE target exposure is compared to the existing summed effective exposure
time at the target location of that exposure and is considered to have been
covered if $>67\%$ of the exposure area exceeds a minimum depth threshold. The
minimum depth of each DELVE-WIDE target exposure is calculated from the tiling
number, $N_{\rm tile}\in\\{1,2,3,4\\}$, the DELVE-WIDE shutter open time,
$T_{\rm exp,0}=90\mathrm{\,s}$, and the minimum effective depth scale factor,
$t_{\rm eff,min}$. A DELVE-WIDE exposure in tiling $N_{\rm tile}$ is
considered covered if the summed effective exposure time in that region of the
sky is

$\sum_{j}t_{{\rm eff},j}\times T_{{\rm exp},j}>N_{\rm tile}\times t_{\rm
eff,min}\times T_{\rm exp,0}.$ (1)

The cumulative exposure time from archival data (the left-hand side of the
above equation) is calculated from archival exposures with $t_{{\rm
eff},j}>0.2$. Motivated by experience from DES and other DECam programs, we
set the minimum effective exposure time threshold as $t_{{\rm eff,min},g}=0.4$
and $t_{{\rm eff,min},i}=0.5$. The DECam coverage map is updated with new
archival data each semester, while completed DELVE exposures are included in
real time during observing.

The DELVE-WIDE data are processed with the DESDM pipeline (Morganson et al.,
2018) and serve as the basis for DELVE DR1. The DELVE-WIDE data processing is
described in more detail in Section 4.2.

###  3.2 DELVE-MC

The DELVE-MC program seeks to map the periphery of the MCs by covering a
contiguous region of ${\sim}\,2200\deg^{2}$ extending $25\deg$ around the LMC
and $15\deg$ around the SMC to a depth comparable to that of SMASH, $g=24.8$,
$r=24.5$, and $i=24.2$ mag (Nidever et al., 2017, 2019). DELVE-MC observes in
three bands to leverage $(g-r)$ and $(r-i)$ colors to help separate compact
blue galaxies from Magellanic MSTO stars at $g\gtrsim 23$ mag (Nidever et al.,
2019). Roughly half of the DELVE-MC footprint has already been covered to the
desired depth by DES and SMASH, and DELVE is in the process of supplementing
the remaining ${\sim}\,1100\deg^{2}$ with ${\sim}\,2200$ new exposures. DELVE-
MC targets a total integrated exposure time of $800\mathrm{\,s}$ in $g,r$, and
$1000\mathrm{\,s}$ in $i$ by using $3\times 267\mathrm{\,s}$ dithered
exposures in $g,r$ and $3\times 333\mathrm{\,s}$ dithered exposures in $i$.
The DELVE-MC tiling scheme is the same as the DELVE-WIDE survey, and regions
of missing DECam coverage are determined following the procedure described in
Section 3.1. Due to the low elevation of the MCs as seen from CTIO, DECam
observations of the MCs generally have larger-than-average PSF FWHM values and
correspondingly lower-than-average $t_{\rm eff}$ values. Based on SMASH
observations, we set $t_{{\rm eff,min},g}=0.3$, $t_{{\rm eff,min},r}=0.3$, and
$t_{{\rm eff,min},i}=0.45$ when calculating the existing coverage. DELVE-MC
observations are scheduled when the PSF FWHM is $<1\farcs 1$ (as estimated for
an $i$-band exposure taken at zenith) to help improve crowded-field
photometry. As of 2021 January, DELVE-MC has collected 1467 exposures.

The high stellar density in the DELVE-MC region motivates us to process the
DELVE-MC data with a modified version of the PHOTRED pipeline (Nidever &
Dorta, 2020). This pipeline, called “DELVERED”, was created to better handle
large dithers between overlapping DELVE-MC exposures. DELVERED first performs
PSF photometry for each exposure and then performs forced PSF photometry for
overlapping exposures. At the exposure level, each night is processed
separately. This processing includes WCS correction for each CCD using Gaia
DR2, PSF photometry using DAOPHOT (Stetson, 1987, 1994), photometric zeropoint
determination, and aperture correction. The photometric zeropoint of each
exposure is estimated in the same manner as for the NOIRLab Source Catalog
(NSC; Nidever et al., 2018, 2021a, 2020) using all-sky catalogs and “model
magnitudes”. Forced PSF photometry is then performed on overlapping exposures
that pass quality cuts on seeing and zeropoints. DAOPHOT/ALLFRAME (Stetson,
1994) is run on all CCD images overlapping each $0.25\deg\times 0.25\deg$
patch of sky (referred to as a “brick” from the DECaLS tiling scheme) using a
master source list generated from a multi-band stacked image and
SourceExtractor. The ALLFRAME results are then calibrated using the zeropoints
and aperture corrections determined at the exposure level. Finally, weighted
mean magnitudes, coordinates, and photometric variability indices are
determined for each object using the measurements from the multiple exposures.

###  3.3 DELVE-DEEP

DELVE-DEEP performs deep DECam imaging around four isolated dwarf galaxies:
Sextans B, NGC 55, NGC 300, and IC 5152. DELVE-DEEP will achieve $5\sigma$
depths of $g=26.0\mathrm{\,mag}$ and $i=25.0\mathrm{\,mag}$ for each target
with total integrated exposure times of $4500\mathrm{\,s}$ in $g$ and
$3000\mathrm{\,s}$ in $i$. The nominal depth can be achieved in $15\times
300\mathrm{\,s}$ exposures in $g$ and $10\times 300\mathrm{\,s}$ exposures in
$i$. However, three of the four DELVE-DEEP targets (NGC 55, NGC 300, and IC
5152) reside in the DES footprint; thus, we decrease the required exposure
time by $900\mathrm{\,s}$ to account for the existing DES data and target
$12\times 300\mathrm{\,s}$ $g$-band exposures and $7\times 300\mathrm{\,s}$
$i$-band exposures for these targets.

The DELVE-DEEP fields are chosen to roughly cover the angular region
corresponding to the virial radius of the SMC ($r_{\rm vir}\sim
110\mathrm{\,kpc}$; Dooley et al., 2017b) at the distance of each target
(Table 2.3). This radius corresponds to ${\sim}\,3\deg$ for NGC 55, NGC 300,
and IC 5152, but it is significantly larger for Sextans B due to its proximity
(${\sim}\,4.4\deg$). Covering this large of a region around Sextans B is
prohibitive given the DELVE-DEEP time allocation, and the coverage around
Sextans B was reduced to ${\sim}\,3\deg$.

Observations of each target start with the central pointing and progress
outward. Small dithers of ${\sim}\,2\arcmin$ are applied in a hexagonal
pattern to each tiling to cover the gaps between CCDs. Star–galaxy separation
is critical to the DELVE-DEEP science, and observations are performed only
when the $i$-band zenith PSF is estimated to have ${\rm FWHM}<0\farcs 9$.
DELVE imaging for Sextans B was completed in 2019A, and imaging of NGC 55 is
${\sim}\,78\%$ complete as of 2020B. The DELVE-DEEP program has collected 419
exposures as of 2021 January.

The DELVE-DEEP data are processed with the DESDM image co-addition pipeline in
a manner similar to the DES deep fields (Hartley & Choi et al., 2020). While
this has allowed for early visual inspection and catalog analysis, the DESDM
pipeline is not optimized for stellar photometry in the crowded fields near
the DELVE-DEEP targets. We are exploring the use of DELVERED to perform
crowded-field stellar photometry in the DELVE-DEEP fields.

##  4 Data Release

The first DELVE data release, DELVE DR1, is based on new and archival DECam
data collected as part of DELVE-WIDE. DELVE DR1 consists of a catalog of
unique astronomical objects covering ${\sim}\,5000\deg^{2}$ in each of
$g,r,i,z$ and ${\sim}\,4000\deg^{2}$ in all of $g,r,i,z$ simultaneously. This
section describes the DELVE DR1 data selection, processing, characterization,
and validation. The DELVE DR1 catalog can be accessed through the NOIRLab
Astro Data Lab.555https://datalab.noirlab.edu/delve

Table 2:  DELVE DR1 key numbers and data quality summary.  Survey Characteristic | Band | Reference
---|---|---
| $g$ | $r$ | $i$ | $z$ |
Number of exposures | 8336 | 7713 | 6840 | 7040 | Section 4.1
Median PSF FWHM (arcsec) | 1.28 | 1.16 | 1.07 | 1.04 | Section 4.1
Sky coverage (individual bands, deg2) | 5599 | 5106 | 5065 | 5153 | Section 4.3
Sky coverage ($g,r,i,z$ intersection, deg2) | 4075 | Section 4.3
Astrometric repeatability (angular distance, $\mathrm{\,mas}$) | 24 | 26 | 25 | 28 | Section 4.4
Astrometric accuracy vs. Gaia (angular distance, $\mathrm{\,mas}$) | 22 | Section 4.4
Photometric repeatability (mmag) | 4.9 | 5.0 | 4.5 | 5.4 | Section 4.5
Photometric uniformity vs. Gaia (mmag) | 9.1 | Section 4.5
Absolute photometric uncertainty (mmag) | $\lesssim 20$ | Section 4.6
Magnitude limit (PSF, ${\rm S/N}=5$) | 24.3 | 23.9 | 23.3 | 22.8 | Section 4.7
Magnitude limit (AUTO, ${\rm S/N}=5$) | 23.9 | 23.4 | 22.9 | 22.4 | Section 4.7
Galaxy selection ($\texttt{{EXTENDED\\_COADD}}\geq 2$, $19\leq\texttt{{MAG\\_AUTO\\_G}}\leq 22$) | Eff. $>99\%$; Contam. $<3\%$ | Section 4.8
Stellar selection ($\texttt{{EXTENDED\\_COADD}}\leq 1$, $19\leq\texttt{{MAG\\_AUTO\\_G}}\leq 22$) | Eff. $>96\%$; Contam. $<2\%$ | Section 4.8

###  4.1 Data Set

DELVE DR1 consists of 29929 DECam exposures assembled from a combination of
DELVE observations and archival DECam data. The largest contributors to these
data are DELVE itself, the DECam eROSITA Survey (DeROSITAS; PI
Zenteno),666http://astro.userena.cl/derositas DECaLS (PI Schlegel; Dey et al.,
2019), and the Blanco Imaging of the Southern Sky Survey (BLISS: PI Soares-
Santos; Mau et al., 2019). However, over half of the exposures in DELVE DR1
come from $>150$ DECam community programs (Appendix A). These data were
downloaded from the NOIRLab Astro Data Archive.

The nominal DELVE DR1 region was defined as exposures having centroids with
${\rm Dec}<0\deg$ and $b>10\deg$. This region was extended to the Galactic
plane in the region of $120\deg<{\rm RA}<140\deg$ to enable an extended search
for the Jet stream (Jethwa et al., 2018a). Exposures were selected to have
exposure time $30\mathrm{\,s}<T_{\rm exp}<350\mathrm{\,s}$ and effective
exposure time scale factor $t_{\rm eff}>0.3$ (this is slightly higher than the
$t_{{\rm eff},j}>0.2$ requirement applied in Section 3.1). While no explicit
cut was placed on the PSF FWHM, the cut on $t_{\rm eff}$ removes exposures
with very poor seeing (Figure 3). Furthermore, exposures were required to have
good astrometric solutions when matched to Gaia DR2 by SCAMP (Bertin, 2006)
including $>250$ astrometric matches, $\chi^{2}_{\rm astrom}<500$,
$\Delta({\rm RA})<150\mathrm{\,mas}$, and $\Delta({\rm
Dec})<150\mathrm{\,mas}$. The key properties of the DELVE DR1 are listed in
Table 2.

Figure 3: PSF FWHM distributions for DECam exposures included in DELVE DR1.

###  4.2 Data Processing

All exposures in DELVE DR1 were consistently processed with the DESDM “Final
Cut” pipeline (Morganson et al., 2018). Identical processing was performed at
Fermilab and NCSA; DELVE DR1 is derived from the Fermilab processing. DELVE
implements updates to the DESDM pipeline that were developed for DES DR2 (see
Section 3 of DES Collaboration 2021). Data were reduced and detrended using
custom, seasonally averaged bias and flat images, and full-exposure sky
background subtraction was performed (Bernstein et al., 2018). 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). Photometric zeropoints for each
CCD were derived by performing a $1\arcsec$ match between the DELVE Final Cut
SourceExtractor catalogs and the ATLAS Refcat2 catalog (Tonry et al., 2018),
which places measurements from Pan-STARRS1 (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 (see Appendix B for details). Zeropoints derived from the DELVE processing
and photometric calibration pipeline were found to agree with those derived by
DES DR1 with an rms scatter of ${\sim}\,0.01\mathrm{\,mag}$.

We built a multi-band catalog of unique sources by combining the
SourceExtractor catalogs from each individual CCD image following the
procedure of Drlica-Wagner et al. (2015). We took the set of SourceExtractor
detections with $\texttt{{FLAGS}}<4$, which allows neighboring and deblended
sources, and $(\texttt{{IMAFLAGS\\_ISO}}\,\&\,2047)=0$, which removes objects
containing bad pixels within their isophotal radii (Morganson et al., 2018).
We further required that each detection have a measured automatic aperture
flux, a measured PSF flux, and a PSF magnitude error of $<0.5$ mag. We sorted
SourceExtractor detections into ${\sim}\,3\deg^{2}$ HEALPix pixels
($\texttt{nside}=32$), and within each HEALPix pixel we grouped detections
into clusters by associating all detections within a $1\arcsec$ radius.
Occasionally, two real astronomical objects are located within $1\arcsec$ of
each other and were grouped into the same cluster. We identified these
“double” objects if they were contemporaneously detected on a single image. If
a double object was detected in two or more images, we split its parent
cluster into two distinct clusters. We did not split clusters when more than
two objects were detected within $1\arcsec$. This splitting procedure means
that the DELVE DR1 catalog should be treated with care when searching for
multiple closely separated objects.

Each cluster of detections was associated with an object in the DELVE DR1
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 had the largest effective exposure time (i.e., the
largest $t_{\rm eff}\times T_{\rm exp}$), and (2) the weighted average (WAVG)
of the individual single-epoch measurements. The weighted average and unbiased
weighted standard deviation were calculated following the weighted sample
prescriptions used by DES (Gough, 2009; DES Collaboration,
2021).777https://www.gnu.org/software/gsl/doc/html/statistics.html#weighted-
samples In addition, we track cluster-level statistics such as the number of
detections in each band.

We follow the procedure of DES DR1 (DES Collaboration, 2018a) to calculate the
interstellar extinction from Milky Way foreground dust. We calculate the value
of $E(B-V)$ at the location of each catalog source by performing a bi-linear
interpolation in $({\rm RA},{\rm Dec})$ 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: $R_{g}=3.185$, $R_{r}=2.140$, $R_{i}=1.571$, and $R_{z}=1.196$
(DES Collaboration, 2018a). 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. The $A_{b}$
values are included for each object in DELVE DR1 but are not applied to the
magnitude columns by default. The list of the photometric and astrometric
properties provided in DELVE DR1 can be found in Appendix C.

###  4.3 Sky Coverage

We quantify the area covered by DELVE DR1 in the form of HEALPix maps with
angular resolution of ${\sim}\,0\farcm 86$ ($\texttt{nside}=4096$). These maps
were created by pixelizing the geometry of each DECam CCD using the decasu
888https://github.com/erykoff/decasu package built on
healsparse.999https://healsparse.readthedocs.io This package calculates the
geometry of each CCD using higher-resolution nested HEALPix maps
($\texttt{nside}=16384$) and sums the resulting covered pixels to generate
lower resolution maps containing the fraction of each pixel that is covered by
the survey. This bypasses the computationally intensive mangle processing done
by DES (Swanson et al., 2008; Morganson et al., 2018) while retaining the same
accuracy at a resolution of $\texttt{nside}=4096$. We quantitatively estimate
the covered area as the sum of the coverage fraction maps in each band
independently, as well as the intersection of the maps in all four bands.
These values are reported in Table 2 and visualized in Appendix D.

###  4.4 Astrometry

We assess the internal astrometric accuracy by comparing the distributions of
angular separations of individual detections of the same objects over multiple
exposures. The median global astrometric spread is $26\mathrm{\,mas}$ across
all bands. We find that this spread is fairly consistent within each band,
with median offsets in $g,r,i,z$ of $24,26,25,28\mathrm{\,mas}$. Furthermore,
we compare the DELVE DR1 catalog-coadd object locations to the locations of
matched sources in the Gaia DR2 catalog (Gaia Collaboration, 2018a), and we
find an overall astrometric agreement of $22\mathrm{\,mas}$. This comparison
is somewhat circular, since the Gaia DR2 catalog was used for the image-level
astrometric calibration; however, the good agreement confirms that no
significant astrometric offsets have been introduced by the catalog coaddition
procedure.

Figure 4: Median difference between the DELVE DR1 photometry transformed into
the Gaia $G$-band, $G_{\rm DELVE}$, and the measured Gaia magnitude, $G_{\it
Gaia}$. The spatial distribution of the median difference in each pixel is
shown in the left panel, while the right panel shows a histogram of the pixel
values. A shift in the zeropoint can be seen at ${\rm Dec}\sim-30\deg$, which
corresponds to the boundary between the ATLAS Refcat2 use of PS1 and SkyMapper
(Section 4.5). This comparison is restricted to the area with overlapping
DELVE DR1 coverage in all four bands ($g,r,i,z$).

###  4.5 Relative Photometric Calibration

We assess the photometric repeatability in each band from the root-mean-square
(rms) scatter between independent PSF measurements of bright stars. For each
band, we select stars with $16<\texttt{{WAVG\\_MAG\\_PSF}}<18$ mag and
calculate the median rms scatter in ${\sim}\,0.2\deg^{2}$ HEALPix pixels
($\texttt{nside}=128$). We estimate the median of the rms scatter over the
entire footprint in each band. This quantity is found to be
${\sim}\,5\mathrm{\,mmag}$ and is listed for each band in Table 2.

We validate the photometric uniformity of DELVE DR1 by comparing to space-
based photometry from Gaia DR2 (Figure 4). We transform the $g,r,i,z$
photometry from DELVE to the Gaia $G$ band following a set of transformations
derived for DES (Sevilla-Noarbe et al., 2021; DES Collaboration, 2021). We
compare the Gaia DR2 $G$-band magnitude in the AB system to the $G$-band AB
magnitude of stars in DELVE with $16<r<20\mathrm{\,mag}$ and
$0<i-z<1.5\mathrm{\,mag}$. We calculate the median difference, $G_{\rm
DELVE}-G_{\it Gaia}$, within each $\texttt{nside}=128$ HEALPix pixel (Figure
4). We find that the median offset between DELVE DR1 and Gaia DR2 is
$-4.8\mathrm{\,mmag}$. We estimate the photometric uniformity across the DELVE
DR1 data as the standard deviation of the median differences across pixels,
which yields a value of 9.1$\mathrm{\,mmag}$ (Table 2). Since the distribution
of residuals is non-Gaussian, we also calculate the 68% containment, which is
$15.3\mathrm{\,mmag}$.

Similar comparisons between DES and Gaia DR2 have 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). However, it is clear
from the spatial structure in Figure 4 that systematics in the DELVE DR1
calibration dominate the nonuniformity relative to Gaia. Furthermore, we
observe a systematic shift of $11.2\mathrm{\,mmag}$ relative to Gaia at ${\rm
Dec}=-30\deg$. This is the declination at which ATLAS Refcat2 switches from
using PS1 to SkyMapper, and a similar feature can be seen in the residuals of
Figure 3 in Tonry et al. (2018). This offset is the dominant contributor to
the broadening of the residuals between DELVE DR1 and Gaia DR2 seen in the
left panel of Figure 4. The relative photometric calibration of DELVE could be
improved in the future by performing an internal relative calibration such as
the “ubercalibration” of SDSS and PS1 (Padmanabhan et al., 2008; Schlafly et
al., 2012), or the forward global calibration of DES (i.e., Burke et al.,
2018).

Figure 5: (Left) Distribution of PSF magnitude limits for point-like sources
at S/N=5. (Right) DELVE DR1 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 ${\sim}\,0.4\mathrm{\,mag}$.

###  4.6 Absolute Photometric Calibration

We do not have a precise estimate of the absolute photometric calibration of
DELVE DR1, but a rough estimate can be derived by comparing to other data
sets. The absolute photometry of DELVE DR1 is tied to the HST CalSpec standard
star C26202 via DES DR2, which was used to adjust the zeropoints of the ATLAS
Refcat2 transformation equations (Appendix B). Thus, DELVE DR1 cannot have a
better absolute calibration than DES DR2, which sets a lower limit on the
statistical uncertainty of $2.2\mathrm{\,mmag}$ per band and a systematic
uncertainty of $11$ to $12\mathrm{\,mmag}$ per band (see Table 1 of DES
Collaboration 2021). The global offset between DELVE DR1 and Gaia DR2 (which
is not seen in DES DR2) suggests that the absolute calibration cannot be
better than $4.8\mathrm{\,mmag}$. Combining the maximum systematic uncertainty
on the absolute calibration from DES DR2 and the DELVE DR1 offset relative to
Gaia, we conservatively estimate that the absolute photometric accuracy of
DELVE DR1 is $\lesssim 20\mathrm{\,mmag}$. This is comparable to the absolute
photometric accuracy estimated for the DES first-year cosmology data set
(Drlica-Wagner et al., 2018). In the future, the absolute photometric
calibration of DELVE can be improved and verified directly following a similar
procedure to DES DR2 using HST CalSpec standards and/or pure hydrogen
atmosphere white dwarf stars (DES Collaboration, 2021).

###  4.7 Photometric Depth

The photometric depth of DELVE DR1 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),

$\delta m=\frac{2.5}{\ln 10}\frac{\delta F}{F}.$ (2)

Using this equation, we estimate the magnitude at which DELVE DR1 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 relationship between $m$ and ${\rm median}(\delta
m)$ in ${\sim}\,12\mathrm{\,arcmin}^{2}$ HEALPix pixels
($\texttt{nside}=1024$). The resulting median magnitude limits over the DELVE
DR1 footprint are shown in Table 3. We show histograms of the MAG_PSF_
magnitude limit for point-like sources at S/N=5 in the left panel of Figure 5.
In the right panel of Figure 5 we show the DELVE DR1 area as a function of
depth in each band. The magnitude limits as a function of location on the sky
are shown in Appendix D. The median S/N=10 point-source depth of DELVE DR1 is
comparable to the point-source depth from the first two years of DES (Drlica-
Wagner et al., 2015), which is consistent with the goal of the DELVE-WIDE
program.

Table 3:  DELVE DR1 median depth estimates.

Measurement |  | Magnitude Limit
---|---|---
|  | $g$ | $r$ | $i$ | $z$
|  | (mag) | (mag) | (mag) | (mag)
MAG_PSF  (S/N=5) |  | 24.3 | 23.9 | 23.3 | 22.8
MAG_PSF  (S/N=10) |  | 23.5 | 23.0 | 22.6 | 22.1
MAG_AUTO (S/N=5) |  | 23.9 | 23.4 | 22.9 | 22.4
MAG_AUTO (S/N=10) |  | 22.9 | 22.5 | 22.0 | 21.5

Note. — MAG_PSF depth is estimated using point-like sources while MAG_AUTO
depth measurements are derived from all DELVE DR1 sources (see Section 4.7).

###  4.8 Object Classification

DELVE DR1 includes the SourceExtractor SPREAD_MODEL_ parameter, which can be
used to separate spatially extended galaxies from point-like stars and quasars
(e.g., Desai et al., 2012). Following DES (e.g., DES Collaboration, 2018a,
2021), we define EXTENDED_CLASS_ parameters as a sum of several Boolean
conditions,

$\displaystyle\begin{split}\texttt{{EXT}}&\texttt{{ENDED\\_CLASS\\_G}}=\\\
~{}&((\texttt{{SPREAD\\_MODEL\\_G}}+3\,\texttt{{SPREADERR\\_MODEL\\_G}})>0.005)\\\
+&((\texttt{{SPREAD\\_MODEL\\_G}}+\texttt{{SPREADERR\\_MODEL\\_G}})>0.003)\\\
+&((\texttt{{SPREAD\\_MODEL\\_G}}-\texttt{{SPREADERR\\_MODEL\\_G}})>0.003).\end{split}$
(3)

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 EXTENDED_CLASS_G since the $g$ band has
the widest coverage and deepest limiting magnitude.

We evaluate the performance of EXTENDED_CLASS_G by matching DELVE DR1 objects
to data from the W04 (WIDE12H+GAMA15H) equatorial field of the wide layer of
HSC-SSP PDR2 (Aihara et al., 2019). The superior image quality ($i$-band PSF
FWHM ${\sim}\,0\farcs 6$) and depth ($i\sim 25.9\mathrm{\,mag}$) of HSC-SSP
PDR2 enable robust tests of star–galaxy separation in DELVE DR1. The matched
data set covers ${\sim}\,95\deg^{2}$ and contains ${\sim}\,2.3$ million
matched objects. Following DES Collaboration (2018a), we select point-like
sources from HSC-SSP DR2 based on the difference between the $i$-band PSF and
model magnitudes of sources,

$\displaystyle\begin{split}\texttt{{HSC}}&\texttt{{\\_STARS}}=\\\
&((\texttt{{I\\_PSFFLUX\\_MAG}}-\texttt{{I\\_CMODEL\\_MAG}})<0.03)\\\
&||~{}(~{}((\texttt{{I\\_PSFFLUX\\_MAG}}-\texttt{{I\\_CMODEL\\_MAG}})<0.1)\\\
&~{}~{}~{}\&~{}(\texttt{{I\\_PSFFLUX\\_MAG < 22}})~{}).\end{split}$ (4)

We require that the PSF and model magnitudes are very similar for fainter
sources, while the agreement is relaxed for brighter sources. This selection
results in ${\sim}\,1.7$ million matched objects classified as galaxies and
${\sim}\,0.6$ million matched objects classified as stars. We use these
objects to evaluate the differential performance of DELVE DR1 EXTENDED_CLASS_G
as a function of magnitude in Figure 6. We report the performance of nominal
star ($0\leq\texttt{{EXTENDED\\_CLASS\\_G}}\leq 1$) and galaxy
($2\leq\texttt{{EXTENDED\\_CLASS\\_G}}$) classifications integrated over the
magnitude range $19\leq\texttt{{MAG\\_AUTO\\_G}}\leq 22$ mag in Table 2.

Spatial maps of high-confidence stars and galaxies are shown in Figure 7. The
stellar density map clearly shows increasing stellar density toward the
Galactic plane, and the galaxy density map shows the large-scale clustering of
galaxies. Stellar contamination of the galaxy sample on the eastern edge of
the footprint correlates with the Galactic bulge. The underdense regions in
the northeast and southwest of the galaxy map correlate with interstellar
extinction, which has not been corrected for when creating this map.
Color–magnitude diagrams for looser selections of stars and galaxies are shown
in Figure 8.

Figure 6: _Top:_ Stars and galaxies occupy distinct regions of SPREAD_MODEL_
space. At fainter magnitudes it becomes harder to distinguish between the two
types of objects. _Middle:_ DELVE DR1 stellar classification performance as
estimated from matched HSC observations. _Bottom:_ Similar to the middle
panel, but showing DELVE DR1 galaxy classification performance.

Figure 7:  Left: Stellar density map created with the
$\texttt{{EXTENDED\\_CLASS\\_G}}=0$ selection described in Section 4.8. Right:
Analogous galaxy counts map created with the
$\texttt{{EXTENDED\\_CLASS\\_G}}=3$ 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 HEALPix $\texttt{nside}=512$ pixel. Both maps
use a magnitude threshold of $\texttt{{MAG\\_AUTO\\_G}}<22$. Figure 8:
Color–magnitude diagrams for objects in a ${\sim}\,30\deg^{2}$ region centered
on ${\rm RA},{\rm Dec}=(140,-20)\deg$. Left: PSF magnitudes for objects
classified as stars ($0\leq\texttt{{EXTENDED\\_CLASS\\_G}}\leq 1$). Right:
Automatic aperture magnitude for objects classified as galaxies
($\texttt{{EXTENDED\\_CLASS\\_G}}\geq 2$).

###  4.9 Known Issues

The DELVE DR1 detrending and image reduction pipeline used flat-field images,
bias images, bad pixel masks, and other image calibration products (i.e.,
“supercals”) assembled during the processing of DES Y4. These are believed to
be the best available instrument calibrations for exposures taken prior to
2017 September (DECam exposure numbers $<674970$). However, newer calibrations
are available from DES Y5 and Y6. Applying these calibrations to exposures
taken after 2017 September leads to a ${\sim}\,10\mathrm{\,mmag}$ shift in the
photometric zeropoints. While the DELVE DR1 calibration to ATLAS Refcat2
removes these shifts, we note that more recent processing of the DELVE data
uses the new DES calibration products and thus the derived zeropoints will
change slightly in future releases. Deriving updated calibration products
extending beyond the end of DES in 2019 February is a topic of ongoing work.

As noted in Section 4.5, there is a residual photometric offset of
$11.2\mathrm{\,mmag}$ that is apparent when comparing DELVE DR1 to Gaia DR2
across the boundary of ${\rm Dec}=-30\deg$. This likely arises because ATLAS
Refcat2 switches from PS1 to SkyMapper at this declination. Internal relative
photometric calibration of DELVE is expected to reduce inhomogeneities in the
photometric calibration in the future.

Regions around bright stars suffer from scattered and reflected light
artifacts (e.g., DES Collaboration, 2018a). Unlike DES, no effort was made to
identify and remove affected CCDs prior to DELVE processing. This can lead to
regions of higher spurious object rates, biased object colors, and incorrect
object sizes. These regions can often be identified at catalog level by
examining object colors (i.e., Section 7.4 of Drlica-Wagner et al., 2018), and
most affected exposures were removed during DELVE DR1 validation.

Several problematic regions can be seen as spurious overdensities in the
galaxy map shown in Figure 7. A prominent overdensity at $({\rm RA},{\rm
Dec})\sim(155.5,-8)\deg$ has been associated with a PSF fitting failure that
leads to incorrect star/galaxy separation. Another spurious overdensity at
$({\rm RA},{\rm Dec})\sim(190.5,-45)\deg$ (HEALPix 10491) corresponds to a
partial failure in the object matching procedure. Each of these issues has
been corrected in subsequent processing of the DELVE data.

The DESDM pipeline used to produce DELVE DR1 is not optimized for crowded-
field photometry. This leads to severe blending and incompleteness in regions
of high stellar density (see Section 4.6 of DES Collaboration 2018a). Blending
is likely responsible for some of the increased stellar contamination in the
galaxy sample close to the Galactic bulge seen in Figure 7.

In general, the catalog creation process merged object detections located
$<1\arcsec$ from each other on different exposures (Section 4.2). The process
for splitting two physical objects located within $1\arcsec$ required that
these objects were detected on more than one exposure. This splitting process
was found to be implemented suboptimally in DELVE DR1. Furthermore, two
objects were not split if they were only resolved in one exposure due to lack
of observations or reduced image quality in other observations. Finally,
higher-multiplicity systems (i.e., three or more objects within 1″) were
merged into a single object. Therefore, the DELVE DR1 catalog should be used
with caution when analyzing multiple sources separated by $<1\arcsec$. The
process of associating individual detections into a unique object catalog will
be improved in future data releases.

###  4.10 Data Access

Access to DELVE DR1 is provided through the Astro Data Lab (Fitzpatrick et
al., 2016; Nikutta et al., 2020),101010https://datalab.noirlab.edu part of the
Community Science and Data Center (CSDC) hosted by NOIRLab. The Astro Data Lab
is a science platform that provides online access and analysis services for
large survey data sets. It provides database access to catalog data from both
a Table Access Protocol (TAP)111111http://ivoa.net/documents/TAP service and
from direct PostgreSQL queries via web-based, command-line, and programmatic
query interfaces. The Astro Data Lab also provides the ability to cross-match
catalogs against other tables held by Astro Data Lab; a cross-match has
already been performed between DELVE DR1 and Gaia EDR3 (Appendix C; Gaia
Collaboration, 2021b). The Astro Data Lab also provides an image cutout
service, built on the Simple Image Access (SIA) protocol, that points at the
petabyte-scale holdings of NOIRLab’s Astro Data Archive, including the DELVE
DR1 images. Furthermore, the Astro Data Lab provides a JupyterHub-based
notebook analysis environment capable of accessing all of its data sets and
services, as well as remote storage space for user databases and files. We
used templates provided by the Astro Data Lab to build the DELVE DR1 web
pages121212https://datalab.noirlab.edu/delve and Jupyter notebooks
demonstrating DELVE DR1 data
access.131313https://github.com/noaodatalab/notebooks-
latest/blob/master/05_Contrib/Galactic/DELVE_DR1/DELVE_access_dr1.ipynb More
detailed information on using the data services for DELVE DR1 science can be
found on the Astro Data Lab website.

##  5 Science Examples

Below we present a few examples of scientific investigations that are possible
with the DELVE DR1 data. This list is not intended to be comprehensive, but
instead provides a glimpse at the wide range of topics that can be studied
with DELVE DR1.

###  5.1 Faint Milky Way Satellites

Figure 9: The Sextans dwarf spheroidal galaxy observed with DELVE DR1. Left:
Normalized spatial density of stellar sources
($0\leq\texttt{{EXTENDED\\_CLASS\\_G}}\leq 1$ using the prescription from
Section 4.8) smoothed with a $1\arcmin$ Gaussian kernel. Middle: Normalized
spatial density of galaxies ($\texttt{{EXTENDED\\_CLASS\\_G}}\geq 2$) smoothed
by the same kernel. Right: Color–magnitude diagram of all stellar sources
within $10\arcmin$ of the system centroid.

One of the primary science objectives of DELVE is the discovery of ultra-faint
satellites in the halo of the Milky Way. Early DELVE data have already
resulted in the discovery of three systems: Centaurus I, DELVE 1, and DELVE 2
(Mau et al., 2020; Cerny et al., 2021). Additional satellites are expected to
be discovered as the coverage, quality, homogeneity, and depth of the DELVE
data continue to improve.

DELVE DR1 includes several known stellar systems spanning a range of
luminosities. The DELVE DR1 footprint contains the Milky Way dwarf spheroidal
satellite galaxies Centaurus I (Mau et al., 2020), Hydra II (Martin et al.,
2015), Leo IV (Belokurov et al., 2007), and Sextans (Irwin et al., 1990).
Furthermore, DELVE DR1 contains several faint outer halo star clusters
including BLISS 1 (Mau et al., 2019), DELVE 1 (Mau et al., 2020), Kim 3 (Kim
et al., 2016), and Laevens 1/Crater 1 (Laevens et al., 2014; Belokurov et al.,
2014). The DELVE DR1 data can be used to study the extended stellar
populations of known dwarf galaxies and star clusters. Such studies can detect
signatures of tidal disturbance, which can help inform the evolutionary
history of these systems (e.g., Mutlu-Pakdil et al., 2019).

In Figure 9, we show an example of the DELVE DR1 data surrounding the Sextans
dwarf spheroidal galaxy, which is located at a distance of $84.7\pm 0.4$
$\mathrm{\,kpc}$ (Vivas et al., 2019). In the left (middle) panel, we plot the
smoothed spatial distribution of stars (galaxies) after applying a selection
based on EXTENDED_CLASS_G as presented in Section 4.8. We note similar
variations in the central stellar density of Sextans as were reported by
Roderick et al. (2016). In the right panel, we present a color–magnitude
diagram for the stars located within $10\arcmin$ of Sextans, demonstrating
that the DELVE DR1 $g$-band depth provides high-precision photometry down to
the subgiant branch of this system.

###  5.2 Stellar Streams

Figure 10: Leading tail of the disrupting globular cluster Palomar 5. Stellar
objects were selected from DELVE DR1 with
$0\leq\texttt{{EXTENDED\\_CLASS\\_G}}\leq 1$ and a color–magnitude filter
consistent with the stellar population of the Palomar 5 cluster (Section 5.2).

The contiguous, deep coverage of DELVE DR1 enables the discovery and study of
stellar streams at large Galactocentric distances (e.g., Odenkirchen et al.,
2001; Newberg et al., 2002; Belokurov et al., 2006; Grillmair, 2006; Bonaca et
al., 2012; Koposov et al., 2014; Bernard et al., 2014, 2016; Grillmair, 2017;
Drlica-Wagner et al., 2015; Balbinot et al., 2016; Shipp et al., 2018, 2020).
Following on other recent analyses (e.g., Shipp et al., 2019, 2020; Bonaca et
al., 2020; Li et al., 2021), DELVE DR1 photometry can be used to characterize
known streams in tandem with complementary proper-motion measurements from
Gaia and targeted spectroscopic follow-up. The phase-space distribution of the
population of stellar streams can be leveraged to construct a global map of
the Milky Way’s gravitational field (e.g., Bovy et al., 2016; Erkal et al.,
2016b; Bonaca & Hogg, 2018), while their internal structure may hold clues to
the particle nature of dark matter (Carlberg, 2013; Erkal et al., 2016a; Banik
et al., 2019).

In Figure 10, we show an example of the tidal disruption of the Palomar 5 (Pal
5) globular cluster (Odenkirchen et al., 2001). We select likely stars
($0\leq\texttt{{EXTENDED\\_CLASS\\_G}}\leq 1$; Section 4.8) consistent with
the main sequence of Pal 5 in a de-reddened color–magnitude diagram.
Specifically, we select stars consistent with an old ($11.5\mathrm{\,Gyr}$),
metal-poor (${\rm[Fe/H]}=-1.3$) isochrone from Dotter et al. (2008) shifted to
a distance of $22.5\mathrm{\,kpc}$. To mitigate effects of nonuniform depth,
we only include stars that are brighter than $g=23.1$ mag. The leading arm of
Pal 5 resides in the DELVE DR1 footprint and is prominently detected extending
to ${\rm Dec}\lesssim-5\deg$, confirming similar results from Bonaca et al.
(2020).

###  5.3 Strong Lensing

Figure 11: DELVE composite color images ($g,r,i$) of the quadruply lensed
quasar systems J1131GL (left; Sluse et al., 2003) and 2M J1134-2103 (right;
Lucey et al., 2018).

Strong gravitational lens systems are excellent probes of galaxy structure,
dark matter, and dark energy (e.g., Treu, 2010). The total catalog of
confirmed strong lens systems numbers only in the hundreds (e.g., Jacobs et
al., 2019b, and references therein), but recent wide-area surveys have rapidly
increased the number of candidate systems through both visual inspection of
image data and the use of machine-learning techniques. Thousands of new lens
candidates have been identified in DES (e.g., Nord et al., 2016; Diehl et al.,
2017; Jacobs et al., 2019a, b), DECaLS (e.g., Huang et al., 2020, 2021), and
PS1 (e.g., Cañameras et al., 2020). DELVE is expected to contain several
thousand galaxy–galaxy lenses and ${\sim}\,100$ lensed quasars brighter than
$i\sim 18.5\mathrm{\,mag}$ based on the estimates of Collett (2015) and Treu
et al. (2018), respectively. In Figure 11, we show DELVE images of two
quadruply lensed quasars: J1131GL (left; Sluse et al., 2003) and 2M J1134-2103
(right; Lucey et al., 2018). DELVE has partnered with the STRong lensing
Insights into the Dark Energy Survey (STRIDES; Treu et al., 2018) to initiate
searches for previously undiscovered galaxy–galaxy lenses and lensed quasars
using the DELVE data.

###  5.4 Galaxies in Different Environments

While DELVE primarily focuses on faint galaxies in the Local Volume, the DELVE
DR1 data can also be used to study more luminous galaxies at larger distances.
The wide coverage and depth of DELVE enable the investigation of galaxy
populations spanning a range of different environments (from isolated galaxies
to galaxy clusters). The study of galaxies in different environments can help
reveal the mechanisms responsible for variations in the structure and physical
properties of galaxies as they form and evolve over cosmic time (e.g., Darvish
et al., 2018; Chartab et al., 2020). As an example, early community data
processed by DELVE have been used to perform environmental studies of local
analogs to high-redshift galaxies (Santana-Silva et al., 2020).

Tidal streams around massive galaxies are a ubiquitous aspect of galaxy
formation that has not yet been fully explored due to the deep imaging
required to detect these systems beyond the Local Group (Bullock & Johnston,
2005; Cooper et al., 2010; Martínez-Delgado et al., 2010; Morales et al.,
2018). A systematic survey for galactic remnants in a large sample of nearby
galaxies is needed to understand whether the recent merger histories of the
Milky Way and Andromeda are “typical.” DELVE has partnered with the Stellar
Stream Legacy Survey (Martinez-Delgado et al., 2021), a systematic imaging
survey of tidal features around nearby galaxies that has reached surface
brightnesses as faint as ${\sim}\,29\mathrm{\,mag}\mathrm{\,arcsec}^{-2}$
using public data from the DESI Legacy Imaging Surveys (Dey et al., 2019). The
DELVE data will enable a search for extragalactic stellar streams around
several thousand galaxies in the southern hemisphere. A catalog of
extragalactic stellar streams will probe the current mass assembly rate of
nearby galaxies, the stellar content and orbits of satellites, and the
formation of stellar halos.

###  5.5 Galaxy Clusters

Figure 12: DELVE DR1 data around the rich galaxy cluster Abell 1689 at a
redshift of $z=0.18$. (Left) False-color image assembled from $g,r,z$ images
covering a $5\farcm 8\times 5\farcm 8$ region around Abell 1689. (Right)
Color–magnitude diagram of galaxies in Abell 1689 after a statistical
background subtraction based on the field galaxy population. The red solid
line is a linear red sequence relationship derived from the DES Y1 redMaPPer
cluster catalog (McClintock et al., 2019), and the red dashed lines represent
the expected 95% containment region for red sequence galaxies. The red markers
are spectroscopically confirmed members of the system.

Galaxy clusters are a powerful tool for studying cosmology and galaxy
evolution in dense environments (Allen et al., 2011; Zenteno et al., 2016;
Hennig et al., 2017; Bocquet et al., 2019; DES Collaboration, 2020).
Assembling a large population of galaxy clusters is essential for improving
measurements of cosmological parameters (Hildebrandt et al., 2020; DES
Collaboration, 2018b; McClintock et al., 2019). DELVE collaborates closely
with DeROSITAS (PI: Zenteno) to assemble contiguous imaging in $g,r,i,z$ and
enable wide-area optical studies of galaxy clusters. The optical data can be
combined with data from large surveys in the submillimeter (e.g., Bleem et
al., 2020; Hilton et al., 2021) and X-ray (e.g., eROSITA; Predehl et al.,
2021) regimes. Multiwavelength studies of galaxy clusters can help mitigate
systemics in optical studies (Grandis et al., 2021) and provide information
about the dynamical state of galaxy clusters, which is important for
cosmological measurements (e.g., Andrade-Santos et al., 2012, 2017; Lovisari
et al., 2020a, b) and studies of galaxy evolution (e.g., Zenteno et al.,
2020).

As a proof of concept, Figure 12 shows DELVE DR1 data associated with the rich
galaxy cluster Abell 1689 (e.g., Limousin et al., 2007). We use a kernel-
density estimate to subtract likely field galaxies from the color–magnitude
diagram (Pimbblet et al., 2002), and we recover a prominent red sequence
without the need for spectroscopic membership information or photometric
redshifts. The colors and magnitudes of DELVE DR1 galaxies (black circles)
follow the linear red sequence relation derived from the DES Y1 redMaPPer
catalog (red line; McClintock et al., 2019) demonstrating consistency between
these data sets in the galaxy cluster regime. In the future, we plan to
assemble a catalog of galaxy clusters by using a cluster finder algorithm such
as redMaPPer (Rykoff et al., 2014) or WAZP (Aguena et al., 2021). These
algorithms have been validated on DES data and can be naturally adapted to
DELVE data.

##  6 Summary

DELVE seeks to study the fundamental physics of dark matter and galaxy
formation through rigorous systematic studies of dwarf galaxies and stellar
substructures in the Local Volume. To do so, DELVE aims to complete contiguous
coverage of the southern, high Galactic latitude sky in $g,r,i,z$ to a depth
comparable to two years of DES observations. As of 2021 January, DELVE has
completed slightly less than half of the 126 nights of scheduled DECam
observing. The first major public release of DELVE DR1 combines observations
from the DELVE-WIDE program with archival DECam data to cover
${\sim}\,4000\deg^{2}$ of the northern Galactic cap to a $5\sigma$ depth of
$g=24.3,r=23.9,i=23.3,z=22.8\mathrm{\,mag}$ (Table 2). The DELVE DR1 catalog
contains PSF and automatic aperture measurements for ${\sim}\,520$ million
astronomical objects produced by the DESDM pipeline and accessible through the
NOIRLab Astro Data Lab.

DELVE DR1 has utility for a broad range of scientific investigations in the
Local Volume and beyond. Future DELVE data releases will increase the
coverage, uniformity, and depth of the DELVE catalogs. Furthermore, these
releases will include products from the DELVE-MC and DELVE-DEEP programs,
which are processed with the multiepoch point-source pipeline from SMASH. We
anticipate that DELVE DR1 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.

##  7 Acknowledgments

The DELVE project is partially supported by Fermilab LDRD project L2019-011
and the NASA Fermi Guest Investigator Program Cycle 9 No. 91201. This work is
supported by the Visiting Scholars Award Program of the Universities Research
Association. ABP acknowledges support from NSF grant AST-1813881. This
research received support from the National Science Foundation (NSF) under
grant no. NSF DGE-1656518 through the NSF Graduate Research Fellowship
received by SM. JLC acknowledges support from NSF grant AST-1816196. JDS
acknowledges support from NSF grant AST-1714873. SRM acknowledges support from
NSF grant AST-1909497. DJS acknowledges support from NSF grants AST-1821967
and 1813708. DMD 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). CPMB and
MRLC acknowledge support from the European Research Council (ERC) under the
European Union’s Horizon 2020 research and innovation programme (grant
agreement no. 682115).

This project used data obtained with the Dark Energy Camera (DECam), which was
constructed by the Dark Energy Survey (DES) collaboration. Funding for the DES
Projects has been provided by the DOE and NSF (USA), MISE (Spain), STFC (UK),
HEFCE (UK), NCSA (UIUC), KICP (U. Chicago), CCAPP (Ohio State), MIFPA (Texas
A&M University), CNPQ, FAPERJ, FINEP (Brazil), MINECO (Spain), DFG (Germany),
and the collaborating institutions in the Dark Energy Survey, which are
Argonne Lab, UC Santa Cruz, University of Cambridge, CIEMAT-Madrid, University
of Chicago, University College London, DES-Brazil Consortium, University of
Edinburgh, ETH Zürich, Fermilab, University of Illinois, ICE (IEEC-CSIC), IFAE
Barcelona, Lawrence Berkeley Lab, LMU München, and the associated Excellence
Cluster Universe, University of Michigan, NSF’s National Optical-Infrared
Astronomy Research Laboratory, University of Nottingham, Ohio State
University, OzDES Membership Consortium University of Pennsylvania, University
of Portsmouth, SLAC National Lab, Stanford University, University of Sussex,
and Texas A&M University.

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.

Based on observations at Cerro Tololo Inter-American Observatory, NSF’s
National Optical-Infrared Astronomy Research Laboratory (2019A-0305; PI:
Drlica-Wagner), which is operated by the Association of Universities for
Research in Astronomy (AURA) under a cooperative agreement with the National
Science Foundation.

This manuscript has been authored by Fermi Research Alliance, LLC, under
contract No. DE-AC02-07CH11359 with the US Department of Energy, Office of
Science, Office of High Energy Physics. The United States Government retains
and the publisher, by accepting the article for publication, acknowledges that
the United States Government retains a non-exclusive, paid-up, irrevocable,
worldwide license to publish or reproduce the published form of this
manuscript, or allow others to do so, for United States Government purposes.

##  Appendix A DECam Data

DELVE DR1 combines DECam observations taken by DELVE with archival DECam data
from 157 other programs. These programs and their contributions to the DELVE
DR1 data set are listed in Table 4.

Table 4:  DECam community data included in DELVE DR1

Prop.ID | PI | $N_{\rm exp}$ | Prop.ID | PI | $N_{\rm exp}$ | Prop.ID | PI | $N_{\rm exp}$
---|---|---|---|---|---|---|---|---
2019A-0305 | Alex Drlica-Wagner | 3087 | 2015A-0631 | Alfredo Zenteno | 108 | 2018A-0137 | Jeffrey Cooke | 20
2014B-0404 | David Schlegel | 2882 | 2017B-0312 | Bryan Miller | 108 | 2015B-0307 | Armin Rest | 20
2018A-0386 | Alfredo Zenteno | 1798 | 2020A-0402 | … | 107 | 2013B-0612 | Julio Chaname | 20
2019A-0272 | Alfredo Zenteno | 1513 | 2015A-0107 | Claudia Belardi | 106 | 2014A-0348 | Haojing Yan | 20
2017A-0260 | Marcelle Soares-Santos | 1257 | 2013B-0614 | Ricardo Munoz | 106 | 2018B-0122 | Armin Rest | 19
2017A-0388 | Alfredo Zenteno | 1187 | 2017A-0918 | Alexandra Yip | 101 | 2018A-0059 | Herve Bouy | 18
2018A-0913 | Brad Tucker | 1039 | 2014A-0327 | Armin Rest | 99 | 2014A-0339 | Jonathan Hargis | 18
2015A-0608 | Francisco Forster | 760 | 2016A-0397 | Anja von der Linden | 98 | 2018A-0907 | Ricardo Munoz | 18
2013A-0214 | Maureen Van Den Berg | 716 | 2018A-0206 | Abhijit Saha | 94 | 2019A-0337 | David E Trilling | 18
2013B-0440 | David Nidever | 715 | 2013B-0421 | Armin Rest | 92 | 2013B-0627 | Gastao B Lima Neto | 18
2013A-0724 | Lori Allen | 615 | 2019A-0265 | Douglas P Finkbeiner | 90 | 2018A-0371 | Sangeeta Malhotra | 16
2018B-0271 | Douglas P Finkbeiner | 527 | 2017A-0909 | Jeffrey Cooke | 90 | 2013A-0737 | Scott Sheppard | 16
2013A-0614 | Sarah Sweet | 510 | 2014B-0146 | Mark Sullivan | 86 | 2014A-0496 | Aren Heinze | 16
2013A-0327 | Armin Rest | 464 | 2014A-0623 | Ken Freeman | 83 | 2013A-0386 | Paul Thorman | 16
2016B-0909 | Camila Navarrete | 463 | 2014A-0321 | Marla Geha | 80 | 2014A-0386 | Ian dell’Antonio | 15
2020A-0399 | Alfredo Zenteno | 452 | 2017A-0913 | Luidhy Santana da Silva | 77 | 2013A-2101 | … | 15
2014A-0624 | Helmut Jerjen | 428 | 2015A-0306 | Eduardo Balbinot | 71 | 2014B-0610 | Julio Chaname | 14
2013A-0360 | Anja von der Linden | 425 | 2013A-0529 | R Michael Rich | 69 | 2014A-0634 | David James | 13
2015A-0616 | Helmut Jerjen | 425 | 2013A-0719 | Abhijit Saha | 68 | 2015A-0610 | Cesar Fuentes | 12
2014A-0270 | Carl J Grillmair | 368 | 2019A-0308 | Ian Dell’Antonio | 65 | 2015A-0617 | David M Nataf | 11
2013A-0741 | David Schlegel | 323 | 2014A-0622 | Iraklis Konstantopoulos | 65 | 2014A-0480 | R Michael Rich | 10
2015A-0630 | Thomas H. Puzia | 309 | 2017B-0285 | Armin Rest | 65 | 2014A-0073 | Mukremin Kilic | 10
2014A-0035 | Herve Bouy | 306 | 2019A-0101 | Patrick M Hartigan | 64 | 2018A-0380 | Armin Rest | 10
2014A-0608 | Francisco Forster | 286 | 2015A-0205 | Eric Mamajek | 64 | 2018B-0904 | Lee Splitter | 10
2014A-0415 | Anja von der Linden | 282 | 2013A-0615 | Joss Bland-Hawthorn | 63 | 2014B-0064 | Mukremin Kilic | 10
2015A-0620 | Ana Bonaca | 268 | 2020A-0238 | Clara Martinez-Vazquez | 62 | 2015A-0609 | Julio Carballo-Bello | 9
2014A-0412 | Armin Rest | 266 | 2017A-0911 | Ana Chies Santos | 62 | 2012B-0001 | Josh Frieman | 9
2015A-0110 | Thomas De Boer | 264 | 2016A-0384 | Jacqueline McCleary | 61 | 2014A-0621 | Dougal Mackey | 7
2019B-0323 | Alfredo Zenteno | 254 | 2014A-0613 | David Rodriguez | 59 | 2013A-0455 | Scott Sheppard | 7
2017A-0914 | Grant Tremblay | 250 | 2014A-0313 | Kathy Vivas | 55 | 2013A-0621 | Matias Gomez | 6
2017A-0916 | Julio Carballo-Bello | 240 | 2017B-0163 | Prashin Jethwa | 55 | 2015A-0151 | Annalisa Calamida | 6
2019A-0205 | Daniel Goldstein | 225 | 2013A-0612 | Yun-Kyeong Sheen | 52 | 2015B-0607 | Jeffrey Cooke | 5
2016B-0279 | Douglas P Finkbeiner | 208 | 2014A-0610 | Matthew Taylor | 51 | 2017B-0078 | Herve Bouy | 5
2018A-0273 | William Dawson | 205 | 2015A-0615 | Brendan McMonigal | 50 | 2015B-0314 | Brad Tucker | 5
2015A-0163 | Carl J Grillmair | 186 | 2018A-0912 | Attila Popping | 49 | 2012B-0624 | Aaron Robotham | 4
2016A-0189 | Armin Rest | 184 | 2012B-0003 | … | 48 | 2013B-0531 | Eric Mamajek | 4
2016A-0327 | Douglas P Finkbeiner | 183 | 2016A-0614 | Thomas H. Puzia | 48 | 2017A-0366 | Sangeeta Malhotra | 3
2017A-0060 | Denija Crnojevic | 172 | 2014A-0239 | Mark Sullivan | 48 | 2013A-0613 | Ricardo Munoz | 3
2015A-0130 | Denija Crnojevic | 172 | 2015A-0371 | Armin Rest | 47 | 2012B-0448 | Paul Thorman | 3
2015A-0121 | Anja von der Linden | 160 | 2016A-0190 | Arjun Dey | 44 | 2013A-0608 | Ricardo Demarco | 2
2014A-0306 | Xinyu Dai | 159 | 2012B-0569 | Lori Allen | 39 | 2014A-0255 | Anton Koekemoer | 2
2015A-0619 | Thiago Goncalves | 156 | 2017B-0103 | Wayne Barkhouse | 38 | 2016A-0610 | Leopoldo Infante | 2
2016B-0910 | Thomas H. Puzia | 153 | 2012B-0363 | Josh Bloom | 37 | 2014B-0375 | Armin Rest | 2
2018A-0911 | Francisco Forster | 152 | 2018A-0369 | Armin Rest | 34 | 2013A-0609 | Douglas P Geisler | 1
2013A-0411 | David Nidever | 140 | 2012B-0506 | Daniel D Kelson | 32 | 2013A-0610 | Mario Hamuy | 1
2018A-0276 | Ian Dell’Antonio | 139 | 2018A-0159 | Kathy Vivas | 32 | 2016A-0386 | Sangeeta Malhotra | 1
2017B-0279 | Armin Rest | 138 | 2017A-0210 | Alistair Walker | 31 | 2013A-0351 | Arjun Dey | 1
2018A-0215 | Jeffrey Carlin | 135 | 2013A-0611 | Dougal Mackey | 30 | 2017A-0308 | Annalisa Calamida | 1
2016B-0301 | Armin Rest | 134 | 2013A-0723 | Eric Mamajek | 25 | 2017B-0307 | Scott Sheppard | 1
2018A-0251 | Douglas P Finkbeiner | 131 | 2017B-0110 | Edo Berger | 24 | 2017B-0951 | Kathy Vivas | 1
2014A-0620 | Andrew Casey | 121 | 2014A-0429 | Douglas P Finkbeiner | 23 | 2019A-0315 | Matthew Penny | 1
2015B-0606 | Katharine Lutz | 113 | 2016A-0622 | Paulo Lopes | 22 | 2016A-0191 | Armin Rest | 1
2018A-0909 | Thomas H. Puzia | 110 | 2015A-0397 | Armin Rest | 21 | TOTAL | DELVE DR1 | 29929

Note. — Programs are ordered by the number of exposures contributed. The
largest single contributor to the DELVE DR1 data set is the DELVE program
itself. Programs with no principal investigator (PI) listed are generally
Target-of-Opportunity (ToO) programs.

##  Appendix B Transformation Equations

Initial transformation equations were derived between ATLAS Refcat2 (Tonry et
al., 2018) and the DES Standard Bandpasses (DES Collaboration, 2018a) using a
set of matched stars from ATLAS Refcat2 and DES DR1 with $16.0<i<20.0$ mag
located within a ${\sim}\,20\deg^{2}$ region centered at $({\rm RA},{\rm
Dec})=(17,-30)\deg$. We performed a straight-line fit to the difference in the
DES DR1 WAVG_PSF magnitudes and the ATLAS Refcat2 magnitudes vs. color over a
color range typical of the main-sequence stellar locus. We further refined the
zeropoints of these initial transformation equations using a set of exposures
processed by both DELVE and DES. We compare zeropoints derived by the DES
global calibration (Burke et al., 2018) to zeropoints derived from the DELVE
processing by matching to ATLAS Refcat2 using the initial transformation
equations. We adjusted the zeropoints of the ATLAS Refcat2 transformation
equations so that there was no mean offset between the DES zeropoints and the
DELVE zeropoints for these exposures (these zeropoint adjustments were
$\lesssim 0.01$ mag).

The resulting transformation equations in $g,r,i,z$ between ATLAS Refcat2 (in
the PS1 system) and DECam are

$\displaystyle g_{\rm DECam}$ $\displaystyle=g_{\rm PS1}+0.0994(g_{\rm
PS1}-r_{\rm PS1})-0.0319$ $\displaystyle r_{\rm DECam}$ $\displaystyle=r_{\rm
PS1}-0.1335(g_{\rm PS1}-r_{\rm PS1})+0.0215$ $\displaystyle i_{\rm DECam}$
$\displaystyle=i_{\rm PS1}-0.3407(i_{\rm PS1}-z_{\rm PS1})-0.0013$
$\displaystyle z_{\rm DECam}$ $\displaystyle=r_{\rm PS1}-0.2575(r_{\rm
PS1}-z_{\rm PS1})-0.0201.$

We used these transformation equations to derive DELVE zeropoints for a larger
set of DES exposures and to compare with the DES zeropoints in Figure 13. The
mean offsets between the DELVE and DES zeropoints are $<1\mathrm{\,mmag}$ in
all bands. The DELVE zeropoints have an rms scatter of ${\sim}\,0.01$ mag per
CCD when compared to the DES zeropoints for these exposures. The
transformations in $g,r$ are valid for stars with $-0.2<(g_{\rm PS1}-r_{\rm
PS1})<1.2\mathrm{\,mag}$, and the transformations in $i,z$ are valid for stars
with $-0.2<(i_{\rm PS1}-z_{\rm PS1})<0.3\mathrm{\,mag}$. Transformations
between the DES/DECam system and other systems can be found in Appendix A of
DES Collaboration (2021).

Figure 13: Comparison of CCD image zeropoints for DES exposures processed by
DES and DELVE. The DELVE zeropoints are derived from ATLAS Refcat2, while the
DES zeropoints derived from the DES global calibration (Burke et al., 2018).
The spread between these two techniques has a standard deviation of
$<0.01\mathrm{\,mag}$ (black curve), with a small fraction of outliers with
larger deviations.

##  Appendix C DELVE DR1 Tables

The DELVE DR1 catalog data are accessible through the DELVE_DR1.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 5. A cross-match
between DELVE DR1 and Gaia EDR3 is also provided in
DELVE_DR1.X1P5__OBJECTS__GAIA_EDR3__GAIA_SOURCE. The columns of this table are
described in Table 6.

Table 5: DELVE.DR1_MAIN table description: 519,737,142 rows; 215 columns  Column Name | Description | Columns
---|---|---
QUICK_OBJECT_ID | Unique identifier for each object | 1
RA | Right ascension derived from the median position of each detection (deg) | 1
DEC | Declination derived from the median position of each detection (deg) | 1
A_IMAGE_G,R,I,Z | Semi-major axis of adaptive aperture in image coordinates (pix) | 4
B_IMAGE_G,R,I,Z | Semi-minor axis of adaptive aperture in image coordinates (pix) | 4
CCDNUM_G,R,I,Z | CCD number for best exposure in each band | 4
CLASS_STAR_G,R,I,Z | Neural-network-based star–galaxy classifier (see SourceExtractor manual for details) | 4
EBV |  $E(B-V)$ value at the object location interpolated from the map of Schlegel et al. (1998) | 1
EXPNUM_G,R,I,Z | Exposure number for best exposure in each band | 4
EXPTIME_G,R,I,Z | Shutter-open exposure time for best exposure in each band | 4
EXTENDED_CLASS_G,R,I,Z | Spread-model-based morphology class (see Section 4.8) | 4
|  $-9$ unknown, 0 high-confidence star, 1 likely star, 2 likely galaxy, 3 high-confidence galaxy |
EXTINCTION_G,R,I,Z | Interstellar extinction from Schlegel et al. (1998) $E(B-V)$ and DES $A_{b}$ (see Section 4.2) | 4
FLAGS_G,R,I,Z |  SourceExtractor flags for the best detection in each band | 4
HPX2048 | HEALPix index for each object in RING format at resolution $\texttt{nside}=2048$ | 1
HTM9 | HTM Level-9 index | 1
MAG_AUTO_G,R,I,Z | Automatic aperture magnitude in each band (see SourceExtractor manual) | 4
MAGERR_AUTO_G,R,I,Z | Automatic aperture magnitude uncertainty in each band (see SourceExtractor manual) | 4
MAG_PSF_G,R,I,Z | PSF magnitude in each band (see SourceExtractor manual) | 4
MAGERR_PSF_G,R,I,Z | PSF magnitude uncertainty in each band (see SourceExtractor manual) | 4
NEPOCHS_G,R,I,Z | Number of single-epoch detections for this object | 4
NEST4096 |  HEALPix index for each object in NEST format at resolution $\texttt{nside}=4096$ | 1
RANDOM_ID | Random ID in the range 0.0 to 100.0 for subsampling | 1
RING256 |  HEALPix index for each object in RING format at resolution $\texttt{nside}=256$ | 1
RING32 |  HEALPix index for each object in RING format at resolution $\texttt{nside}=32$ | 1
SPREAD_MODEL_G,R,I,Z | Likelihood-based star–galaxy classifier (see Desai et al., 2012) | 4
SPREADERR_MODEL_G,R,I,Z | Likelihood-based star–galaxy classifier uncertainty (see Desai et al., 2012) | 4
T_EFF_G,R,I,Z | Effective exposure time scale factor for best exposure in each band (see Neilsen et al., 2015) | 4
THETA_IMAGE_G,R,I,Z | Position angle of automatic aperture in image coordinates (deg) | 4
WAVG_FLAGS_G,R,I,Z | OR of SourceExtractor flags from all detections in each band | 4
WAVG_MAG_AUTO_G,R,I,Z | Weighted average of automatic aperture magnitude measurements in each band | 4
WAVG_MAGERR_AUTO_G,R,I,Z | Sum in quadrature of the automatic aperture magnitude uncertainties in each band | 4
WAVG_MAGRMS_AUTO_G,R,I,Z | Unbiased weighted standard deviation of the automatic aperture magnitude in each band | 4
WAVG_MAG_PSF_G,R,I,Z | Weighted average of PSF magnitude measurements in each band | 4
WAVG_MAGERR_PSF_G,R,I,Z | Sum in quadrature of the PSF magnitude uncertainties in each band | 4
WAVG_MAGRMS_PSF_G,R,I,Z | Unbiased weighted standard deviation of the PSF magnitude in each band | 4
WAVG_SPREAD_MODEL_G,R,I,Z | Weighted average spread model in each band | 4
WAVG_SPREADERR_MODEL_G,R,I,Z | Sum in quadrature of the spread model uncertainties in each band | 4
WAVG_SPREADRMS_MODEL_G,R,I,Z | Unbiased weighted standard deviation of SPREAD_MODEL in each band | 4
Table 6: DELVE_DR1.X1P5__OBJECTS__GAIA_EDR3__GAIA_SOURCE table description: 143,701,359 rows; 7 columns  Column Name | Description | Columns
---|---|---
DEC1 | Declination in DELVE DR1 (deg) | 1
DEC2 | Declination in Gaia EDR3 (deg) | 1
DISTANCE | Distance between RA1,DEC1 and RA2,DEC2 (arcsec) | 1
ID1 | ID in DELVE DR1 (QUICK_OBJECT_ID) | 1
ID2 | ID in Gaia EDR3 (SOURCE_ID) | 1
RA1 | Right ascension from DELVE DR1 (deg) | 1
RA2 | Right ascension from Gaia EDR3 (deg) | 1

##  Appendix D Depth

This appendix includes sky maps showing variations in the S/N=5 depth of DELVE
DR1 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
4.7). These values were derived in ${\sim}\,12\mathrm{\,arcmin}^{2}$ HEALPix
pixels ($\texttt{nside}=1024$) and are shown in Figure 14. A region of deeper
$r$-band imaging can be seen around (${\rm RA},{\rm Dec})\sim(170,-40)\deg$.
This corresponds to archival data from the SLAMS survey (Jethwa et al., 2018b)
that were taken in excellent conditions.

Figure 14: Sky maps and histograms of the S/N=5 magnitude limit computed from
the statistical uncertainty in MAG_PSF. Sky maps are plotted using an equal-
area McBryde–Thomas flat polar quartic projection in celestial equatorial
coordinates.

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