# The JWST Resolved Stellar Populations Early Release Science Program II.
Survey Overview

Daniel R. Weisz Department of Astronomy, University of California, Berkeley,
CA 94720, USA  Kristen B. W. McQuinn Department of Physics and Astronomy,
Rutgers, the State University of New Jersey, 136 Frelinghuysen Road,
Piscataway, NJ 08854, USA  Alessandro Savino Department of Astronomy,
University of California, Berkeley, CA 94720, USA  Nitya Kallivayalil
Department of Astronomy, University of Virginia, 530 McCormick Road,
Charlottesville, VA 22904, USA  Jay Anderson Space Telescope Science
Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Martha L. Boyer
Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218,
USA  Matteo Correnti INAF Osservatorio Astronomico di Roma, Via Frascati 33,
00078, Monteporzio Catone, Rome, Italy  ASI-Space Science Data Center, Via del
Politecnico, I-00133, Rome, Italy  Marla C. Geha Department of Astronomy, Yale
University, New Haven, CT 06520, USA  Andrew E. Dolphin Raytheon Technologies,
1151 E. Hermans Road, Tucson, AZ 85756, USA  Steward Observatory, University
of Arizona, 933 N. Cherry Avenue, Tucson, AZ 85719, USA  Karin M. Sandstrom
Center for Astrophysics and Space Sciences, Department of Physics, University
of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA  Andrew A.
Cole School of Natural Sciences, University of Tasmania, Private Bag 37,
Hobart, Tasmania 7001, Australia  Benjamin F. Williams Department of
Astronomy, University of Washington, Box 351580, U.W., Seattle, WA 98195-1580,
USA  Evan D. Skillman University of Minnesota, Minnesota Institute for
Astrophysics, School of Physics and Astronomy, 116 Church Street, S.E.,
Minneapolis, MN 55455, USA  Roger E. Cohen Department of Physics and
Astronomy, Rutgers, the State University of New Jersey, 136 Frelinghuysen
Road, Piscataway, NJ 08854, USA  Max J. B. Newman Department of Physics and
Astronomy, Rutgers, the State University of New Jersey, 136 Frelinghuysen
Road, Piscataway, NJ 08854, USA  Rachael Beaton Space Telescope Science
Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Department of
Astrophysical Sciences, Princeton University, 4 Ivy Lane, Princeton, NJ 08544,
USA  The Observatories of the Carnegie Institution for Science, 813 Santa
Barbara St., Pasadena, CA 91101, USA  Alessandro Bressan SISSA, Via Bonomea
265, 34136 Trieste, Italy  Alberto Bolatto Department of Astronomy, University
of Maryland, College Park, MD 20742, USA  Joint Space-Science Institute,
University of Maryland, College Park, MD 20742, USA  Michael Boylan-Kolchin
Department of Astronomy, The University of Texas at Austin, 2515 Speedway,
Stop C1400, Austin, TX 78712-1205, USA  Alyson M. Brooks Department of Physics
and Astronomy, Rutgers, the State University of New Jersey, 136 Frelinghuysen
Road, Piscataway, NJ 08854, USA  Center for Computational Astrophysics,
Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA  James S.
Bullock Department of Physics and Astronomy, University of California, Irvine,
CA 92697 USA  Charlie Conroy Center for Astrophysics — Harvard & Smithsonian,
Cambridge, MA, 02138, USA  M. C. Cooper Department of Physics and Astronomy,
University of California, Irvine, CA 92697 USA  Julianne J. Dalcanton
Department of Astronomy, University of Washington, Box 351580, U.W., Seattle,
WA 98195-1580, USA  Center for Computational Astrophysics, Flatiron Institute,
162 Fifth Avenue, New York, NY 10010, USA  Aaron L. Dotter Department of
Physics and Astronomy, Dartmouth College, 6127 Wilder Laboratory, Hanover, NH
03755, USA  Tobias K. Fritz Department of Astronomy, University of Virginia,
530 McCormick Road, Charlottesville, VA 22904, USA  Christopher T. Garling
Department of Astronomy, University of Virginia, 530 McCormick Road,
Charlottesville, VA 22904, USA  Mario Gennaro Space Telescope Science
Institute, 3700 San Martin Dr., Baltimore, MD 21218, USA  The William H.
Miller III Department of Physics & Astronomy, Bloomberg Center for Physics and
Astronomy, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD
21218, USA  Karoline M. Gilbert Space Telescope Science Institute, 3700 San
Martin Dr., Baltimore, MD 21218, USA  The William H. Miller III Department of
Physics & Astronomy, Bloomberg Center for Physics and Astronomy, Johns Hopkins
University, 3400 N. Charles Street, Baltimore, MD 21218, USA  Léo Girardi
Padova Astronomical Observatory, Vicolo dell’Osservatorio 5, Padova, Italy
Benjamin D. Johnson Center for Astrophysics — Harvard & Smithsonian,
Cambridge, MA, 02138, USA  L. Clifton Johnson Center for Interdisciplinary
Exploration and Research in Astrophysics (CIERA) and Department of Physics and
Astronomy, Northwestern University, 1800 Sherman Avenue, Evanston, IL 60201,
USA  Jason S. Kalirai John Hopkins Applied Physics Laboratory, 11100 Johns
Hopkins Road, Laurel, MD 20723, USA  Evan N. Kirby Department of Physics,
University of Notre Dame, Notre Dame, IN 46556, USA  Dustin Lang Perimeter
Institute for Theoretical Physics, Waterloo, ON N2L 2Y5, Canada  Paola Marigo
Department of Physics and Astronomy G. Galilei, University of Padova, Vicolo
dell’Osservatorio 3, I-35122, Padova, Italy  Hannah Richstein Department of
Astronomy, University of Virginia, 530 McCormick Road, Charlottesville, VA
22904, USA  Edward F. Schlafly Space Telescope Science Institute, 3700 San
Martin Dr., Baltimore, MD 21218, USA  Judy Schmidt 2817 Rudge Pl, Modesto, CA
95355, USA  Erik J. Tollerud Space Telescope Science Institute, 3700 San
Martin Drive, Baltimore, MD 21218, USA  Jack T. Warfield Department of
Astronomy, University of Virginia, 530 McCormick Road, Charlottesville, VA
22904, USA  Andrew Wetzel Department of Physics and Astronomy, University of
California, Davis, CA 95616, USA

###### Abstract

We present the JWST Resolved Stellar Populations Early Release Science (ERS)
science program. We obtained 27.5 hours of NIRCam and NIRISS imaging of three
targets in the Local Group (Milky Way globular cluster M92, ultra-faint dwarf
galaxy Draco II, star-forming dwarf galaxy WLM), which span factors of $\sim
10^{5}$ in luminosity, $\sim 10^{4}$ in distance, and $\sim 10^{5}$ in surface
brightness. We describe the survey strategy, scientific and technical goals,
implementation details, present select NIRCam color-magnitude diagrams (CMDs),
and validate the NIRCam exposure time calculator (ETC). Our CMDs are among the
deepest in existence for each class of target. They touch the theoretical
hydrogen burning limit in M92 ($<0.08$ $M_{\odot}$; SNR $\sim 5$ at
$m_{F090W}\sim 28.2$; $M_{F090W}\sim+13.6$), include the lowest-mass stars
observed outside the Milky Way in Draco II (0.09 $M_{\odot}$; SNR $=10$ at
$m_{F090W}\sim 29$; $M_{F090W}\sim+12.1$), and reach $\sim 1.5$ magnitudes
below the oldest main sequence turnoff in WLM (SNR $=10$ at $m_{F090W}\sim
29.5$; $M_{F090W}\sim+4.6$). The PARSEC stellar models provide a good
qualitative match to the NIRCam CMDs, though are $\sim 0.05$ mag too blue
compared to M92 F090W$-$F150W data. The NIRCam ETC (v2.0) matches the SNRs
based on photon noise from DOLPHOT stellar photometry in uncrowded fields, but
the ETC may not be accurate in more crowded fields, similar to what is known
for HST. We release beta versions of DOLPHOT NIRCam and NIRISS modules to the
community. Results from this ERS program will establish JWST as the premier
instrument for resolved stellar populations studies for decades to come.

Stellar photometry (1620), Local Group (929), Stellar populations (1622),
Hertzsprung Russell diagram (725), JWST (2291)

††facilities: JWST(NIRCAM), JWST(NIRISS)

##  1 Introduction

The resolved stellar populations of nearby galaxies are central to a wide
range of astrophysics. The observed colors, luminosities, and spectral
features of resolved stars in galaxies within the Local Volume (LV) anchor our
knowledge of star formation (e.g., star cluster formation, the initial mass
function, the importance of binarity; Massey, 2003; McKee & Ostriker, 2007;
Sarajedini et al., 2007; Bastian et al., 2010; Sana et al., 2012; Kroupa et
al., 2013; Krumholz, 2014; Piotto et al., 2015; Krumholz et al., 2019),
stellar feedback (e.g., the interplay between stars and their immediate
surroundings; e.g., Oey, 1996; Dohm-Palmer et al., 1998; Stinson et al., 2006,
2007; Governato et al., 2010; Ostriker et al., 2010; Lopez et al., 2011;
Pellegrini et al., 2011; Lopez et al., 2014; El-Badry et al., 2016; McQuinn et
al., 2018, 2019b), dust production and characteristics (e.g., Gordon et al.,
2003; Boyer et al., 2006; Boyer, 2010; Meixner et al., 2010; Dalcanton et al.,
2015; Schlafly et al., 2016; Green et al., 2019; Gordon et al., 2021;
Yanchulova Merica-Jones et al., 2021), and stellar chemistry and kinematics
across a wide range of environments (e.g., Venn et al., 2004; Simon & Geha,
2007; Geha et al., 2009; Kirby et al., 2011; Collins et al., 2013; Vargas et
al., 2013; Gilbert et al., 2014; Vargas et al., 2014; Ji et al., 2016; Gilbert
et al., 2019b; Escala et al., 2020; Kirby et al., 2020; Gilbert et al., 2022).
Resolved stars are the basis for the local distance ladder (e.g., Freedman et
al., 2001; Riess et al., 2011; Beaton et al., 2016; Riess et al., 2016;
McQuinn et al., 2017a, 2019a; Freedman et al., 2020; Riess et al., 2021),
which provides constraints on the expansion of the Universe and the nature of
dark energy (e.g., Di Valentino et al., 2021). They anchor our knowledge of
the stellar evolution models that are used to interpret the light of distant
galaxies (e.g., Dotter et al., 2008; Ekström et al., 2012; Girardi et al.,
2010; Bressan et al., 2012; VandenBerg et al., 2012; Choi et al., 2016;
Eldridge et al., 2017; Conroy et al., 2018; Hidalgo et al., 2018; Eldridge &
Stanway, 2022) and provide detailed insight into the dynamic assembly of our
Galactic neighborhood, cosmic reionization, the first stars, near-field
cosmology, and the nature of dark matter on the smallest scales (e.g., Mateo,
1998; Tolstoy et al., 2009; Brown et al., 2014; Weisz et al., 2014a; Boylan-
Kolchin et al., 2015; Gallart et al., 2015; McQuinn et al., 2015; Frebel &
Norris, 2015; Wetzel et al., 2016; Bullock & Boylan-Kolchin, 2017; Starkenburg
et al., 2017b; McConnachie et al., 2018; Kallivayalil et al., 2018; Conroy et
al., 2019; Simon, 2019; Patel et al., 2020; Boylan-Kolchin & Weisz, 2021;
Sacchi et al., 2021; Pearson et al., 2022).

Over the past $\sim 30$ years, much of this science has been enabled by the
Hubble Space Telescope (HST). Since its first images of resolved stars in the
local Universe (e.g., Paresce et al., 1991; Campbell et al., 1992;
Guhathakurta et al., 1992; Freedman et al., 1994; Hunter et al., 1995), HST’s
exquisite sensitivity, angular resolution, and broad wavelength coverage have
transformed our knowledge of the Universe by observing hundreds of nearby
galaxies for thousands of hours (e.g., Freedman et al., 2001; Brown et al.,
2006; Holtzman et al., 2006; Dalcanton et al., 2009; Brown et al., 2012;
Dalcanton et al., 2012a; Gallart et al., 2015; Riess et al., 2016; Skillman et
al., 2017; Tully et al., 2019; Williams et al., 2021), including the
Panchromatic Hubble Andromeda Treasury (PHAT) program, which resolved $100$
million stars across the disk of M31 (Dalcanton et al., 2012b; Williams et
al., 2014).

However, while HST continues to catalyze new astrophysical insights in nearby
galaxies, it has only scratched the surface of science enabled by infrared
(IR) observations. Compared to the UV and optical, HST’s IR camera has coarser
angular resolution, which limits it to brighter stars due to stellar crowding,
and it can only observe a small portion of the IR spectrum, which limits the
types of stellar populations it can study.

JWST will be transformative for resolved stellar populations in the IR.
Compared to any other facility, JWST will resolve individual stars at larger
distances, to fainter luminosities, over wider color baselines, in more
crowded areas, and in regions of higher extinction. JWST can provide the first
main-sequence turnoff-based (MSTO) star formation histories (SFHs) of galaxies
beyond the Local Group (LG; e.g., Weisz & Boylan-Kolchin, 2019),
systematically measure the sub-Solar mass stellar IMF directly from star
counts as a function of environment (e.g., Geha et al., 2013; Kalirai et al.,
2013; El-Badry et al., 2017; Gennaro et al., 2018a; Filion et al., 2020;
Gennaro & Robberto, 2020), determine proper motions and orbital histories for
dozens of galaxies outside our immediate Galactic neighborhood (e.g., van der
Marel et al., 2012; Sohn et al., 2013; Kallivayalil et al., 2013; Zivick et
al., 2018; Sohn et al., 2020; Warfield et al., 2023), construct parsec-scale
maps of the interstellar medium (ISM) in galaxies out to several Mpc (e.g.,
Dalcanton et al., 2015; Gordon et al., 2016; Yanchulova Merica-Jones et al.,
2017, 2021), establish a new anchor to the physics of the evolved stars that
dominate the rest-frame near-IR light of distant galaxies (e.g., Maraston,
2006; Melbourne et al., 2012; Boyer et al., 2015, 2019), provide high fidelity
distances to galaxies throughout the Local Volume (e.g., Beaton et al., 2016;
McQuinn et al., 2019a; Tully et al., 2019; Freedman et al., 2020; Riess et
al., 2021), and much more.

With these remarkable capabilities in mind, we have undertaken the JWST
Resolved Stellar Populations Early Release Science (ERS) Program (DD-1334; PI
D. Weisz) to establish JWST as the premier facility for the study of resolved
stellar populations in the IR such that it can match and exceed HST’s
successes in the local Universe. To realize this goal, our ERS program has
acquired deep multi-band NIRCam and NIRISS imaging of three targets in the
Local Group (LG): one Milky Way globular cluster (GC; M92), one ultra-faint
dwarf galaxy (UFD; Draco II), and one distant star-forming dwarf galaxy (WLM).
These diverse targets showcase a broad range of the science described above
and enable the development and testing of JWST-specific modules for the widely
used crowded field stellar photometry package DOLPHOT (Dolphin, 2000, 2016).

In this paper, we summarize the design of our ERS program, illustrate the new
JWST-specific capabilities of DOLPHOT, outline the photometric reduction
process, present a first look at JWST observations of our targets, and
undertake select comparisons with stellar models and the current JWST exposure
time calculator (ETC). Papers in preparation by our team will provide a
detailed overview of the new NIRCam and NIRISS modules for DOLPHOT and will
focus on a wide variety of science results enabled by the ERS data beyond what
is described here.

This paper is organized as follows. We summarize the program’s overarching
science and technical aims and target selection in §2. We then describe how we
translated these goals into an observational strategy in §3. We detail the
actual ERS observations in §4 and summarize the application of DOLPHOT in §5.
In §6 we present NIRCam color-magnitude diagrams (CMDs) and compare them to
select stellar models and evaluate the performance of NIRCam ETC.

##  2 Program Goals

Our team developed a set of main science and technical goals based on
anticipated common community use cases of JWST for resolved stellar
populations. For simplicity, we limited our considerations to science cases
based on imaging with NIRCam, which is considered the “workhorse” camera of
JWST, as well as NIRISS imaging, which we used in parallel. This setup is
analogous to the commonly used mode of HST in which ACS/WFC operates as the
primary instrument with WFC3/UVIS acquiring imaging in parallel (e.g.,
Dalcanton et al., 2012a; Gallart et al., 2015; Skillman et al., 2017; Albers
et al., 2019; Williams et al., 2021).

Science based on imaging of resolved stars often requires stellar photometry
in crowded fields. Because of that, resolved stellar population studies are
technically daunting, requiring highly optimized observations and
sophisticated analysis tools that have been developed and refined over the
past $\sim 40$ years (e.g., Buonanno et al., 1979; Tody, 1980; Stetson, 1987;
Schechter et al., 1993; Stetson, 1994; Anderson & King, 2000; Dalcanton et
al., 2012b; Williams et al., 2014). A main technical goal of our program is to
develop and release NIRCam and NIRISS modules for DOLPHOT, along with
practical recommendations and demonstrations for applying DOLPHOT to NIRCam
and NIRISS imaging. Here, we summarize our main science goals, technical
goals, and science “deliverables” which guide our ERS program.

###  2.1 Scientific Goals

Our team identified six main science themes that guided the construction of
our ERS program. They are:

  1. 1.

Star Formation Histories. A galaxy’s resolved stellar content encodes its star
formation history (SFH), which can be reconstructed by fitting CMDs with
stellar evolution models (e.g., Tosi et al., 1989; Tolstoy, 1996; Harris &
Zaritsky, 2001; Dolphin, 2002; Hidalgo et al., 2009). These SFHs are
particularly robust when CMDs extend below the oldest main sequence turnoff
(MSTO; e.g., Gallart et al., 2005). The faintness of this feature in the
optical ($M_{V}\sim+4$) has limited current ‘gold standard’ SFHs to galaxies
within the LG. However, the relatively low effective temperatures of these
stars, combined with the decreased sky background in the near-IR and JWST’s
excellent sensitivity and angular resolution, will enable it to measure the
first SFHs based on the oldest MSTOs for galaxies outside the LG (e.g., Weisz
& Boylan-Kolchin 2019; JWST-GO-1617 PI K. McQuinn) from which outstanding
questions (e.g., the effects of reionization and/or environment on galaxy
formation) can be uniquely addressed (e.g., Bullock & Boylan-Kolchin, 2017;
Simon, 2019). Our JWST program will showcase JWST’s ability to measure robust
SFHs.

  2. 2.

The Sub-Solar Mass IMF. Resolved star counts shows that lowest-mass galaxies
appear to have sub-Solar IMF slopes which deviate from the Galactic value.
(e.g., Geha et al., 2013; Kalirai et al., 2013; Gennaro et al., 2018b).
However, even with HST, it has proven challenging to acquire sufficiently deep
data (down to $\sim 0.2\,M_{\odot}$ El-Badry et al., 2017; Gennaro et al.,
2018b, a) to unambiguously confirm these putative IMF variations. Our ERS
program will illustrate JWST’s capabilities for definitively measuring the
sub-Solar IMF in a ultra-faint MW satellite, paving the way for a systematic
study of the low-mass IMF and star formation in extreme environments.

  3. 3.

Proper Motions. High-precision astrometry enables the measurement of proper
motions (PMs) throughout the LG. Gaia has been transformative for objects in
the MW halo, while HST has laid the foundation for fainter, more distant
systems. JWST is the future of precision astrometry for faint and/or more
distant objects. On its own, and in tandem with Gaia, HST, and Roman, JWST
imaging will provide measurements of total masses, dark matter profiles, and
orbital histories for $\sim 100$ galaxies in and around the LG (e.g., Bullock
& Boylan-Kolchin, 2017; Kallivayalil et al., 2015; Fritz et al., 2018; Gilbert
et al., 2019a; Battaglia et al., 2022; Warfield et al., 2023). Our ERS program
will showcase the PM measurements capabilities of JWST.

  4. 4.

Evolved Stars. Cool evolved stars such as red supergiants and asympotic giant
branch (AGB) stars are responsible for 20–70% of the rest-frame near-IR
luminosity of star-forming galaxies (e.g., Maraston, 2006; Melbourne et al.,
2012) and are sites of dust production (e.g., Ventura, 2001). However, the
rapid evolution of dusty AGB stars is challenging to model (e.g., Maraston,
2006; Girardi et al., 2010; Conroy, 2013; Marigo et al., 2017), which has only
begun to be alleviated by recent observations (e.g., Boyer et al., 2015,
2017). JWST’s expansive IR filter set will reveal elusive dust-enshrouded
populations of AGB stars (e.g., oxygen-rich M stars and carbon-rich C stars)
across a wide range of galactic environments (e.g., Hjort et al., 2016; Jones
et al., 2017; Marini et al., 2020). Our ERS program will demonstrate JWST’s
capacity to study IR-bright, evolved stars.

  5. 5.

Extinction Mapping. In the LG, Spitzer and Herschel have mapped dust emission
at $\sim 10-40$″ and $\sim 7-12$″ resolution, respectively (10 pc for the
Magellanic Clouds; 100 pc for M31 and M33; Draine, 2007; Gordon, 2014;
Chastenet et al., 2019; Utomo et al., 2019). JWST can map the cold ISM at
significantly higher spatial resolution by inferring dust content from its
impact on stellar spectral energy distributions (e.g., Dalcanton et al., 2015;
Gordon et al., 2016). Our ERS observations will demonstrate JWST’s ability to
map dust extinction and relate it to properties of the cold ISM.

  6. 6.

Ages of Globular Clusters. Accurate ages of the oldest GCs are particularly
important for connecting the stellar fossil record to events in the early
Universe including cosmic reionization and the age of the Universe itself
(e.g., Chaboyer et al., 1996; Grebel & Gallagher, 2004; Ricotti & Gnedin,
2005; Monelli et al., 2010; Brown et al., 2014; Weisz et al., 2014b; Boylan-
Kolchin et al., 2015). Current age estimates are typically limited to $\sim 1$
Gyr precision (i.e., twice as long as reionization lasted) due to the age-
metallicity degeneracies at the MSTO (see Boylan-Kolchin & Weisz 2021 and
references therein). JWST observations of the ‘kink’ on the lower main
sequence (MS) can yield more precise estimates of cluster ages (e.g.,
Sarajedini et al., 2009; Bono et al., 2010; Kalirai et al., 2012; Correnti et
al., 2018). Our ERS data will showcase the powerful capabilities of JWST for
precise GC age-dating.

Beyond enabling our main science goals, we sought to identify observations
that would make our ERS program rich for archival pursuits. Examples include
measuring extragalactic distances in JWST bands (e.g., TRGB, variable stars;
Beaton et al., 2016; Madore et al., 2018; McQuinn et al., 2019a), identifying
rare stars from low-mass metal-poor stars to luminous red supergiants (e.g.,
Schlaufman & Casey, 2014; Casey & Schlaufman, 2015; Levesque, 2018), searching
for dust production among red giant branch stars (RGB; e.g., Boyer et al.,
2006; Boyer, 2010), and examining the nature of dark matter using wide
binaries (e.g., Peñarrubia et al., 2016).

###  2.2 Technical Goals

The main technical goal of our ERS program is to enable resolved star science
by the broader community. At the heart of this goal is the addition of NIRCam
and NIRISS modules to DOLPHOT. This process includes the technical development
of NIRCam and NIRISS modules for DOLPHOT, testing their performance on real
data, releasing data products that immediately enable science (e.g., stellar
catalogs), and providing guidance to the community on best use practices of
DOLPHOT for future applications. Here, we broadly describe each of these
technical goals and how they influenced the observational strategy of our ERS
program.

As with previous updates to DOLPHOT (e.g., Dalcanton et al., 2012a; Dolphin,
2016, and many unpublished updates), the core functionality of the code
remains the same as described in Dolphin (2000), but certain aspects have been
updated for NIRCam and NIRISS.

The DOLPHOT modules for NIRCam and NIRISS each feature their own pre-
processing routines that apply masks (e.g., of reference, saturated, and other
unusable pixels) to the images based on the data quality flags provided by the
STScI pipeline. They also apply the pixel area maps appropriate to each
camera. Other updates include the use of photometric calibrations provided in
the image metadata, conversions to VEGAMAG, and camera-specific PSF models
with corresponding encircled energy corrections.

For testing DOLPHOT on real data, we identified several observational
scenarios that we anticipate to be common for NIRCam and NIRISS studies of
resolved stars. They are:

  1. 1.

Targets with various levels of crowding. This includes images that are
completely uncrowded (e.g., in which aperture vs PSF photometry can be
compared), images with highly variable amounts of crowding (e.g., due to
surface brightness variations), and highly crowded images (i.e., the
photometric depth is primarily limited by crowding).

  2. 2.

Targets that include stars spanning a large dynamic range in brightness in the
same image. An example would be a GC, in which there are very bright red
giants and extremely faint dwarfs. This enables a variety of tests, including
the ability to recover faint sources next to very bright objects.

  3. 3.

Targets with bright, saturated stars. JWST is extremely sensitive.
Understanding the degree to which saturated stars affect the photometry of
fainter objects will be important to a variety of science goals.

  4. 4.

Targets that demonstrate the ability of using the higher angular resolution
short-wavelength (SW) images to increase the accuracy of the long-wavelength
(LW) photometry. PHAT showed that joint reduction of HST optical and IR data
produced IR photometry that provides significantly sharper CMDs compared to
reducing IR data alone (e.g., Williams et al., 2014). Similar gains should be
possible with NIRCam.

  5. 5.

Targets that enable the simultaneous reduction of HST and JWST imaging. To
date, DOLPHOT has produced wonderful cross-camera results for HST (e.g.,
Dalcanton et al., 2012b; Williams et al., 2014, 2021), but it needs to be
vetted and optimized for cross-facility use.

Figure 1: The locations of our NIRcam and NIRISS observations (plotted in red)
for each ERS target, overplotted on a Pan-STARRS optical image. The orange
dotted lines indicate: (a) 2 and 5 half-light radii ($r_{\rm h}$), (b) 1 and 2
$r_{\rm h}$ and (c) 1 $r_{\rm h}$ of each target. We show additional pointings
for each system: (a) M92: select HST optical (green; HST-GO-10775; Sarajedini
et al. 2007) and IR (pink; HST-GO-11664; e.g., Brown et al. 2010). (b) Draco
II: HST/ACS optical data (HST-GO-14734; PI Kallivayalil) are shown in green.
(c) WLM: An exhaustive, though not complete set of HST observations including
HST/WFPC2 UV and optical imaging in blue (HST-GO-11079; Bianchi et al. 2012),
HST/WFC3 UVIS UV imaging in purple (HST-GO-15275 PI Gilbert), HST/ACS and
HST/UVIS optical imaging in green (HST-GO-13768; PI Weisz, Albers et al.
2019), and HST/WFC3 IR imaging in pink (HST-GO-16162, PI Boyer). We opted not
to undertake large dithers to fill the NIRCam chip and module gaps which would
have substantially increased the program time while only marginally enhancing
our science goals.

###  2.3 Deliverables

Our program is in the process of providing several “deliverables” to the
community that can be found on our team website111https://ers-stars.github.io.
A primary deliverable is the public release of DOLPHOT with NIRCam and NIRISS
specific modules for which “beta” versions can be found on the main DOLPHOT
website222http://americano.dolphinsim.com/dolphot/. This software enables
crowded field stellar photometry for a diverse range of science in the local
Universe. Along with the software release we will provide extensive
documentation of how to use DOLPHOT and examples of it applied to our ERS
observations. Following careful calibration and testing, we will release high
level science products including the output of our team DOLPHOT runs on ERS
data (e.g., diagnostic plots and files), and NIRCam and NIRISS stellar
catalogs for each target along with artificial star tests. These data products
will be refined as our understanding of JWST improves (e.g., due to updated
PSF models) and will eventually include examples of how to use DOLPHOT for
simultaneous reduction of HST and JWST imaging.

Figure 2: NIRCam color composite images for our 3 ERS targets. In each RGB
image, F090W was used as the blue channel, F150W as the green and a
combination of the two LW filters as the red channel.

##  3  Strategy

###  3.1 Filters

The diversity of our science cases required careful consideration of filter
selection. Several of our science goals are centered around maximizing depth,
color baseline, and astrometric precision. Accordingly, we primarily focused
on SW filter selection, which has better sensitivity (for most stars) and
angular resolving power than the long-wavelength channel.

Using an ancient, metal-poor isochrone (12.5 Gyr, [Fe/H]=$-2.0$) from the MIST
stellar models (Choi et al., 2016), we examined the expected performance for
the SW wide filter (F070W, F090W, F115W, F150W, F200W) permutations at three
different CMD locations: the blue HB ($T_{\text{eff}}\sim 7000$ K), the MSTO
($T_{\text{eff}}\sim 6000$ K), and the lower MS ($\sim 0.2\,M_{\odot}$;
$T_{\text{eff}}\sim 4000$ K). At each point, we used the pre-commissioning
JWST ETC (v1.1.1) to compute the exposure time required to reach a SNR$=10$
for the “scene” in the ETC.

The best performing filters for our areas of consideration are F090W, F115W,
and F150W. They all exhibit comparable performance at the HB and MSTO.
However, F090W requires 2.5 times more exposure time to achieve the same SNR
for a $0.2\,M_{\odot}$ star as either F115W or F150W. Nevertheless, we opted
for F090W over F115W because compared to F115W$-$F150W, F090W$-$F150W provides
superior color information for most stars and F090W has the potential for
higher angular resolution (if dithered appropriately), which is critical for
astrometry. Finally, the similarity between F090W and HST/F814W (or Johnson
I-band) provides useful features such as matching catalogs between facilities
and TRGB distance determinations (e.g., McQuinn et al., 2019a). F070W and
F200W provide the largest color baseline, but each filter is less sensitive to
stars far from their effective wavelength. For example, F070W required 4 times
more integration time for a $0.2\,M_{\odot}$ star than the next bluest filter,
F090W. F200W requires twice as much exposure time for a HB star than F150W.

We opted to use the same F090W$-$F150W filter combination for all targets to
provide for an empirical comparison between the GC and UFD (e.g., Brown et
al., 2012) and for good sampling of the oldest MSTO in the distant dwarf. We
considered more than two SW filters, but the cost of acquiring extra data
outweighed the scientific utility. We selected simultaneously observed LW
filters on a per target basis, as they enable secondary science unique to each
object. Finally, we selected F090W and F150W for parallel NIRISS imaging for
consistency with NIRCam.

We emphasize that while our filter combinations represent a good compromise
across the CMD for our program goals, they may not be optimal for all science
cases. We encourage exploration tailored to a program’s particular science
aims.

###  3.2 Target Selection

We selected targets by first considering all known GCs in the MW (Harris,
2010) and galaxies within $\sim 1$ Mpc (McConnachie, 2012), including updates
to both catalogs and discoveries through 2017 (e.g., Laevens et al., 2014,
2015a; Bechtol et al., 2015; Drlica-Wagner et al., 2015; Koposov et al.,
2015). The limiting distance was selected to ensure we could reach the oldest
MSTO with SNR$=10$ in the most distant system in a reasonable amount of time
based on previous experience with HST (e.g., Cole et al., 2014; Albers et al.,
2019) and results from the JWST exposure time calculator (ETC).

We required that each target have extensive HST imaging (e.g., to enable
combined HST and JWST proper motion studies, create panchromatic stellar
catalogs) and have a good sampling of ground-based spectra (e.g., for full
phase space information, comparing stellar properties from spectra and
photometry, incorporating stellar abundance patterns into various analyses).

We then identified a minimum set of targets that could be used to achieve our
science and technical goals: one MW GC, one UFD, and one more distant star-
forming dwarf galaxy. We then sought to maximize observational efficiency by
focusing on some of the nearest examples of these classes. We eliminated
targets that were not visible during the nominal ERS window.

This selection process yielded three targets: MW GC M92, MW satellite UFD
Draco II, and star-forming dwarf galaxy WLM. Basic observational
characteristics of these targets are listed in Table 1. We detail the
observational strategy for each target in the following sections.

Table 1: Basic observational properties of the three ERS targets. Properties for M92 have been taken from the updated MW GC catalog of Harris (2010), while those of Draco II and WLM are from the updated LG galaxy catalog of McConnachie (2012). Note that $\mu_{0}$ is the effective surface brightness and $r_{h}$ is the half-light radius.  | M92 | Draco II | WLM
---|---|---|---
RA (J2000) | 17h17m07.27s | 15h52m47.60s | 00h01m58.16s
Dec (J2000) |  $+$43d08m11.5s |  $+$64d33m55.0s |  $-$15d27m39.34s
$M_{V}$ (mag) | $-8.2$ | $-0.8$ | $-14.2$
E(B-V) (mag) | $0.02$ | $0.01$ | $0.03$
$(m-M)_{0}$ (mag) | $14.6$ | $16.9$ | $24.9$
$\mu_{0}$ (mag arcsec-2) | $15.5$ | $28.1$ | $24.8$
$r_{h}$ (′) | $1.0$ | $2.7$ | $7.8$
|  |  |

###  3.3 M92

M92 (NGC 6341) is a well-studied, metal-poor GC in the MW that is often used
as a benchmark for extragalactic stellar population studies and for
photometric calibration (e.g., to verify zero points; e.g., Dalcanton et al.
2009; Brown et al. 2014; Gallart et al. 2015). Imaging this system satisfies
several science and technical goals including GC ages, individual star proper
motions, the present day mass function, testing DOLPHOT over a large dynamic
range of stellar brightness and spatially varying stellar density, and gauging
the effects of bright saturated stars on the photometric process.

As illustrated in Figure 1, we placed the NIRCam field near the center of M92,
with the aim of maximizing NIRCam spatial overlap with a wealth of multi-band
HST imaging of M92. The parallel NIRISS field is located at $\sim 5$ half-
light radii. We constrained the orientation such that the NIRISS field had a
modest probability of overlapping at least some HST data in the outer regions.
However, orientations allowed by the final ERS window did not result in
overlap between NIRISS and HST imaging.

We chose the F090W, F150W, F277W, and F444W for our NIRCam imaging of M92. We
selected F277W and F444W for their broad scientific utility including studying
the lower MS kink at long wavelengths (e.g., Sarajedini et al., 2009; Bono et
al., 2010), searching for dust production at low-metallicities (e.g., Boyer et
al., 2006; Boyer, 2010), and exploring multiple populations in the IR (e.g.,
Milone et al., 2012, 2014; Correnti et al., 2016; Milone et al., 2017).

We aimed to reach SNR$=10$ at $0.1\,M_{\odot}$ in F090W and F150W, which we
estimate to be $m_{F090W}\approx 26$ ($M_{F090W}\approx+11.4$) and
$m_{F150W}\approx 25.8$ ($M_{F150W}\approx+11.2$) based on the MIST isochrones
and the characteristics of M92 listed in Table 1. The 2017 versions of the ETC
(v1.1.1) and APT (v25.1.1) yielded exposure times of 1288s in each filter. The
anticipated SNRs in the LW filters at $m_{F090W}\approx 26$ were $21.1$ in
F277W and 6.4 in F444W. We estimated NIRISS imaging to be marginally shallower
than NIRCam with integration times of 1074s in each of F090W and F150W. The
difference in duration is set by facility overheads as calculated by APT.

###  3.4 Draco II

At $\sim$20 kpc, Draco II is one of the nearest examples of a MW satellite
(Laevens et al., 2015b; Longeard et al., 2018). At the time of program design,
Draco II was designated as a UFD (i.e., it has dark matter; Willman & Strader,
2012; Martin et al., 2016). In the interim, there has been some debate in the
literature over its status as a UFD vs GC (e.g., whether or not it has dark
matter, a metallicity spread, the slope of its sub-Solar stellar mass
function; e.g., Longeard et al., 2018; Baumgardt et al., 2022), an issue our
JWST data should help resolve.

Draco II’s close proximity provides for efficient JWST imaging that reaches
far down the lower main sequence ($\lesssim 0.2\,M_{\odot}$). Our deep Draco
II data satisfies several of our science goals including measuring the low-
mass stellar IMF (which can shed insight into its status as a GC or UFD),
determining the SFH of an ancient sparsely populated system, measuring proper
motions of individual faint stars using HST and JWST, and exploring our
ability to distinguish between faint stars and unresolved background galaxies.

We placed the NIRCam field on the center of Draco II. The field overlaps with
archival HST imaging obtained in March 2017 (GO-14734; PI N. Kallivayalil),
Keck spectroscopy, and is well-matched to the half-light radius. To maximize
scheduling opportunities, we did not constrain the orientation. The NIRISS
field is unlikely to contain many Draco II member stars, so its exact
placement was not crucial. The primary use of the NIRISS data will be to aid
with modeling contamination (e.g., foreground stars and background galaxies)
in the F090W$-$F150W CMD, e.g., as part of measuring the low-mass IMF.

We selected the F090W, F150W, F360M, and F480M for our NIRCam imaging of Draco
II. The rationale for the two SW filters is described in §3.3. The LW filters
are located near metallicity sensitive molecular features in the mid-IR, and
they may be suitable for measuring photometric metallicities of metal-poor
stars (e.g., Schlaufman & Casey, 2014; Casey & Schlaufman, 2015) similar to
what is possible in the optical using, for example, the Calcium H&K lines
(e.g., Starkenburg et al., 2017b; Fu et al., 2022). For NIRISS, we used the
F090W and F150W filters.

Our target depth is set by low-mass IMF science. Tightly constraining the low-
mass IMF in Draco II requires reaching stars $\lesssim 0.2\,M_{\odot}$ (e.g.,
El-Badry et al., 2017) with SNR$=10$ in F090W and F150W. Using the MIST
stellar models and the observational properites of Draco II listed in Table 1,
our target depths are SNR$\sim 10$ at $m_{F090W}=27$ ($M_{F090W}=+10.3$) and
$m_{F150W}=26.8$ ($M_{F150W}=+10.1$). Using the 2017 versions of the ETC
(v1.1.1) and APT (v25.1.1) we found exposure times of 12798s in F090W and
6399s in F150W would each these depths. We opted on SW/LW combinations of
F090W/F480M and F150W/F360M, which provided for SNRs of $\sim 7$ (F360M) and
$\sim 4$ (F480M) at F150W=25. We estimated the NIRISS imaging to have 12540s
in F090W and 6270s in F150W, which will result in marginally shallower CMDs
than NIRCam.

###  3.5 WLM

WLM (Wolf, 1909; Melotte, 1926) is a metal-poor ([Fe/H]$=-1.2$; Leaman et al.
2009) star-forming dwarf galaxy at $\sim 0.9$ Mpc (e.g., Albers et al., 2019).
Though slightly closer objects of this class exist (e.g., IC 1613), WLM is the
nearest example of a low-metallicity environment in which resolved CO clouds
have been detected (Rubio et al., 2015). It has a sufficiently high star
formation rate in the past several Gyr that it should host a sizable
population of AGB stars (e.g., Dolphin, 2000; Weisz et al., 2014a; McQuinn et
al., 2017b; Albers et al., 2019). In terms of science, our JWST imaging of WLM
will allow us to measure its SFH from the ancient MSTO and compare it to the
existing HST-based SFH, measure its bulk PM using archival HST imaging,
explore the stellar populations associated with it CO clouds, construct
parsec-scale extinction using IR-only techniques and combined UV-optical-IR
HST and JWST stellar SEDs (e.g., Dalcanton et al., 2015; Gordon et al., 2016).
On the technical side, our observations of WLM allow us to test DOLPHOT in a
regime of faint, crowded stars, that is typical of more distant systems, and
also test its capabilities for simultaneously measuring stellar photometry
across facilities (HST and JWST) and instruments (WFPC2, ACS, UVIS, NIRCam).

We required that one module of the NIRCam observations overlap UV-optical-IR
HST observations as well as the ALMA-detected CO clouds (Rubio et al., 2015).
The other NIRCam module overlaps with the deep optical HST/ACS imaging
presented in Albers et al. (2019). We enforced this configuration by requiring
orientations of 70–110∘ or 250–290∘. This orientation placed the NIRISS field
in the stellar halo of WLM, providing an expansion on the areal coverage to
build on the population gradient studies in WLM (e.g., Leaman et al., 2013;
Albers et al., 2019). We used artificial star tests associated with the deep
HST imaging of Albers et al. (2019) to ensure that our NIRCam observations
would only be modestly affected by stellar crowding.

We selected the F090W, F150W, F250M, and F430M for our NIRCam imaging of WLM.
As discussed in §3.3, the SW filter combination F090W$-$F150W is likely to be
widely used for SFHs measured from the ancient MSTO. Medium band filters in
the near-IR have proven remarkably efficient for photometric identification
and classification of AGB stars (e.g., Boyer et al., 2017). Our simulations,
based on these studies, suggest that the F250M and F430M filters should work
well for similar science. For NIRISS, we selected the F090W and F150W filters.

In the optical, measuring a well-constrained SFH for distant dwarf galaxies
requires a CMD that reaches a SNR$\sim$5–10 at the oldest MSTO (e.g., Cole et
al., 2007; Monelli et al., 2010; Cole et al., 2014; Skillman et al., 2014;
Gallart et al., 2015; Skillman et al., 2017; Albers et al., 2019). Using the
MIST stellar models, we find that the ancient MSTO for metal-poor stellar
population has $M_{F090W}=+3.4$ and $M_{F150W}=+3.1$. Using the parameters for
WLM in Table 1 and v1.1.1 of the ETC, we estimated that 33241s in F090W and
18724s in F150W would reach the required depths of $m_{F090W}=28.3$ and
$m_{F150W}=28.1$ with SNR=10 in each filter.

Table 2: A summary of our JWST ERS observations taken in 2022.  Target | Date | Camera | Filter |  $t_{exp}$ [s] | Groups | Integrations | Dithers
---|---|---|---|---|---|---|---
M92 | June 20–21 | NIRCam | F090W/F277W | 1245.465 | 6 | 1 | 4
|  | NIRCam | F150W/F444W | 1245.465 | 6 | 1 | 4
|  | NIRISS | F090W | 1245.465 | 7 | 1 | 4
|  | NIRISS | F150W | 1245.465 | 7 | 1 | 4
Draco II | July 3 | NIRCam | F090W/F480M | 11810.447 | 7 | 4 | 4
|  | NIRCam | F150W/F360M | 5883.75 | 7 | 2 | 4
|  | NIRISS | F090W | 11123.294 | 9 | 7 | 4
|  | NIRISS | F150W | 5883.75 | 10 | 3 | 4
WLM | July 23–24 | NIRCam | F090W/F430M | 30492.427 | 8 | 9 | 4
|  | NIRCam | F150W/F250M | 23706.788 | 8 | 7 | 4
|  | NIRISS | F090W | 26670.137 | 17 | 9 | 4
|  | NIRISS | F150W | 19841.551 | 19 | 6 | 4
|  |  |  |  |  |  |

###  3.6 Program Updates Since 2017

Our program has only had minor changes since it was first approved in November
2017. In 2021, we changed the DEEP2 readout patterns for WLM and Draco II to
MEDIUM8, following updated advice from a STScI technical review. The number of
groups and integrations was changed accordingly. In 2021, STScI increased our
allocated time from 27.35 hr to 27.5 hr to reflect updated overhead
accounting.

Following commissioning in spring 2022, STScI staff changed the range of
aperture PA ranges of Draco II from unconstrained to be have a value of
47–118∘, 143–280∘, or 354–24∘ in order to avoid the “claws”
feature333urlhttps://jwst-docs.stsci.edu/jwst-near-infrared-camera/nircam-
features-and-caveats/nircam-claws-and-wisps which is due to stray light from
the bright source that is outside the field of view of NIRCam (jdo, 2016;
Rigby et al., 2022).

##  4 Observations

We acquired NIRCam and NIRISS imaging of our three targets in July and August
of 2022. Figure 1 shows the NIRCam and NIRISS footprints for each of our
targets overlaid on ground-based images and Table 1 lists our observational
configurations. Full details on implementation can be viewed by retrieving
proposal 1334 in the Astronomer’s Proposal Tool (APT)444apt.stsci.edu.

We followed the same fundamental observing strategy for all three targets. For
each target, the primary instrument, NIRCam, imaged a single central field in
SW and LW filters as described in §4. NIRISS obtained imaging in parallel in
the two filters F090W and F150W.

All observations were taken with the 4 point subpixel dither pattern 4-POINT-
MEDIUM-WITH-NIRISS. This pattern ensured adequate PSF sampling for both
cameras, as well as improved rejection of cosmic rays, hot pixels, etc.

We opted against primary dithers. The main advantage of the primary dithers is
to fill the gaps between the dectectors and/or modules. However, the inter-gap
imaging is generally shallower than the rest of the data and requires more
JWST time to acquire. For our particular science cases, including primary
dithers added a modest amount of time to the program but would not
substantially enhance the data for our main science goals. Other science cases
(e.g., covering a large region such as PHAT did) may benefit from primary
dithers and filling gaps.

For M92, we used the SHALLOW4 readout pattern for NIRCam and the NIS readout
pattern for NIRISS. The orientation was constrained to an Aperture PA range of
156 to 226∘ in order to maximize the possibility of overlap between the NIRISS
field and existing HST imaging, while also allowing for reasonable
schedulability very early in the lifetime of JWST. The central location of the
NIRCam field ensures it will overlap with existing HST imaging. Including
overheads, the total time charged for observing M92 was $7037s$. The ratio of
science to charged time was $\sim 0.35$ (counting the primary NIRCam imaging
only) or $\sim 0.7$ (for both NIRCam and NIRISS imaging). The data volume was
well within the allowable range.

For Draco II, we used the MEDIUM8 readout pattern for NIRCam and the NIS
readout pattern for NIRISS. The NIRCam field was centered on the galaxy and
will largely overlap with existing HST imaging and Keck spectroscopic data.
The large angular separation of the NIRISS and NIRCam fields compared to the
size of Draco II means that the NIRISS field will contain few, if any, bona
fide members of Draco II. Including overheads, the total time charged for
observing Draco II was $24539s$. The ratio of science to charged time was
$\sim 0.71$ (counting the primary NIRCam imaging only) or $\sim 1.36$ (for
both NIRCam and NIRISS imaging). The data volume was well within the allowable
range.

For WLM, we used the MEDIUM8 readout pattern for NIRCam and the NIS readout
pattern for NIRISS. The NIRCam field was placed in the center of WLM in order
to overlap the low-metallicity molecular clouds discovered by ALMA as well as
deep archival optical HST imaging. Subsequent UV and near-IR HST imaging of
WLM obtained by members of our team were placed to maximize the chances of
overlap with our JWST observations of WLM.

Including overheads, the total time charged for observing WLM was $66884s$.
The ratio of science to charged time was $\sim 0.8$ (counting the primary
NIRCam imaging only) or $\sim 1.50$ (for both NIRCam and NIRISS imaging). The
large data volume (19.962 GB) for WLM generated a “Data Excess over Lower
Threshold” warning in APT. Though this level of warning is only a
recommendation to mitigate data volume excess, we nevertheless consulted with
STScI about mitigation strategies. However, we were unable to identify a way
to reduce data volume without compromising the science goals and no changes
were made. We caution that even longer integrations (e.g., that may be needed
for deep CMDs outside the LG) may require careful planning to avoid data
volume limitations.

Because our WLM observations span several continuous hours (Table 2), our
observations of WLM should allow us to recover the light curves of short
period variables (e.g., RR Lyrae). Generation of the light curves requires
performing photometry on calibrated images at each integration. At the time of
this paper’s writing, due to the large data volume, the STScI JWST reduction
pipeline generates integration-level calibrated images only when the time
series observation (TSO) mode is used. However, we were unable to use the TSO
mode of JWST as it does not permit dithers nor parallel observations. Instead,
producing the necessary images to generate light curves requires running the
reduction pipeline locally and creating custom time series analysis software,
which is beyond the scope of this paper.

In total, all of our science observations with NIRCam total 20.45 hr and
observations with NIRISS total 17.85 hr, indicating a fairly high ratio of
science-to-charged time.

Figure 3: CMDs from all 8 NIRCam chips for each of our targets. Panel (a): M92
extends from our bright saturation limit without Frame 0 data (F090W$\sim 19$)
just below the MSTO and reaches just fainter than the hydrogen burning limit
($F090W>28.2$). The inflection point of the MS at $F090W\sim 21$ is the MS
kink. Panel (b): The CMD of Draco II includes the MS kink at $F090W\sim 23$
and reaches the bottom of the stellar sequence provided by the standard PARSEC
models ($0.09\,M_{\odot}$). Panel (c): The CMD of WLM displays a wide variety
of stellar sequences that span a range of ages including young stars (e.g.,
the upper main sequence, red core helium burning stars), intermediate age
stars (e.g., AGB and RGB stars), and ancient stars that span a wide range of
colors and magnitude (e.g., RGB, SGB, oMSTO, lower MS). This CMD of WLM
(SNR$=$10 at $M_{F090W}=+4.6$) is the deepest taken for a galaxy outside the
immediate vicinity of the MW.

##  5 Photometry

We perform photometric reductions of our observations using the newly
developed NIRCam module for DOLPHOT. The detailed description of how DOLPHOT
works is well-documented in the literature (e.g., Dolphin, 2000; Dalcanton et
al., 2012a; Dolphin, 2016) and the functionality of the new NIRCam module are
summarized in § 2.2. A more detailed write up of the DOLPHOT NIRCam and NIRISS
modules are the subjects of an upcoming paper from our team. For this paper,
we only focus on the NIRCam data. The
WebbPSF555https://webbpsf.readthedocs.io/en/latest/ NIRISS PSF models appear
to concentrate light significantly more than what we have observed in the ERS
images. Consequently, our NIRISS photometry is not yet reliable and further
updates await improvements to the PSF models.

We first acquired all images from MAST. Per their FITS headers, versions of
all JWST images used in this paper have the following JWST pipeline versioning
information CAL_VER$=$1.7.2,CRDS_VER$=$11.16.11, and
CRDS_CTX$=$jwst_p1009.pmap. Next, we performed DOLPHOT reductions on the level
2b crf frames and use the level 3 i2d F150W drizzled image as the astrometric
reference frame. The use of this reference image ensures excellent internal
alignment of our image stack and ties the absolute astrometry to Gaia DR2. We
perform photomery in all four bands simultaneously. Since there is no spatial
overlap between the footprint of the two NIRCam modules, we photometer them
independently and merge the catalogs a posteriori.

We have not included Frame 0 in the photomeric reductions for this paper. As
of this paper’s writing, the JWST pipeline does not automatically provide
Frame 0, requiring these images be generated locally. Even when created with
our own execution of the JWST pipeline, we have yet to fully resolve how $1/f$
noise issue (e.g., Schlawin et al., 2020; Bagley et al., 2022) can be resolved
in a manner that ensures self-consistent photometry with DOLPHOT for all
frames. This issue will be addressed in more detail in our forthcoming
photometry paper. In the meantime, without Frame 0 data, our photometry
saturates at fainter magnitudes that we expect to recover once the Frame 0
data in included in our DOLPHOT runs.

For our photometric reductions, we adopt the DOLPHOT parameter setup
recommended by PHAT (Williams et al., 2014). We use the parameters recommend
for ACS from PHAT for the SW images and the WFC3/IR parameters for the LW
images. Subsequent data releases will make use of a set of parameters that we
are tailoring to NIRCam and NIRISS observations of resolved stars.

We make use of NIRCam PSF models generated with WebbPSF (Perrin et al., 2014).
We use the Optical Path Delay maps from July 24th, 2022 (the best matching
file to our WLM observation epoch). Inspection of JWST’s wavefront field at
the epochs of our three observations show little variation in the optical
performance of the telescope, justifying our choice of a common PSF library.
We are currently working in quantifying the full effect of JWST time-dependent
PSF variations on DOLPHOT photometry.

Table 3: Quality-metric criteria used to cull our DOLPHOT photometric catalogs.  Band | SNR | $Sharp^{2}$ | $Crowd$ | $Flag$ | $Object\,Type$
---|---|---|---|---|---
F090W | $\geq 4$ | $\leq 0.01$ | $\leq 0.5$ | $\leq 2$ | $\leq 1$
F150W | $\geq 4$ | $\leq 0.01$ | $\leq 0.5$ | $\leq 2$ | $\leq 1$
|  |  |  |  |

The final products of the DOLPHOT photometric run are catalogs with positions,
VEGAmag magnitudes (calibrated using the latests NIRCam zero-
points666https://jwst-docs.stsci.edu/jwst-near-infrared-camera/nircam-
performance/nircam-absolute-flux-calibration-and-zeropoints), uncertainties
based on photon noise, and a set of quality metrics related to the goodness of
point-source photometry (e.g., SNR, $\chi^{2}$ of the PSF fit, angular extent
of the source, crowding level). At this stage, metrics such as the photometric
error and the SNR are based on the Poissonian treatment of photon noise. While
in many cases this is sufficient for rough estimation, there are caveats
associated with this approximation, especially when measuring stars in very
crowded fields, or close to the limiting magnitude. We will provide a more
thorough discussion on SNRs estimation in our upcoming JWST DOLPHOT photometry
paper.

The catalogs provided by DOLPHOT are subsequently inspected and culled to
remove contaminants (e.g., artifacts, cosmic rays, extended sources) while
aiming to retain the largest number of bona fide stars. We identify a set of
quality-metric cuts, listed in Table 3, that provide a reasonable trade-off
between completeness and purity of the stellar sample; though for this initial
presentation we erred on the side of purity. The selection criteria need to be
satisfied in the F090W and F150W bands simultaneously. We only use the SW
bands as they are shared by all three targets, allowing a common set of
culling criteria. Our initial exploration suggests that the LW photometry may
improve star–galaxy separation at faint magnitude. This important topic is
being further investigated by members of our team.

Full characterization of uncertainties in resolved stellar populations studies
required artificial star tests (ASTs). ASTs consist of adding mock stars of
known properties into each frame and recovering them using the same
photometric procedure that is applied to the real data. For the purposes of
this paper focused on survey description, we have not included results of the
ASTs. The large data volume and multi-filter nature of the data make running
ASTs computationally challenging. We will present full AST results and
analyses in the upcoming JWST DOLPHOT photometry paper.

Figure 4: Select NIRCam CMDs of M92, along with HST WFC3/IR (HST-GO-11664) and
ACS/WFC (GO-9453, GO-10775, GO-12116, GO-16298) CMDS. Overploted are Solar-
scaled PARSEC stellar models at fixed age (13 Gyr) over a select range of
[Fe/H] values, which have been corrected to match the known
$\alpha$-enhancement of M92 (Mészáros et al., 2020). The SW NIRCam CMD extends
from our saturation limit (F090W $\sim 19$) to below the lowest-mass star from
the standard PARSEC models ($M=0.09\,M_{\odot}$) and into the expected
hydrogen burning limit regime ($0.078\leq M<0.08\,M_{\odot}$). The LW CMDs
reach slightly higher limits ($M=0.12\,M_{\odot}$). These are among the
deepest CMDs of a GC in existence and highlight how JWST will easily enable
the study of prominent features for low-mass stars such as the MS-kink and
lowest-mass stars. While the stellar models are in excellent agreement with
the more luminous stellar evolutionary phases (e.g., RGB, MSTO) in the HST
CMDs, they are $\sim 0.05$ mag too blue for lower-mass stars in the SW JWST
filters. This offset could be due to the complex atmospheres of low-mass
stars.  Figure 5: Select NIRCam CMDs of Draco II along with the deepest
optical CMD which is based on HST/ACS imaging (GO-14734; panel b). Overploted
are PARSEC stellar models at fixed age (13 Gyr) for the same [Fe/H] values
selected for M92. The SW CMD extends to the lowest-mass stellar model
($M=0.09\,M_{\odot}$), making it the deepest CMD of a MW satellite galaxy to
date. The exquisite depth of our data indicate how JWST enables a variety of
science including constraining the low-mass IMF and quantifying low-mass star
features (e.g,. the MS kink and objects near the hydrogen burning limit)
outside the MW.  Figure 6: Select NIRCam CMDs of WLM along with the deepest
available HST/ACS optical CMD from Albers et al. (2019). Overplotted are
isochrones from the PARSEC Solar-scaled stellar models at a fixed metallicity
of [Fe/H]$=-1.2$ dex for a variety of indicated ages. Panel (a) is the deepest
CMD of an isolated dwarf galaxy to date, extending $\sim 1.5$ mag below the
oldest MSTO; it is deeper than the HST CMD despite $\sim 7000$s less
integration time. The isochrones indicate the variety of stellar ages present
in WLM. The LW CMD shown in panel (d) extends $\sim 2$ magnitudes below the
red clump. Such deep CMDs show that JWST will provide for a variety of science
at different cosmic epochs including exquisite lifetime SFHs, the study of
evolved red stars, TRGB distances, and very young stars in galaxies outside
the LG.

##  6 Discussion

###  6.1 Color-Magnitude Diagrams

Figure 3 shows the NIRCam SW CMDs for all three targets over the same
magnitude range. The juxtaposition of these CMDs illustrates the quality and
diversity of science possibilities provided by our program. In each panel, we
overplot select SNRs reported by DOLPHOT. As discussed in §5, these SNRs are
solely based on photon noise and do not account for the effects of crowding
and incompletness. We discuss SNRs further in §6.2.

The SNRs for each target are remarkable, with SNRs ranging from 500 near the
MS kink to 10 for the lowest mass stars in M92 and Draco II. WLM has a photon
noise-based SNR of $\sim 50$ at the oldest MSTO, making it the highest
fidelity resolved stars observation of a distant dwarf in existence. We now
discuss the multi-band CMDs for each of our targets in more detail.

Figures 4–6 show illustrative NIRCam CMDs in a selection of filter
combinations for each ERS target. In all cases, we apply the catalog culling
parameters described in §5 and listed in Table 3.

For guidance, we overplot a selection of stellar isochrones from the PARSEC
v1.2S stellar libraries (Bressan et al., 2012; Chen et al., 2015). These
models span the full range of metallicities and ages needed to characterize
our datasets. We have adjusted these isochrones to the distances and
reddenings listed in Table 1.

####  6.1.1 M92

Figure 4 shows select NIRCam CMDs for M92, along with select HST-based CMDs
for comparison. The HST CMDs were reduced using DOLPHOT and the parameters
recommended in Williams et al. (2014).

We overplot select PARSEC isochrones at a fixed age of 13 Gyr with varying
metallicities, which we discuss below. Though stars brighter than $F090W\sim
19$ are omitted due to saturation effects (see §5), key CMD features for
faint, low-mass stars are clearly visible. Notably, the MS kink, which is due
to opacity effects in M dwarfs, the region in which is in panels (a) - (c) at
$F090W\sim 21$ and $F150W\sim 20$. The MS kink exhibits the sharpest
inflection point in the F150W$-$F277W filter combination. The kink is much
less pronounced in the F277W$-$F444W CMD (panel d). This is partially due to
the lower SNRs of the LW observations as well as the LW filters being far into
the Rayleigh–Jeans tail of the stars’ spectral energy distributions (SEDs),
which makes them only weakly sensitive to stellar temperature, thus resulting
in similar colors and a less obvious kink.

Our NIRCam observations have produced the deepest CMD of M92 to date. The data
extend fainter than the lowest stellar mass ($0.09\,M_{\odot}$) available from
the standard PARSEC stellar library in the SW filters and to slightly higher
masses ($0.12\,M_{\odot}$) in the LW filters.

The faintest objects in the F090W$-$F150W CMD fall into the bright end of the
expected hydrogen burning sequence. The exact mass at which a star-like object
cannot sustain hydrogen fusion has long been debated (e.g., Hayashi & Nakano,
1963; Kumar, 1963; Chabrier et al., 2000). For this analysis, we computed
custom PARSEC models with a mass resolution of 0.002 $M_{\odot}$ and find that
that the minimum mass for hydrogen burning is $0.078\leq M<0.08$ $M_{\odot}$
for a metallicity of [Fe/H]$=-1.7$ dex and an age of 13 Gyr. This translates
to a magnitude range of $28.2<m_{F090W}\leq 29.5$ mag in M92. This depth is
quite remarkable considering our observations only consistent of $\sim 1050s$
in each filter. In comparison, the faintest stars in the HST WFC3/IR CMD
(panel e) are a few magnitudes brighter despite $\sim 1200s$ of integration
time in each filter. To date, comparably deep IR studies of metal-poor,
extremely low-mass stars with HST have been limited to the nearest GCs (e.g.,
M4; Dieball et al., 2016, 2019). As our M92 data shows, the superior
sensitivity of NIRCam will make such studies possible throughout the MW.

The PARSEC models over-plotted in Figure 4 are nominally higher than the
metallicity of M92 ([Fe/H] $=-2.23$ dex) derived from high-resolution APOGEE
spectroscopy (Mészáros et al., 2020). This discrepancy is because the current
version of the PARSEC models are Solar-scaled, whereas M92 is highly
$\alpha$-enhanced with $[\alpha$/Fe] $\sim 0.5$ dex. Well-established
corrections can be applied to match Solar-scaled models with $\alpha$-enhanced
populations (e.g., Salaris et al., 1993). For M92, the corrective factor for
the PARSEC models results in values of [Fe/H] $\sim 0.5$ dex higher than
derived from spectroscopy. PARSEC models with $\alpha$-enhancements are under
construction and will mitigate the need to apply such corrections.

Overall, the PARSEC models are in reasonably good agreement with the NIRCam
CMDs. For the F150W$-$F444W and F277W$-$F444W CMDs, the models trace the locus
of the data quite well. However, for the F090W$-$F150W and F150W$-$F277W CMDs,
the models are systematically too blue by $\sim 0.05$ mag. The source of this
offset is not due to distance, reddening, age, or metallicity as these same
models are well-matched to the MSTO in the brighter HST CMDs (panels e and f
of Figure 4). In general, stars above the MS kink are well-matched by the
models in the LW NIRCam and HST filters, limiting the offsets to only SWs. One
possible source of the offset is the presence of poorly modeled absorption
features (e.g., TiO) in the atmospheres of very cool, low-mass stars. A
detailed exploration of this offset is beyond the scope of this paper, but we
note that deep JWST imaging of a larger set of GCs in several filters has the
potential to help elucidate the exact nature of this issue.

The NIRCam CMDs also exhibit scatter in color that is larger than photon noise
and variations in age or metallicity. The most likely explanation is the
presence of multiple chemically distinct populations. Like many luminous
Galactic GCs, M92 exhibits multiple populations with distinct abundance
patterns (e.g., Mészáros et al., 2020). Other GCs with deep CMDs and similar
abundance patterns show a broadening of stellar sequences at and below the MS
kink. This broadening has been attributed to the persistence of these
chemically distinct sequences to the lowest stellar masses (e.g., Milone et
al., 2012, 2014; Correnti et al., 2016; Milone et al., 2017), which is thought
to be driven by oxygen variations expressed via water lines (e.g., Dotter et
al., 2015; VandenBerg et al., 2022). Finally, we note that some of the scatter
could be due to NIRCam zero point calibrations which will not be finalized
with uncertainties until at least the end of Cycle 1 (Gordon et al., 2022).

####  6.1.2 Draco II

Figure 5 shows select NIRcam CMDs of Draco II, along with the deepest existing
optical HST CMD for reference (GO-14734; PI N. Kallivayalil). Like M92, the
brightest stars in Draco II suffer from saturation and are not included in our
current photometric reduction. We include the same PARSEC isochrones as shown
in M92 (Figure 4) as Draco II hosts a comparably ancient (13 Gy), metal-poor,
and likely $\alpha$-enhanced stellar population (e.g., Longeard et al., 2018;
Simon, 2019). The selected isochrones provide a good fit of the MSTO in the
HST CMD (panel b) suggesting the adopted parameters (i.e., age, metallicity,
distance, extinction) are reasonable, but as with M92, the models are slightly
too blue in the NIRCam SW CMDs of Draco II.

Our F090W$-$F150W CMD of Draco II is the deepest CMD of a galaxy outside the
MW and has imaged the lowest-mass stars outside the MW ($0.09\,M_{\odot}$).

Previously, the deepest CMD in an external galaxy was from Gennaro et al.
(2018b), which used HST WFC3/IR data of MW UFD Coma Berenicies to study its
low-mass IMF. From $32780s$ of integration time in each filter, their F110W
and F160W photometry reached a usable low-mass limit of $0.17\,M_{\odot}$,
whereas our data extend to $0.09\,M_{\odot}$. The large dynamic range of
stellar masses in Draco II provides excellent leverage for a low-mass IMF
measurement. Tight constraints on the IMF in Draco II could provide a new
means of distinguishing whether faint stellar systems are dark-matter
dominated dwarf galaxies or GCs (e.g., Willman & Strader, 2012; Baumgardt et
al., 2022), as well as insight into star formation in extreme environments
(e.g., Geha et al., 2013; Krumholz et al., 2019).

The width of the lower MS is in excess of photometric noise. Metallicities
derived from more luminous stars in Draco II suggest a spread of $\sigma\sim
0.5$ dex (e.g., Li et al., 2017; Longeard et al., 2018; Fu et al., 2022),
which may contribute to this scatter. Background galaxies are also a source of
contamination and likely contribute to the scatter, particularly at the
faintest magnitudes. The combination of very deep imaging and the sparsity of
Draco II’s stellar population mean that background galaxies are a large source
of contamination. Our preliminary investigations indicate that the multi-color
NIRCam photometry may be efficient for star-galaxy separation (Warfield et al.
in prep).

The F150$-$F360M CMD (panel c) extends to a comparably low stellar mass as the
SW CMD, albeit at lower SNR. The LW CMD (panel d) is much shallower; though
the primary purpose of these filters is to explore their potential as
photometric metallicity indicators akin to the Calcium H & K filters being
used in the optical (e.g., Starkenburg et al., 2017a; Longeard et al., 2018;
Fu et al., 2022).

As with M92, the stellar isochrones provide a good qualitative match to the
data. The shape and magnitude of the MS kink appears to track the data well.
However, as discussed in the context of M92 the models are modestly too blue
compared to the data.

####  6.1.3 WLM

Figure 6 shows select NIRCam CMDs for WLM, along with the deepest optical CMD
of WLM taken with HST. Due to its large distance, few stars in WLM are
affected by saturation.

The CMDs of WLM exhibit a wide variety of stellar sequences that span a range
of ages and phases of evolution. Examples include the young MS, the RGB and
AGB, the HB, and the oldest MSTO. The relative positions of these features are
generally similar to what is known for optical CMDs with subtle changes due to
the shift to IR wavelengths (e.g., Dalcanton et al., 2012b; Williams et al.,
2014; Gull et al., 2022). For example, the HB slopes to fainter values at
bluer wavelengths as hot blue HB stars are less luminous at IR wavelengths.
Similarly, red stars (e.g., RGB, AGB) become more luminous as the IR
wavelengths are closer to their peak temperatures compared to optical
wavelengths.

Figure 6 shows that our NIRCam SW CMD of WLM is at $\sim 1$ mag deeper than
the HST/ACS CMD despite similar integration times ($\sim 54200$s for NIRCam
versus $\sim 61400$s for ACS). This increase owes primarily to the increased
sensitivity of JWST at these wavelengths. McQuinn et al. (in prep.) is
deriving the SFHs from both datasets to quantify the capabilites of JWST
imaging for detailed SFH determinations.

More broadly, our CMDs of WLM are the deepest in existence for an isolated
dwarf galaxy. Prior to our program, HST/ACS observations of Leo A from Cole et
al. (2007) extended to the lowest stellar masses in an galaxy outside the
immediate vicinity of the MW. The HST observations of Leo A reach nearly as
deep, but the larger distance of WLM (0.5 mag farther) means that our
measurements actually extend to less luminous stars on the MS.

Though not as deep as the SW data, the LW is remarkably deep for medium bands.
The F090W$-$F250M data extends below the oldest MSTO, making it the deepest
medium band data available for an isolated dwarf galaxy. The F250M$-$F430M
(panel d) CMD extends well below the red clump. This data is expected to
provide an excellent means of identifying AGB stars and helping constrain
their underlying physics.

The over-plotted isochrones provide a reasonable match to the data. In this
case, we have selected a single metallicity of ${\rm[Fe/H]}=-1.2$ dex matched
to RGB spectroscopic abundances (Leaman et al., 2009), and plotted select ages
that range from 50 Myr to 13 Gyr. Visually, the PARSEC models provide a good
qualitative match to the data. The level of agreement will be formally
quantified in McQuinn et al. (in prep.).

###  6.2 Comparisons with the Exposure Time Calculator

Our photometry provides an opportunity to gauge the accuracy of the NIRCam ETC
in practice.

In Figure 7, we consider the SNRs for stars in M92 as reported by DOLPHOT
against what is reported by the NIRCam ETC. M92 is the best of our three
targets for this exploratory exercise as it is not particularly crowded and
its CMD is well-populated. The lack of crowding means that the SNRs reported
by DOLPHOT are a reasonable proxy for real noise, whereas ASTs are necessary
to accurately assess the noise in even moderately crowded images.

Figure 7: Comparisons between the photon noise-based SNRs from the DOLPHOT
F090W and F150W photometry of M92 (grey shaded region) and the expected SNRs
from v2.0 of the NIRCam ETC (black lines). Over most of the dynamic ranges in
magnitude the SNRs reported by DOLPHOT and the ETC are consistent within
scatter ($\sim 20$%) of the data. Deviations at the bright end may be due to
the presence of saturated pixels, while PSF shape, non-stellar objects (e.g.,
background galaxies), and incompleteness may contribute to a slight increase
in the ratio at the faintest F150W magnitudes.

We compute SNRs as a function of F090W and F150W by only considering stars
that pass a stricter version of the culling criteria listed in §5.
Specifically, we require that each star has crowd $\leq 0.1$, which eliminates
all but the least crowded stars. We also only consider stars with
$0.2<F090W-F150W<1.5$, which isolates the MS in the SW filters and removes
much of the contamination (e.g., background galaxies, diffraction spike
artifacts) from our analysis. Finally, we exclude the brightest stars as they
may be affected by (partial) saturation. We specifically only consider stars
fainter than $F090W=20$ and $150W=19$ for their respective SNR calculations.

From stars that pass these cuts, we compute the 50th, 16th, and 84th
percentiles of the F090W and F150W SNR distributions in 0.25 mag bins over the
entire magnitude ranges considered.

For the expected SNRs, we use v2.0 of the JWST ETC to compute SNR as a
function of F090W and F150W. In the ETC, the detector strategies are set to
match our F090W and F150W observational set up for M92 as listed in Table 2
and described in §3.3. We verified that the integration times in the ETC are
identical to what our program acquired.

For the ETC scene, we used a K5V star ($T_{\text{eff}}=4250$ K, $\log(g)=4.5$
dex) from the Phoenix stellar models. Though the stellar type varies over the
color and luminosity range considered, we found that reasonable changes in the
choice of stellar atmosphere only affected our findings at the $\sim 5$%
level. For simplicity, we adopted a single stellar atmosphere model for this
calculation.

We adopted an extinction of $A_{V}=0.06$ mag and a MW extinction curve. We set
the background model to the central coordinates of M92 on June 20th, 2022, the
date of our observations. We computed the SNR in the F090W and F150Ws filter
in 0.5 mag steps from 19 to 30 mag in each filter, renormalizing after
extinction was applied.

The result is a smooth variation in SNR as a function of magnitude. We
interpolated the results onto a finer magnitude grid for clearer comparison
with the DOLPHOT results. Interpolation errors are $<1$%.

To compute the SNR, we used the default aperture photometry setup in the
NIRCam ETC. Specifically, this uses an aperture radius of 0.1″ and performs
background subtraction using an annulus 0.22 to 0.4″ from the source. For both
F090W and F150W, this radius within the aperture radius range of 2-3$\times$
the PSF FWHM specified in JDOX777https://jwst-docs.stsci.edu/jwst-near-
infrared-camera/nircam-performance/nircam-point-spread-functions as
recommended by the JWST help desk. We will explore more filter SNRs and
variations in the ETC photometric set p in a future paper.

Figure 7 shows a comparison between the SNRs reported by DOLPHOT (grey shaded
regions) and the NIRCam ETC (black lines) as a function of F090W and F150W
magnitude. The bottom panels show the ratios of the DOLPHOT and ETC SNRs. Both
visually and quantitatively, the expected ETCs agree quite well. For most of
the magnitude ranges, the DOLPHOT and ETC SNRs agree within $\sim 20$%, which
is within the bounds of our uncertainty range. The small structures in the
residuals over this range are due to finite numbers of real stars in each bin.
There are some noticeable deviations from unity at bright magnitudes for both
F090W and F150W. We believe that these may be due to saturation effects that
might be mitigated by improved data quality masks and/or the use of Frame 0
data. Both will be explored in our forthcoming DOLPHOT NIRcam module paper.
Similarly, the increased ratio at the faintest F150W magnitudes is not overly
concerning as small variations in the PSF shape (Dolphin, 2000) and the
presence of non-stellar artifacts at the very bottom of the CMD can affect the
photon noise-based SNRs. The uptick could also be caused by the removal of
objects that don’t meet our culling criteria; formally a correct calculation
requires factoring in completeness as determined by ASTs.

Overall, this comparison provides preliminary indications that v2.0 of the ETC
provides reasonable SNR estimates for fairly uncrowded stars imaged with
NIRCam. Of course, in practice, many resolved stellar systems that will be
targeted by NIRCam will be more affected by crowding than M92, which can lead
to larger discrepancies in the expected versus recovered SNR. For HST, this
effect is partially mitigated by the optimal SNR reported by its
ETC888https://etc.stsci.edu/etcstatic/users_guide/1_3_imaging.html. This
number reflects that expected SNR for an isolated point source recovered by
PSF fitting and is generally a factor of 1.5–2 higher than the regular SNR
reported by the HST ETC. Our initial analysis of M92 suggests that the
baseline SNRs from the NIRCam ETC may not be off by as large a factor.
However, further exploration in a variety of images with variable crowding,
stellar type, etc. Ultimately, ASTs will aid in calculation of SNRs seen in
the data over a range of stellar densities. We will carry out such an
exploration in the context of our NIRCam and NIRISS DOLPHOT photometry paper.

##  7 Summary

We have undertaken the JWST resolved stellar populations Early Release Science
program in order to establish JWST as a the premier facility for resolved
stellar populations early JWST’s lifetime. In this paper, we have described
the motivation, planning, implementation, execution, and present NIRCam CMDs
from preliminary photometric reductions with DOLPHOT. Some key takeaways from
our survey include:

  * •

Our 27.5 hr program obtained NIRCam (primary) and NIRISS (parallel) imaging of
3 diverse targets: Milky Way globular cluster M92, satellite ultra-faint
galaxy Draco II, and more distant (0.9 Mpc) star-forming galaxy WLM. A summary
of their properties are listed in Table 1 while a summary of our JWST
observations for each target are listed in Table 2. These targets were
selected in order to enable a variety of science and technical goals related
to resolved stellar populations analysis as described in §2.

  * •

This ERS program facilitated the development of NIRCam and NIRISS modules for
DOLPHOT, a widely used stellar crowded field photometry package. We used our
ERS targets to test these modules for a variety of image properties (e.g.,
various filter combinations, over a large dynamic range in stellar crowding).
We describe the application of DOLPHOT to our ERS data in §5. Beta versions of
these DOLPHOT modules, along with theoretical PSF models for all NIRCam and
NIRISS filters are publicly available on our team website and on the DOLPHOT
website.

  * •

We presented preliminary NIRCam CMDs in select SW and LW filter combinations
from a first pass DOLPHOT reduction. The CMDs are among deepest CMDs in
existence for each class of object. The F090W$-$F150W CMD of M92 touches the
hydrogen burning limit ($F090W>28.2$; ($M<0.08$ $M_{\odot}$). The
F090W$-$F150W CMD of Draco II reaches the the bottom of the stellar sequence
(0.09 $M_{\odot}$) in the standard PARSEC models. The F090W$-$F150W CMD of WLM
extends $\sim$1.5 mag below the oldest MSTOs in WLM.

  * •

We compare our NIRCam CMDs to select age and metallicity isochrones from the
PARSEC models. We find that the models are in generally good agreement with
all JWST CMDs, though we find them to be $\sim 0.05$ mag systematically bluer
of the lower MS in M92 and Draco II. We posit that this color offset may be
due to the complexity of stellar atmospheres in extremely low-mass stars that
is currently not well-captured in theoretical stellar atmospheres. A notable
example includes the known sensitivity of color to oxygen abundance (e.g.,
VandenBerg et al., 2022)

  * •

We compare the photon-noise based SNRs for the F090W and F150W reported by
DOLPHOT for stars in M92 with expectations from v2.0 of the NIRCam ETC. We
find they agree within $\sim 20$% over most of the magnitude range, with
slightly larger deviations at the very bright and very faint limits. The
differences may be due to saturation effects at the bright end and selection
effects and/or subtle mismatches between theoretical and observed PSFs at the
faint end. We caution that this preliminary comparison does not capture
effects such as crowding, which is important in distant dwarf galaxies such as
WLM.

  * •

We are in the process of optimizing DOLPHOT for use with NIRCam and NIRISS.
All technical details of the DOLPHOT modules and their application to our ERS
data the subject of an upcoming publication on crowded field photometry.

The authors would like to thank David W. Hogg for his input on the program and
paper. This work is based on observations made with the NASA/ESA/CSA James
Webb Space Telescope. The data were obtained from the Mikulski Archive for
Space Telescopes at the Space Telescope Science Institute, which is operated
by the Association of Universities for Research in Astronomy, Inc., under NASA
contract NAS 5-03127 for JWST. These observations are associated with program
DD-ERS-1334. This program also benefits from recent DOLPHOT development work
based on observations made with the NASA/ESA Hubble Space Telescope obtained
from the Space Telescope Science Institute, which is operated by the
Association of Universities for Research in Astronomy, Inc., under NASA
contract NAS 5–26555. These observations are associated with program HST-
GO-15902.

## References

  * jdo (2016) 2016, JWST User Documentation (JDox), JWST User Documentation Website
  * Albers et al. (2019) Albers, S. M., Weisz, D. R., Cole, A. A., et al. 2019, MNRAS, 490, 5538, doi: 10.1093/mnras/stz2903
  * Anderson & King (2000) Anderson, J., & King, I. R. 2000, PASP, 112, 1360, doi: 10.1086/316632
  * Bagley et al. (2022) Bagley, M. B., Finkelstein, S. L., Koekemoer, A. M., et al. 2022, arXiv e-prints, arXiv:2211.02495.  https://arxiv.org/abs/2211.02495
  * Bastian et al. (2010) Bastian, N., Covey, K. R., & Meyer, M. R. 2010, ARA&A, 48, 339, doi: 10.1146/annurev-astro-082708-101642
  * Battaglia et al. (2022) Battaglia, G., Taibi, S., Thomas, G. F., & Fritz, T. K. 2022, A&A, 657, A54, doi: 10.1051/0004-6361/202141528
  * Baumgardt et al. (2022) Baumgardt, H., Faller, J., Meinhold, N., McGovern-Greco, C., & Hilker, M. 2022, MNRAS, 510, 3531, doi: 10.1093/mnras/stab3629
  * Beaton et al. (2016) Beaton, R. L., Freedman, W. L., Madore, B. F., et al. 2016, ArXiv e-prints.  https://arxiv.org/abs/1604.01788
  * Bechtol et al. (2015) Bechtol, K., Drlica-Wagner, A., Balbinot, E., et al. 2015, ApJ, 807, 50, doi: 10.1088/0004-637X/807/1/50
  * Bono et al. (2010) Bono, G., Stetson, P. B., Walker, A. R., et al. 2010, PASP, 122, 651, doi: 10.1086/653590
  * Boyer (2010) Boyer. 2010, Is Dust Forming on the Red Giant Branch in 47 Tuc?
  * Boyer et al. (2006) Boyer, M. L., Woodward, C. E., van Loon, J. T., et al. 2006, AJ, 132, 1415, doi: 10.1086/506518
  * Boyer et al. (2015) Boyer, M. L., McQuinn, K. B. W., Barmby, P., et al. 2015, ApJS, 216, 10, doi: 10.1088/0067-0049/216/1/10
  * Boyer et al. (2017) Boyer, M. L., McQuinn, K. B. W., Groenewegen, M. A. T., et al. 2017, ApJ, 851, 152, doi: 10.3847/1538-4357/aa9892
  * Boyer et al. (2019) Boyer, M. L., Williams, B. F., Aringer, B., et al. 2019, ApJ, 879, 109, doi: 10.3847/1538-4357/ab24e2
  * Boylan-Kolchin & Weisz (2021) Boylan-Kolchin, M., & Weisz, D. R. 2021, MNRAS, 505, 2764, doi: 10.1093/mnras/stab1521
  * Boylan-Kolchin et al. (2015) Boylan-Kolchin, M., Weisz, D. R., Johnson, B. D., et al. 2015, MNRAS, 453, 1503, doi: 10.1093/mnras/stv1736
  * Bressan et al. (2012) Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127, doi: 10.1111/j.1365-2966.2012.21948.x
  * Brown et al. (2006) Brown, T. M., Smith, E., Ferguson, H. C., et al. 2006, ApJ, 652, 323, doi: 10.1086/508015
  * Brown et al. (2012) Brown, T. M., Tumlinson, J., Geha, M., et al. 2012, ApJ, 753, L21, doi: 10.1088/2041-8205/753/1/L21
  * Brown et al. (2014) —. 2014, ApJ, 796, 91, doi: 10.1088/0004-637X/796/2/91
  * Bullock & Boylan-Kolchin (2017) Bullock, J. S., & Boylan-Kolchin, M. 2017, ARA&A, 55, 343, doi: 10.1146/annurev-astro-091916-055313
  * Buonanno et al. (1979) Buonanno, R., Corsi, C. E., de Biase, G. A., & Ferraro, I. 1979, in Image Processing in Astronomy, ed. G. Sedmak, M. Capaccioli, & R. J. Allen, 354
  * Campbell et al. (1992) Campbell, B., Hunter, D. A., Holtzman, J. A., et al. 1992, AJ, 104, 1721, doi: 10.1086/116355
  * Casey & Schlaufman (2015) Casey, A. R., & Schlaufman, K. C. 2015, ApJ, 809, 110, doi: 10.1088/0004-637X/809/2/110
  * Chaboyer et al. (1996) Chaboyer, B., Demarque, P., Kernan, P. J., & Krauss, L. M. 1996, Science, 271, 957, doi: 10.1126/science.271.5251.957
  * Chabrier et al. (2000) Chabrier, G., Baraffe, I., Allard, F., & Hauschildt, P. 2000, ApJ, 542, 464, doi: 10.1086/309513
  * Chastenet et al. (2019) Chastenet, J., Sandstrom, K., Chiang, I.-D., et al. 2019, ApJ, 876, 62, doi: 10.3847/1538-4357/ab16cf
  * Chen et al. (2015) Chen, Y., Bressan, A., Girardi, L., et al. 2015, MNRAS, 452, 1068, doi: 10.1093/mnras/stv1281
  * Choi et al. (2016) Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102, doi: 10.3847/0004-637X/823/2/102
  * Cole et al. (2014) Cole, A. A., Weisz, D. R., Dolphin, A. E., et al. 2014, ApJ, 795, 54, doi: 10.1088/0004-637X/795/1/54
  * Cole et al. (2007) Cole, A. A., Skillman, E. D., Tolstoy, E., et al. 2007, ApJ, 659, L17, doi: 10.1086/516711
  * Collins et al. (2013) Collins, M. L. M., Chapman, S. C., Rich, R. M., et al. 2013, ApJ, 768, 172, doi: 10.1088/0004-637X/768/2/172
  * Conroy (2013) Conroy, C. 2013, ARA&A, 51, 393, doi: 10.1146/annurev-astro-082812-141017
  * Conroy et al. (2018) Conroy, C., Villaume, A., van Dokkum, P. G., & Lind, K. 2018, ApJ, 854, 139, doi: 10.3847/1538-4357/aaab49
  * Conroy et al. (2019) Conroy, C., Bonaca, A., Cargile, P., et al. 2019, ApJ, 883, 107, doi: 10.3847/1538-4357/ab38b8
  * Correnti et al. (2016) Correnti, M., Gennaro, M., Kalirai, J. S., Brown, T. M., & Calamida, A. 2016, ApJ, 823, 18, doi: 10.3847/0004-637X/823/1/18
  * Correnti et al. (2018) Correnti, M., Gennaro, M., Kalirai, J. S., Cohen, R. E., & Brown, T. M. 2018, ApJ, 864, 147, doi: 10.3847/1538-4357/aad805
  * Dalcanton et al. (2009) Dalcanton, J. J., Williams, B. F., Seth, A. C., et al. 2009, ApJS, 183, 67, doi: 10.1088/0067-0049/183/1/67
  * Dalcanton et al. (2012a) Dalcanton, J. J., Williams, B. F., Melbourne, J. L., et al. 2012a, ApJS, 198, 6, doi: 10.1088/0067-0049/198/1/6
  * Dalcanton et al. (2012b) Dalcanton, J. J., Williams, B. F., Lang, D., et al. 2012b, ApJS, 200, 18, doi: 10.1088/0067-0049/200/2/18
  * Dalcanton et al. (2015) Dalcanton, J. J., Fouesneau, M., Hogg, D. W., et al. 2015, ApJ, 814, 3, doi: 10.1088/0004-637X/814/1/3
  * Di Valentino et al. (2021) Di Valentino, E., Anchordoqui, L. A., Akarsu, Ö., et al. 2021, Astroparticle Physics, 131, 102605, doi: 10.1016/j.astropartphys.2021.102605
  * Dieball et al. (2019) Dieball, A., Bedin, L. R., Knigge, C., et al. 2019, MNRAS, 486, 2254, doi: 10.1093/mnras/stz996
  * Dieball et al. (2016) —. 2016, ApJ, 817, 48, doi: 10.3847/0004-637X/817/1/48
  * Dohm-Palmer et al. (1998) Dohm-Palmer, R. C., Skillman, E. D., Gallagher, J., et al. 1998, AJ, 116, 1227, doi: 10.1086/300514
  * Dolphin (2000) Dolphin, A. E. 2000, PASP, 112, 1383, doi: 10.1086/316630
  * Dolphin (2002) —. 2002, MNRAS, 332, 91, doi: 10.1046/j.1365-8711.2002.05271.x
  * Dolphin (2016) —. 2016, ArXiv e-prints.  https://arxiv.org/abs/1605.01461
  * Dotter et al. (2008) Dotter, A., Chaboyer, B., Jevremović, D., et al. 2008, ApJS, 178, 89, doi: 10.1086/589654
  * Dotter et al. (2015) Dotter, A., Ferguson, J. W., Conroy, C., et al. 2015, MNRAS, 446, 1641, doi: 10.1093/mnras/stu2170
  * Draine (2007) Draine, B. T. 2007, Dust Masses, PAH Abundances, and Starlight Intensities in the SINGS Galaxy Sample
  * Drlica-Wagner et al. (2015) Drlica-Wagner, A., Bechtol, K., Rykoff, E. S., et al. 2015, ApJ, 813, 109, doi: 10.1088/0004-637X/813/2/109
  * Ekström et al. (2012) Ekström, S., Georgy, C., Eggenberger, P., et al. 2012, A&A, 537, A146, doi: 10.1051/0004-6361/201117751
  * El-Badry et al. (2017) El-Badry, K., Weisz, D. R., & Quataert, E. 2017, ArXiv e-prints.  https://arxiv.org/abs/1701.02347
  * El-Badry et al. (2016) El-Badry, K., Wetzel, A., Geha, M., et al. 2016, ApJ, 820, 131, doi: 10.3847/0004-637X/820/2/131
  * Eldridge & Stanway (2022) Eldridge, J. J., & Stanway, E. R. 2022, arXiv e-prints, arXiv:2202.01413.  https://arxiv.org/abs/2202.01413
  * Eldridge et al. (2017) Eldridge, J. J., Stanway, E. R., Xiao, L., et al. 2017, PASA, 34, e058, doi: 10.1017/pasa.2017.51
  * Escala et al. (2020) Escala, I., Gilbert, K. M., Kirby, E. N., et al. 2020, ApJ, 889, 177, doi: 10.3847/1538-4357/ab6659
  * Filion et al. (2020) Filion, C., Kozhurina-Platais, V., Avila, R. J., Platais, I., & Wyse, R. F. G. 2020, ApJ, 901, 82, doi: 10.3847/1538-4357/abafb6
  * Frebel & Norris (2015) Frebel, A., & Norris, J. E. 2015, ARA&A, 53, 631, doi: 10.1146/annurev-astro-082214-122423
  * Freedman et al. (1994) Freedman, W. L., Hughes, S. M., Madore, B. F., et al. 1994, ApJ, 427, 628, doi: 10.1086/174172
  * Freedman et al. (2001) Freedman, W. L., Madore, B. F., Gibson, B. K., et al. 2001, ApJ, 553, 47, doi: 10.1086/320638
  * Freedman et al. (2020) Freedman, W. L., Madore, B. F., Hoyt, T., et al. 2020, ApJ, 891, 57, doi: 10.3847/1538-4357/ab7339
  * Fritz et al. (2018) Fritz, T. K., Battaglia, G., Pawlowski, M. S., et al. 2018, ArXiv e-prints, arXiv:1805.00908.  https://arxiv.org/abs/1805.00908
  * Fu et al. (2022) Fu, S. W., Weisz, D. R., Starkenburg, E., et al. 2022, ApJ, 925, 6, doi: 10.3847/1538-4357/ac3665
  * Gallart et al. (2005) Gallart, C., Zoccali, M., & Aparicio, A. 2005, ARA&A, 43, 387, doi: 10.1146/annurev.astro.43.072103.150608
  * Gallart et al. (2015) Gallart, C., Monelli, M., Mayer, L., et al. 2015, ApJ, 811, L18, doi: 10.1088/2041-8205/811/2/L18
  * Geha et al. (2009) Geha, M., Willman, B., Simon, J. D., et al. 2009, ApJ, 692, 1464, doi: 10.1088/0004-637X/692/2/1464
  * Geha et al. (2013) Geha, M., Brown, T. M., Tumlinson, J., et al. 2013, ApJ, 771, 29, doi: 10.1088/0004-637X/771/1/29
  * Gennaro & Robberto (2020) Gennaro, M., & Robberto, M. 2020, ApJ, 896, 80, doi: 10.3847/1538-4357/ab911a
  * Gennaro et al. (2018a) Gennaro, M., Geha, M., Tchernyshyov, K., et al. 2018a, ApJ, 863, 38, doi: 10.3847/1538-4357/aaceff
  * Gennaro et al. (2018b) Gennaro, M., Tchernyshyov, K., Brown, T. M., et al. 2018b, ApJ, 855, 20, doi: 10.3847/1538-4357/aaa973
  * Gilbert et al. (2019a) Gilbert, K., Tollerud, E. J., Anderson, J., et al. 2019a, BAAS, 51, 540.  https://arxiv.org/abs/1904.01074
  * Gilbert et al. (2019b) Gilbert, K. M., Kirby, E. N., Escala, I., et al. 2019b, ApJ, 883, 128, doi: 10.3847/1538-4357/ab3807
  * Gilbert et al. (2014) Gilbert, K. M., Kalirai, J. S., Guhathakurta, P., et al. 2014, ApJ, 796, 76, doi: 10.1088/0004-637X/796/2/76
  * Gilbert et al. (2022) Gilbert, K. M., Quirk, A. C. N., Guhathakurta, P., et al. 2022, ApJ, 924, 116, doi: 10.3847/1538-4357/ac3480
  * Girardi et al. (2010) Girardi, L., Williams, B. F., Gilbert, K. M., et al. 2010, ApJ, 724, 1030, doi: 10.1088/0004-637X/724/2/1030
  * Gordon (2014) Gordon. 2014, Dust and Gas in the Magellanic Clouds from the HERITAGE Herschel Key Project. I. Dust Properties and Insights into the Origin of the Submillimeter Excess Emission
  * Gordon et al. (2003) Gordon, K. D., Clayton, G. C., Misselt, K. A., Landolt, A. U., & Wolff, M. J. 2003, ApJ, 594, 279, doi: 10.1086/376774
  * Gordon et al. (2016) Gordon, K. D., Fouesneau, M., Arab, H., et al. 2016, ApJ, 826, 104, doi: 10.3847/0004-637X/826/2/104
  * Gordon et al. (2021) Gordon, K. D., Misselt, K. A., Bouwman, J., et al. 2021, ApJ, 916, 33, doi: 10.3847/1538-4357/ac00b7
  * Gordon et al. (2022) Gordon, K. D., Bohlin, R., Sloan, G. C., et al. 2022, AJ, 163, 267, doi: 10.3847/1538-3881/ac66dc
  * Governato et al. (2010) Governato, F., Brook, C., Mayer, L., et al. 2010, Nature, 463, 203, doi: 10.1038/nature08640
  * Grebel & Gallagher (2004) Grebel, E. K., & Gallagher, III, J. S. 2004, ApJ, 610, L89, doi: 10.1086/423339
  * Green et al. (2019) Green, G. M., Schlafly, E., Zucker, C., Speagle, J. S., & Finkbeiner, D. 2019, ApJ, 887, 93, doi: 10.3847/1538-4357/ab5362
  * Guhathakurta et al. (1992) Guhathakurta, P., Yanny, B., Schneider, D. P., & Bahcall, J. N. 1992, AJ, 104, 1790, doi: 10.1086/116359
  * Gull et al. (2022) Gull, M., Weisz, D. R., Senchyna, P., et al. 2022, ApJ, 941, 206, doi: 10.3847/1538-4357/aca295
  * Harris & Zaritsky (2001) Harris, J., & Zaritsky, D. 2001, ApJS, 136, 25, doi: 10.1086/321792
  * Harris (2010) Harris, W. E. 2010, arXiv e-prints, arXiv:1012.3224.  https://arxiv.org/abs/1012.3224
  * Hayashi & Nakano (1963) Hayashi, C., & Nakano, T. 1963, Progress of Theoretical Physics, 30, 460, doi: 10.1143/PTP.30.460
  * Hidalgo et al. (2009) Hidalgo, S. L., Aparicio, A., Martínez-Delgado, D., & Gallart, C. 2009, ApJ, 705, 704, doi: 10.1088/0004-637X/705/1/704
  * Hidalgo et al. (2018) Hidalgo, S. L., Pietrinferni, A., Cassisi, S., et al. 2018, ApJ, 856, 125, doi: 10.3847/1538-4357/aab158
  * Hjort et al. (2016) Hjort, A., Zackrisson, E., & Eriksson, K. 2016, in 19th Cambridge Workshop on Cool Stars, Stellar Systems, and the Sun (CS19), Cambridge Workshop on Cool Stars, Stellar Systems, and the Sun, 40, doi: 10.5281/zenodo.164944
  * Holtzman et al. (2006) Holtzman, J. A., Afonso, C., & Dolphin, A. 2006, ApJS, 166, 534, doi: 10.1086/507074
  * Hunter et al. (1995) Hunter, D. A., Shaya, E. J., Holtzman, J. A., et al. 1995, ApJ, 448, 179, doi: 10.1086/175950
  * Ji et al. (2016) Ji, A. P., Frebel, A., Chiti, A., & Simon, J. D. 2016, Nature, 531, 610, doi: 10.1038/nature17425
  * Jones et al. (2017) Jones, O. C., Meixner, M., Justtanont, K., & Glasse, A. 2017, ApJ, 841, 15, doi: 10.3847/1538-4357/aa6bf6
  * Kalirai et al. (2012) Kalirai, J. S., Richer, H. B., Anderson, J., et al. 2012, AJ, 143, 11, doi: 10.1088/0004-6256/143/1/11
  * Kalirai et al. (2013) Kalirai, J. S., Anderson, J., Dotter, A., et al. 2013, ApJ, 763, 110, doi: 10.1088/0004-637X/763/2/110
  * Kallivayalil et al. (2013) Kallivayalil, N., van der Marel, R. P., Besla, G., Anderson, J., & Alcock, C. 2013, ApJ, 764, 161, doi: 10.1088/0004-637X/764/2/161
  * Kallivayalil et al. (2015) Kallivayalil, N., Wetzel, A. R., Simon, J. D., et al. 2015, ArXiv e-prints.  https://arxiv.org/abs/1503.01785
  * Kallivayalil et al. (2018) Kallivayalil, N., Sales, L. V., Zivick, P., et al. 2018, ApJ, 867, 19, doi: 10.3847/1538-4357/aadfee
  * Kirby et al. (2020) Kirby, E. N., Gilbert, K. M., Escala, I., et al. 2020, AJ, 159, 46, doi: 10.3847/1538-3881/ab5f0f
  * Kirby et al. (2011) Kirby, E. N., Lanfranchi, G. A., Simon, J. D., Cohen, J. G., & Guhathakurta, P. 2011, ApJ, 727, 78, doi: 10.1088/0004-637X/727/2/78
  * Koposov et al. (2015) Koposov, S. E., Belokurov, V., Torrealba, G., & Evans, N. W. 2015, ApJ, 805, 130, doi: 10.1088/0004-637X/805/2/130
  * Kroupa et al. (2013) Kroupa, P., Weidner, C., Pflamm-Altenburg, J., et al. 2013, The Stellar and Sub-Stellar Initial Mass Function of Simple and Composite Populations, ed. T. D. Oswalt & G. Gilmore, 115, doi: 10.1007/978-94-007-5612-0_4
  * Krumholz (2014) Krumholz, M. R. 2014, ArXiv e-prints.  https://arxiv.org/abs/1402.0867
  * Krumholz et al. (2019) Krumholz, M. R., McKee, C. F., & Bland-Hawthorn, J. 2019, ARA&A, 57, 227, doi: 10.1146/annurev-astro-091918-104430
  * Kumar (1963) Kumar, S. S. 1963, ApJ, 137, 1121, doi: 10.1086/147589
  * Laevens et al. (2014) Laevens, B. P. M., Martin, N. F., Sesar, B., et al. 2014, ApJ, 786, L3, doi: 10.1088/2041-8205/786/1/L3
  * Laevens et al. (2015a) Laevens, B. P. M., Martin, N. F., Ibata, R. A., et al. 2015a, ApJ, 802, L18, doi: 10.1088/2041-8205/802/2/L18
  * Laevens et al. (2015b) Laevens, B. P. M., Martin, N. F., Bernard, E. J., et al. 2015b, ApJ, 813, 44, doi: 10.1088/0004-637X/813/1/44
  * Leaman et al. (2009) Leaman, R., Cole, A. A., Venn, K. A., et al. 2009, ApJ, 699, 1, doi: 10.1088/0004-637X/699/1/1
  * Leaman et al. (2013) Leaman, R., Venn, K. A., Brooks, A. M., et al. 2013, ApJ, 767, 131, doi: 10.1088/0004-637X/767/2/131
  * Levesque (2018) Levesque, E. M. 2018, ApJ, 867, 155, doi: 10.3847/1538-4357/aae776
  * Li et al. (2017) Li, T. S., Simon, J. D., Drlica-Wagner, A., et al. 2017, ApJ, 838, 8, doi: 10.3847/1538-4357/aa6113
  * Longeard et al. (2018) Longeard, N., Martin, N., Starkenburg, E., et al. 2018, MNRAS, 1901, doi: 10.1093/mnras/sty1986
  * Lopez et al. (2011) Lopez, L. A., Krumholz, M. R., Bolatto, A. D., Prochaska, J. X., & Ramirez-Ruiz, E. 2011, ApJ, 731, 91, doi: 10.1088/0004-637X/731/2/91
  * Lopez et al. (2014) Lopez, L. A., Krumholz, M. R., Bolatto, A. D., et al. 2014, ApJ, 795, 121, doi: 10.1088/0004-637X/795/2/121
  * Madore et al. (2018) Madore, B. F., Freedman, W. L., Hatt, D., et al. 2018, ApJ, 858, 11, doi: 10.3847/1538-4357/aab7f4
  * Maraston (2006) Maraston, C.; Daddi, E. R. A. C. A. D. M. P. C. P. A. P. N. 2006, The Astrophysical Journal
  * Marigo et al. (2017) Marigo, P., Girardi, L., Bressan, A., et al. 2017, ApJ, 835, 77, doi: 10.3847/1538-4357/835/1/77
  * Marini et al. (2020) Marini, E., Dell’Agli, F., Di Criscienzo, M., et al. 2020, MNRAS, 493, 2996, doi: 10.1093/mnras/staa353
  * Martin et al. (2016) Martin, N. F., Geha, M., Ibata, R. A., et al. 2016, MNRAS, 458, L59, doi: 10.1093/mnrasl/slw013
  * Massey (2003) Massey, P. 2003, ARA&A, 41, 15, doi: 10.1146/annurev.astro.41.071601.170033
  * Mateo (1998) Mateo, M. L. 1998, ARA&A, 36, 435, doi: 10.1146/annurev.astro.36.1.435
  * McConnachie (2012) McConnachie, A. W. 2012, AJ, 144, 4, doi: 10.1088/0004-6256/144/1/4
  * McConnachie et al. (2018) McConnachie, A. W., Ibata, R., Martin, N., et al. 2018, ApJ, 868, 55, doi: 10.3847/1538-4357/aae8e7
  * McKee & Ostriker (2007) McKee, C. F., & Ostriker, E. C. 2007, ARA&A, 45, 565, doi: 10.1146/annurev.astro.45.051806.110602
  * McQuinn et al. (2019a) McQuinn, K. B. W., Boyer, M., Skillman, E. D., & Dolphin, A. E. 2019a, ApJ, 880, 63, doi: 10.3847/1538-4357/ab2627
  * McQuinn et al. (2017a) McQuinn, K. B. W., Skillman, E. D., Dolphin, A. E., Berg, D., & Kennicutt, R. 2017a, AJ, 154, 51, doi: 10.3847/1538-3881/aa7aad
  * McQuinn et al. (2015) McQuinn, K. B. W., Skillman, E. D., Dolphin, A. E., & Mitchell, N. P. 2015\.  https://arxiv.org/abs/1505.00791v1
  * McQuinn et al. (2018) McQuinn, K. B. W., Skillman, E. D., Heilman, T. N., Mitchell, N. P., & Kelley, T. 2018, MNRAS, 477, 3164, doi: 10.1093/mnras/sty839
  * McQuinn et al. (2019b) McQuinn, K. B. W., van Zee, L., & Skillman, E. D. 2019b, ApJ, 886, 74, doi: 10.3847/1538-4357/ab4c37
  * McQuinn et al. (2017b) McQuinn, K. B. W., Boyer, M. L., Mitchell, M. B., et al. 2017b, ApJ, 834, 78, doi: 10.3847/1538-4357/834/1/78
  * Meixner et al. (2010) Meixner, M., Galliano, F., Hony, S., et al. 2010, A&A, 518, L71, doi: 10.1051/0004-6361/201014662
  * Melbourne et al. (2012) Melbourne, J., Williams, B. F., Dalcanton, J. J., et al. 2012, ApJ, 748, 47, doi: 10.1088/0004-637X/748/1/47
  * Melotte (1926) Melotte, P. J. 1926, MNRAS, 86, 636, doi: 10.1093/mnras/86.8.636
  * Mészáros et al. (2020) Mészáros, S., Masseron, T., García-Hernández, D. A., et al. 2020, MNRAS, 492, 1641, doi: 10.1093/mnras/stz3496
  * Milone et al. (2012) Milone, A. P., Marino, A. F., Cassisi, S., et al. 2012, ApJ, 754, L34, doi: 10.1088/2041-8205/754/2/L34
  * Milone et al. (2014) Milone, A. P., Marino, A. F., Bedin, L. R., et al. 2014, MNRAS, 439, 1588, doi: 10.1093/mnras/stu030
  * Milone et al. (2017) —. 2017, MNRAS, 469, 800, doi: 10.1093/mnras/stx836
  * Monelli et al. (2010) Monelli, M., Hidalgo, S. L., Stetson, P. B., et al. 2010, ApJ, 720, 1225, doi: 10.1088/0004-637X/720/2/1225
  * Oey (1996) Oey, M. S. 1996, ApJ, 467, 666, doi: 10.1086/177642
  * Ostriker et al. (2010) Ostriker, E. C., McKee, C. F., & Leroy, A. K. 2010, ApJ, 721, 975, doi: 10.1088/0004-637X/721/2/975
  * Paresce et al. (1991) Paresce, F., Shara, M., Meylan, G., et al. 1991, Nature, 352, 297, doi: 10.1038/352297a0
  * Patel et al. (2020) Patel, E., Kallivayalil, N., Garavito-Camargo, N., et al. 2020, ApJ, 893, 121, doi: 10.3847/1538-4357/ab7b75
  * Peñarrubia et al. (2016) Peñarrubia, J., Ludlow, A. D., Chanamé, J., & Walker, M. G. 2016, MNRAS, 461, L72, doi: 10.1093/mnrasl/slw090
  * Pearson et al. (2022) Pearson, S., Clark, S. E., Demirjian, A. J., et al. 2022, ApJ, 926, 166, doi: 10.3847/1538-4357/ac4496
  * Pellegrini et al. (2011) Pellegrini, E. W., Baldwin, J. A., & Ferland, G. J. 2011, ApJ, 738, 34, doi: 10.1088/0004-637X/738/1/34
  * Perrin et al. (2014) Perrin, M. D., Sivaramakrishnan, A., Lajoie, C.-P., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9143, Space Telescopes and Instrumentation 2014: Optical, Infrared, and Millimeter Wave, ed. J. Oschmann, Jacobus M., M. Clampin, G. G. Fazio, & H. A. MacEwen, 91433X, doi: 10.1117/12.2056689
  * Piotto et al. (2015) Piotto, G., Milone, A. P., Bedin, L. R., et al. 2015, AJ, 149, 91, doi: 10.1088/0004-6256/149/3/91
  * Ricotti & Gnedin (2005) Ricotti, M., & Gnedin, N. Y. 2005, ApJ, 629, 259, doi: 10.1086/431415
  * Riess et al. (2011) Riess, A. G., Macri, L., Casertano, S., et al. 2011, ApJ, 730, 119, doi: 10.1088/0004-637X/730/2/119
  * Riess et al. (2016) Riess, A. G., Macri, L. M., Hoffmann, S. L., et al. 2016, ApJ, 826, 56, doi: 10.3847/0004-637X/826/1/56
  * Riess et al. (2021) Riess, A. G., Yuan, W., Macri, L. M., et al. 2021, arXiv e-prints, arXiv:2112.04510.  https://arxiv.org/abs/2112.04510
  * Rigby et al. (2022) Rigby, J., Perrin, M., McElwain, M., et al. 2022, arXiv e-prints, arXiv:2207.05632.  https://arxiv.org/abs/2207.05632
  * Rubio et al. (2015) Rubio, M., Elmegreen, B. G., Hunter, D. A., et al. 2015, Nature, 525, 218, doi: 10.1038/nature14901
  * Sacchi et al. (2021) Sacchi, E., Richstein, H., Kallivayalil, N., et al. 2021, ApJ, 920, L19, doi: 10.3847/2041-8213/ac2aa3
  * Salaris et al. (1993) Salaris, M., Chieffi, A., & Straniero, O. 1993, ApJ, 414, 580, doi: 10.1086/173105
  * Sana et al. (2012) Sana, H., de Mink, S. E., de Koter, A., et al. 2012, Science, 337, 444, doi: 10.1126/science.1223344
  * Sarajedini et al. (2009) Sarajedini, A., Dotter, A., & Kirkpatrick, A. 2009, ApJ, 698, 1872, doi: 10.1088/0004-637X/698/2/1872
  * Sarajedini et al. (2007) Sarajedini, A., Bedin, L. R., Chaboyer, B., et al. 2007, AJ, 133, 1658, doi: 10.1086/511979
  * Schechter et al. (1993) Schechter, P. L., Mateo, M., & Saha, A. 1993, PASP, 105, 1342, doi: 10.1086/133316
  * Schlafly et al. (2016) Schlafly, E. F., Meisner, A. M., Stutz, A. M., et al. 2016, ApJ, 821, 78, doi: 10.3847/0004-637X/821/2/78
  * Schlaufman & Casey (2014) Schlaufman, K. C., & Casey, A. R. 2014, ApJ, 797, 13, doi: 10.1088/0004-637X/797/1/13
  * Schlawin et al. (2020) Schlawin, E., Leisenring, J., Misselt, K., et al. 2020, AJ, 160, 231, doi: 10.3847/1538-3881/abb811
  * Simon (2019) Simon, J. D. 2019, arXiv e-prints, arXiv:1901.05465.  https://arxiv.org/abs/1901.05465
  * Simon & Geha (2007) Simon, J. D., & Geha, M. 2007, ApJ, 670, 313, doi: 10.1086/521816
  * Skillman et al. (2014) Skillman, E. D., Hidalgo, S. L., Weisz, D. R., et al. 2014, ApJ, 786, 44, doi: 10.1088/0004-637X/786/1/44
  * Skillman et al. (2017) Skillman, E. D., Monelli, M., Weisz, D. R., et al. 2017, ApJ, 837, 102, doi: 10.3847/1538-4357/aa60c5
  * Sohn et al. (2013) Sohn, S. T., Besla, G., van der Marel, R. P., et al. 2013, ApJ, 768, 139, doi: 10.1088/0004-637X/768/2/139
  * Sohn et al. (2020) Sohn, S. T., Patel, E., Fardal, M. A., et al. 2020, ApJ, 901, 43, doi: 10.3847/1538-4357/abaf49
  * Starkenburg et al. (2017a) Starkenburg, E., Oman, K. A., Navarro, J. F., et al. 2017a, MNRAS, 465, 2212, doi: 10.1093/mnras/stw2873
  * Starkenburg et al. (2017b) Starkenburg, E., Martin, N., Youakim, K., et al. 2017b, MNRAS, 471, 2587, doi: 10.1093/mnras/stx1068
  * Stetson (1987) Stetson, P. B. 1987, PASP, 99, 191, doi: 10.1086/131977
  * Stetson (1994) —. 1994, PASP, 106, 250, doi: 10.1086/133378
  * Stinson et al. (2006) Stinson, G., Seth, A., Katz, N., et al. 2006, MNRAS, 373, 1074, doi: 10.1111/j.1365-2966.2006.11097.x
  * Stinson et al. (2007) Stinson, G. S., Dalcanton, J. J., Quinn, T., Kaufmann, T., & Wadsley, J. 2007, ApJ, 667, 170, doi: 10.1086/520504
  * Tody (1980) Tody, D. 1980, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 264, Conference on Applications of Digital Image Processing to Astronomy, ed. D. A. Elliott, 171–179, doi: 10.1117/12.959800
  * Tolstoy (1996) Tolstoy, E. 1996, ApJ, 462, 684, doi: 10.1086/177182
  * Tolstoy et al. (2009) Tolstoy, E., Hill, V., & Tosi, M. 2009, ARA&A, 47, 371, doi: 10.1146/annurev-astro-082708-101650
  * Tosi et al. (1989) Tosi, M., Greggio, L., & Focardi, P. 1989, Ap&SS, 156, 295, doi: 10.1007/BF00646381
  * Tully et al. (2019) Tully, R. B., Pomarède, D., Graziani, R., et al. 2019, ApJ, 880, 24, doi: 10.3847/1538-4357/ab2597
  * Utomo et al. (2019) Utomo, D., Chiang, I.-D., Leroy, A. K., Sandstrom, K. M., & Chastenet, J. 2019, ApJ, 874, 141, doi: 10.3847/1538-4357/ab05d3
  * van der Marel et al. (2012) van der Marel, R. P., Fardal, M., Besla, G., et al. 2012, ApJ, 753, 8, doi: 10.1088/0004-637X/753/1/8
  * VandenBerg et al. (2012) VandenBerg, D. A., Bergbusch, P. A., Dotter, A., et al. 2012, ApJ, 755, 15, doi: 10.1088/0004-637X/755/1/15
  * VandenBerg et al. (2022) VandenBerg, D. A., Edvardsson, B., Casagrande, L., & Ferguson, J. W. 2022, MNRAS, 509, 4189, doi: 10.1093/mnras/stab2996
  * Vargas et al. (2013) Vargas, L. C., Geha, M., Kirby, E. N., & Simon, J. D. 2013, ApJ, 767, 134, doi: 10.1088/0004-637X/767/2/134
  * Vargas et al. (2014) Vargas, L. C., Geha, M. C., & Tollerud, E. J. 2014, ApJ, 790, 73, doi: 10.1088/0004-637X/790/1/73
  * Venn et al. (2004) Venn, K. A., Irwin, M., Shetrone, M. D., et al. 2004, AJ, 128, 1177, doi: 10.1086/422734
  * Ventura (2001) Ventura, Paolo; D’Antona, F. M. I. G. R. 2001, The Astrophysical Journal
  * Warfield et al. (2023) Warfield, J. T., Kallivayalil, N., Zivick, P., et al. 2023, MNRAS, 519, 1189, doi: 10.1093/mnras/stac3647
  * Weisz & Boylan-Kolchin (2019) Weisz, D., & Boylan-Kolchin, M. 2019, BAAS, 51, 1.  https://arxiv.org/abs/1901.07571
  * Weisz et al. (2014a) Weisz, D. R., Dolphin, A. E., Skillman, E. D., et al. 2014a, ApJ, 789, 147, doi: 10.1088/0004-637X/789/2/147
  * Weisz et al. (2014b) —. 2014b, ApJ, 789, 148, doi: 10.1088/0004-637X/789/2/148
  * Wetzel et al. (2016) Wetzel, A. R., Hopkins, P. F., Kim, J.-h., et al. 2016, ApJ, 827, L23, doi: 10.3847/2041-8205/827/2/L23
  * Williams et al. (2014) Williams, B. F., Lang, D., Dalcanton, J. J., et al. 2014, ApJS, 215, 9, doi: 10.1088/0067-0049/215/1/9
  * Williams et al. (2021) Williams, B. F., Durbin, M. J., Dalcanton, J. J., et al. 2021, ApJS, 253, 53, doi: 10.3847/1538-4365/abdf4e
  * Willman & Strader (2012) Willman, B., & Strader, J. 2012, AJ, 144, 76, doi: 10.1088/0004-6256/144/3/76
  * Wolf (1909) Wolf, M. 1909, Astronomische Nachrichten, 183, 187, doi: 10.1002/asna.19091831204
  * Yanchulova Merica-Jones et al. (2017) Yanchulova Merica-Jones, P., Sandstrom, K. M., Johnson, L. C., et al. 2017, ApJ, 847, 102, doi: 10.3847/1538-4357/aa8a67
  * Yanchulova Merica-Jones et al. (2021) —. 2021, ApJ, 907, 50, doi: 10.3847/1538-4357/abc48b
  * Zivick et al. (2018) Zivick, P., Kallivayalil, N., van der Marel, R. P., et al. 2018, ApJ, 864, 55, doi: 10.3847/1538-4357/aad4b0