diff --git "a/-NE3T4oBgHgl3EQfrQru/content/tmp_files/2301.04659v1.pdf.txt" "b/-NE3T4oBgHgl3EQfrQru/content/tmp_files/2301.04659v1.pdf.txt" new file mode 100644--- /dev/null +++ "b/-NE3T4oBgHgl3EQfrQru/content/tmp_files/2301.04659v1.pdf.txt" @@ -0,0 +1,2242 @@ +DRAFT VERSION JANUARY 13, 2023 +Typeset using LATEX twocolumn style in AASTeX631 +The JWST Resolved Stellar Populations Early Release Science Program II. +Survey Overview +DANIEL R. WEISZ,1 KRISTEN B. W. MCQUINN,2 ALESSANDRO SAVINO,1 NITYA KALLIVAYALIL,3 JAY ANDERSON,4 +MARTHA L. BOYER,4 MATTEO CORRENTI,5, 6 MARLA C. GEHA,7 ANDREW E. DOLPHIN,8, 9 KARIN M. SANDSTROM,10 +ANDREW A. COLE,11 BENJAMIN F. WILLIAMS,12 EVAN D. SKILLMAN,13 ROGER E. COHEN,2 MAX J. B. NEWMAN,2 +RACHAEL BEATON,4, 14, 15 ALESSANDRO BRESSAN,16 ALBERTO BOLATTO,17, 18 MICHAEL BOYLAN-KOLCHIN,19 +ALYSON M. BROOKS,2, 20 JAMES S. BULLOCK,21 CHARLIE CONROY,22 M. C. COOPER,21 JULIANNE J. DALCANTON,12, 20 +AARON L. DOTTER,23 TOBIAS K. FRITZ,3 CHRISTOPHER T. GARLING,3 MARIO GENNARO,24, 25 KAROLINE M. GILBERT,24, 25 +L´EO GIRARDI,26 BENJAMIN D. JOHNSON,22 L. CLIFTON JOHNSON,27 JASON S. KALIRAI,28 EVAN N. KIRBY,29 DUSTIN LANG,30 +PAOLA MARIGO,31 HANNAH RICHSTEIN,3 EDWARD F. SCHLAFLY,24 JUDY SCHMIDT,32 ERIK J. TOLLERUD,4 JACK T. WARFIELD,3 +AND ANDREW WETZEL33 +1Department of Astronomy, University of California, Berkeley, CA 94720, USA +2Department of Physics and Astronomy, Rutgers, the State University of New Jersey, 136 Frelinghuysen Road, Piscataway, NJ 08854, USA +3Department of Astronomy, University of Virginia, 530 McCormick Road, Charlottesville, VA 22904, USA +4Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA +5INAF Osservatorio Astronomico di Roma, Via Frascati 33, 00078, Monteporzio Catone, Rome, Italy +6ASI-Space Science Data Center, Via del Politecnico, I-00133, Rome, Italy +7Department of Astronomy, Yale University, New Haven, CT 06520, USA +8Raytheon Technologies, 1151 E. Hermans Road, Tucson, AZ 85756, USA +9Steward Observatory, University of Arizona, 933 N. Cherry Avenue, Tucson, AZ 85719, USA +10Center for Astrophysics and Space Sciences, Department of Physics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA +11School of Natural Sciences, University of Tasmania, Private Bag 37, Hobart, Tasmania 7001, Australia +12Department of Astronomy, University of Washington, Box 351580, U.W., Seattle, WA 98195-1580, USA +13University of Minnesota, Minnesota Institute for Astrophysics, School of Physics and Astronomy, 116 Church Street, S.E., Minneapolis, MN 55455, USA +14Department of Astrophysical Sciences, Princeton University, 4 Ivy Lane, Princeton, NJ 08544, USA +15The Observatories of the Carnegie Institution for Science, 813 Santa Barbara St., Pasadena, CA 91101, USA +16SISSA, Via Bonomea 265, 34136 Trieste, Italy +17Department of Astronomy, University of Maryland, College Park, MD 20742, USA +18Joint Space-Science Institute, University of Maryland, College Park, MD 20742, USA +19Department of Astronomy, The University of Texas at Austin, 2515 Speedway, Stop C1400, Austin, TX 78712-1205, USA +20Center for Computational Astrophysics, Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA +21Department of Physics and Astronomy, University of California, Irvine, CA 92697 USA +22Center for Astrophysics — Harvard & Smithsonian, Cambridge, MA, 02138, USA +23Department of Physics and Astronomy, Dartmouth College, 6127 Wilder Laboratory, Hanover, NH 03755, USA +24Space Telescope Science Institute, 3700 San Martin Dr., Baltimore, MD 21218, USA +25The 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 +26Padova Astronomical Observatory, Vicolo dell’Osservatorio 5, Padova, Italy +27Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) and Department of Physics and Astronomy, Northwestern University, 1800 +Sherman Avenue, Evanston, IL 60201, USA +28John Hopkins Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, USA +29Department of Physics, University of Notre Dame, Notre Dame, IN 46556, USA +30Perimeter Institute for Theoretical Physics, Waterloo, ON N2L 2Y5, Canada +31Department of Physics and Astronomy G. Galilei, University of Padova, Vicolo dell’Osservatorio 3, I-35122, Padova, Italy +322817 Rudge Pl, Modesto, CA 95355, USA +33Department of Physics and Astronomy, University of California, Davis, CA 95616, USA +ABSTRACT +Corresponding author: Daniel R. Weisz +dan.weisz@berkeley.edu +arXiv:2301.04659v1 [astro-ph.GA] 11 Jan 2023 + +2 +WEISZ ET AL. +We present the JWST Resolved Stellar Populations Early Release Science (ERS) science program. We ob- +tained 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 ∼ 105 +in luminosity, ∼ 104 in distance, and ∼ 105 in surface brightness. We describe the survey strategy, scientific and +technical goals, implementation details, present select NIRCam color-magnitude diagrams (CMDs), and vali- +date 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⊙; SNR ∼ 5 at mF 090W ∼ 28.2; +MF 090W ∼ +13.6), include the lowest-mass stars observed outside the Milky Way in Draco II (0.09 M⊙; +SNR = 10 at mF 090W ∼ 29; MF 090W ∼ +12.1), and reach ∼ 1.5 magnitudes below the oldest main se- +quence turnoff in WLM (SNR = 10 at mF 090W ∼ 29.5; MF 090W ∼ +4.6). The PARSEC stellar models +provide a good qualitative match to the NIRCam CMDs, though are ∼ 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. +Keywords: Stellar photometry (1620), Local Group (929), Stellar populations (1622), Hertzsprung Russell dia- +gram (725), JWST (2291) +1. INTRODUCTION +The resolved stellar populations of nearby galaxies are +central to a wide range of astrophysics. The observed col- +ors, luminosities, and spectral features of resolved stars in +galaxies within the Local Volume (LV) anchor our knowl- +edge of star formation (e.g., star cluster formation, the ini- +tial 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 sur- +roundings; e.g., Oey 1996; Dohm-Palmer et al. 1998; Stin- +son 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; Dal- +canton 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; Var- +gas 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; Ekstr¨om 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, cos- +mic reionization, the first stars, near-field cosmology, and +the nature of dark matter on the smallest scales (e.g., Ma- +teo 1998; Tolstoy et al. 2009; Brown et al. 2014; Weisz et al. +2014a; Boylan-Kolchin et al. 2015; Gallart et al. 2015; Mc- +Quinn 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 ∼ 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 cover- +age have transformed our knowledge of the Universe by ob- +serving 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; Dal- +canton 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 astrophys- +ical insights in nearby galaxies, it has only scratched the sur- +face of science enabled by infrared (IR) observations. Com- +pared to the UV and optical, HST’s IR camera has coarser an- +gular 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 popula- +tions in the IR. Compared to any other facility, JWST will re- + +JWST RESOLVED STELLAR POPULATIONS I +3 +solve individual stars at larger distances, to fainter luminosi- +ties, 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 histo- +ries (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 imme- +diate 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 sev- +eral 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), pro- +vide high fidelity distances to galaxies throughout the Lo- +cal 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 under- +taken 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 test- +ing 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 cur- +rent JWST exposure time calculator (ETC). Papers in prepa- +ration 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 tar- +get 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 ap- +plication of DOLPHOT in §5. In §6 we present NIRCam +color-magnitude diagrams (CMDs) and compare them to se- +lect 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 par- +allel. 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., Dal- +canton 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, re- +solved stellar population studies are technically daunting, +requiring highly optimized observations and sophisticated +analysis tools that have been developed and refined over the +past ∼ 40 years (e.g., Buonanno et al. 1979; Tody 1980; Stet- +son 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 sum- +marize 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. Star Formation Histories. A galaxy’s resolved stel- +lar content encodes its star formation history (SFH), +which can be reconstructed by fitting CMDs with stel- +lar 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 (MV ∼ +4) has limited current +‘gold standard’ SFHs to galaxies within the LG. How- +ever, the relatively low effective temperatures of these +stars, combined with the decreased sky background in +the near-IR and JWST’s excellent sensitivity and angu- +lar 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 ques- +tions (e.g., the effects of reionization and/or environ- +ment 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. + +4 +WEISZ ET AL. +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; Gen- +naro et al. 2018b). However, even with HST, it has +proven challenging to acquire sufficiently deep data +(down to ∼ 0.2 M⊙ El-Badry et al. 2017; Gennaro +et al. 2018b,a) to unambiguously confirm these puta- +tive 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. 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 ob- +jects. 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 ∼ 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. Evolved Stars. Cool evolved stars such as red super- +giants and asympotic giant branch (AGB) stars are re- +sponsible for 20–70% of the rest-frame near-IR lumi- +nosity 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., Maras- +ton 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 pro- +gram will demonstrate JWST’s capacity to study IR- +bright, evolved stars. +5. Extinction Mapping. +In the LG, Spitzer and Her- +schel have mapped dust emission at ∼ 10 − 40′′ and +∼ 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. Ages of Globular Clusters. Accurate ages of the oldest +GCs are particularly important for connecting the stel- +lar fossil record to events in the early Universe includ- +ing cosmic reionization and the age of the Universe it- +self (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 ∼ 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 refer- +ences therein). JWST observations of the ‘kink’ on the +lower main sequence (MS) can yield more precise esti- +mates 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 ex- +tragalactic 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., Pe˜narrubia 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 develop- +ment of NIRCam and NIRISS modules for DOLPHOT, test- +ing 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 de- +scribe 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 de- +scribed in Dolphin (2000), but certain aspects have been up- +dated 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 photomet- +ric calibrations provided in the image metadata, conversions + +JWST RESOLVED STELLAR POPULATIONS I +5 +to VEGAMAG, and camera-specific PSF models with corre- +sponding 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. 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. 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 gi- +ants and extremely faint dwarfs. This enables a variety +of tests, including the ability to recover faint sources +next to very bright objects. +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. 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 pro- +vides significantly sharper CMDs compared to reduc- +ing IR data alone (e.g., Williams et al. 2014). Similar +gains should be possible with NIRCam. +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., Dalcan- +ton et al. 2012b; Williams et al. 2014, 2021), but it +needs to be vetted and optimized for cross-facility use. +2.3. Deliverables +Our program is in the process of providing several “deliv- +erables” to the community that can be found on our team +website1. +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 +website2. This software enables crowded field stellar pho- +tometry 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 cali- +bration and testing, we will release high level science prod- +ucts including the output of our team DOLPHOT runs on +1 https://ers-stars.github.io +2 http://americano.dolphinsim.com/dolphot/ +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 un- +derstanding 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. +3. STRATEGY +3.1. Filters +The diversity of our science cases required careful consid- +eration of filter selection. Several of our science goals are +centered around maximizing depth, color baseline, and astro- +metric precision. Accordingly, we primarily focused on SW +filter selection, which has better sensitivity (for most stars) +and angular resolving power than the long-wavelength chan- +nel. +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) per- +mutations at three different CMD locations: the blue HB +(Teff ∼ 7000 K), the MSTO (Teff ∼ 6000 K), and the lower +MS (∼ 0.2 M⊙; Teff ∼ 4000 K). At each point, we used the +pre-commissioning JWST ETC (v1.1.1) to compute the ex- +posure 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 compa- +rable 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⊙ star as either F115W or F150W. Never- +theless, 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 use- +ful 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 in- +tegration time for a 0.2 M⊙ 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 combi- +nation 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 acquir- +ing 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. + +6 +WEISZ ET AL. +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 (rh), (b) 1 and 2 rh and (c) 1 rh 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. +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 ∼ 1 Mpc (Mc- +Connachie 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 ex- +perience 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 imag- +ing (e.g., to enable combined HST and JWST proper mo- +tion 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 spec- +tra 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 nomi- +nal 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 +Table 1. Basic observational properties of the three ERS targets. +Properties for M92 have been taken from the updated MW GC cat- +alog of Harris (2010), while those of Draco II and WLM are from +the updated LG galaxy catalog of McConnachie (2012). Note that +µ0 is the effective surface brightness and rh 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 +MV (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 +µ0 (mag arcsec−2) +15.5 +28.1 +24.8 +rh (′) +1.0 +2.7 +7.8 +listed in Table 1. We detail the observational strategy for each +target in the following sections. +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 stel- +lar 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 sev- +eral science and technical goals including GC ages, individ- +ual star proper motions, the present day mass function, test- +ing DOLPHOT over a large dynamic range of stellar bright- +ness and spatially varying stellar density, and gauging the ef- +fects of bright saturated stars on the photometric process. + +(a) M92 +(b) Draco II +(c) WLM +HST/AWFPC2 +HST/ACS +HST/ACS +UWST +HST/ACS +JWST +JWST +HST/WFC3 IR +HST/WFC3UVIS +'NIRCam & NIRISS +HSTWFC3 UVIS +NIRCam & NIRISS +HST/WFC3IR +NIRCam & NIRISSJWST RESOLVED STELLAR POPULATIONS I +7 +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. + +(a) M92 +(b) Draco il +(c) WLM8 +WEISZ ET AL. +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 ∼ 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⊙ in F090W and +F150W, which we estimate to be mF 090W ≈ 26 (MF 090W ≈ ++11.4) and mF 150W ≈ 25.8 (MF 150W ≈ +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 mF 090W ≈ 26 were +21.1 in F277W and 6.4 in F444W. We estimated NIRISS +imaging to be marginally shallower than NIRCam with in- +tegration times of 1074s in each of F090W and F150W. The +difference in duration is set by facility overheads as calcu- +lated by APT. +3.4. Draco II +At ∼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; Mar- +tin 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 +(≲ 0.2 M⊙). 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), de- +termining 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 orienta- +tion. 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 galax- +ies) 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 metal- +licities 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 ≲ 0.2 M⊙ (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∼ 10 at mF 090W = 27 (MF 090W = ++10.3) and mF 150W = 26.8 (MF 150W = +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 ∼ 7 (F360M) and ∼ 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 +∼ 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, mea- +sure 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 com- +bined UV-optical-IR HST and JWST stellar SEDs (e.g., Dal- +canton 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 mea- +suring stellar photometry across facilities (HST and JWST) +and instruments (WFPC2, ACS, UVIS, NIRCam). +We required that one module of the NIRCam observa- +tions 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 + +JWST RESOLVED STELLAR POPULATIONS I +9 +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 cover- +age 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 dis- +tant dwarf galaxies requires a CMD that reaches a SNR∼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 MF 090W += +3.4 and +MF 150W = +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 +mF 090W = 28.3 and mF 150W = 28.1 with SNR=10 in each +filter. +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 tech- +nical 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 ac- +counting. +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” feature3 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 NIR- +Cam and NIRISS footprints for each of our targets overlaid +on ground-based images and Table 1 lists our observational +3 urlhttps://jwst-docs.stsci.edu/jwst-near-infrared-camera/nircam-features- +and-caveats/nircam-claws-and-wisps +configurations. Full details on implementation can be viewed +by retrieving proposal 1334 in the Astronomer’s Proposal +Tool (APT)4. +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 fil- +ters 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 pro- +gram 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 NIR- +Cam and the NIS readout pattern for NIRISS. The orienta- +tion 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 allow- +ing 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 over- +heads, the total time charged for observing M92 was 7037s. +The ratio of science to charged time was ∼ 0.35 (counting the +primary NIRCam imaging only) or ∼ 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 NIR- +Cam 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 ∼ 0.71 (counting the primary NIRCam imaging only) or +∼ 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 NIR- +Cam 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 mem- +4 apt.stsci.edu + +10 +WEISZ ET AL. +Table 2. A summary of our JWST ERS observations taken in 2022. +Target +Date +Camera +Filter +texp [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 +bers 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 ∼ 0.8 (counting the primary NIRCam imaging only) or +∼ 1.50 (for both NIRCam and NIRISS imaging). The large +data volume (19.962 GB) for WLM generated a “Data Ex- +cess 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 sci- +ence 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 observa- +tion (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 im- +ages to generate light curves requires running the reduction +pipeline locally and creating custom time series analysis soft- +ware, 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, indi- +cating a fairly high ratio of science-to-charged time. +5. PHOTOMETRY +We perform photometric reductions of our observations us- +ing 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 WebbPSF5 +NIRISS PSF models appear to concentrate light significantly +more than what we have observed in the ERS images. Con- +sequently, our NIRISS photometry is not yet reliable and fur- +ther 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 version- +ing 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 reduc- +tions 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 forthcom- +ing photometry paper. In the meantime, without Frame 0 +data, our photometry saturates at fainter magnitudes that we +5 https://webbpsf.readthedocs.io/en/latest/ + +JWST RESOLVED STELLAR POPULATIONS I +11 +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∼ 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 ∼ 21 is the MS kink. Panel (b): The CMD of Draco II includes the MS kink at F090W ∼ 23 and reaches the bottom +of the stellar sequence provided by the standard PARSEC models (0.09 M⊙). 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 MF 090W = +4.6) is the deepest taken for a galaxy outside the immediate vicinity of the MW. +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 De- +lay 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 vari- +ation in the optical performance of the telescope, justifying +our choice of a common PSF library. We are currently work- +ing in quantifying the full effect of JWST time-dependent +PSF variations on DOLPHOT photometry. +The final products of the DOLPHOT photometric run are +catalogs with positions, VEGAmag magnitudes (calibrated +Table 3. Quality-metric criteria used to cull our DOLPHOT photo- +metric catalogs. +Band +SNR +Sharp2 +Crowd +Flag +Object Type +F090W +≥ 4 +≤ 0.01 +≤ 0.5 +≤ 2 +≤ 1 +F150W +≥ 4 +≤ 0.01 +≤ 0.5 +≤ 2 +≤ 1 +using the latests NIRCam zero-points6), uncertainties based +on photon noise, and a set of quality metrics related to the +goodness of point-source photometry (e.g., SNR, χ2 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 +6 https://jwst-docs.stsci.edu/jwst-near-infrared-camera/ +nircam-performance/nircam-absolute-flux-calibration-and-zeropoints + +(a) M92 +(b) Draco Il +(c) WLM +18 +20 +SNR=500 +22 +SNR=500 +F090W +100 +24 +SNR=500 +50 +100 +26 +100 +10 +50 +28 +10 +30 +0.0 +0.5 +1.0 +1.5 +0.0 +0.5 +1.0 +1.5 +-0.5 +0.0 +0.5 +1.0 +1.5 +F090W-F150W +F090W-F150W +F090W-F150W12 +WEISZ ET AL. +caveats associated with this approximation, especially when +measuring stars in very crowded fields, or close to the limit- +ing magnitude. We will provide a more thorough discussion +on SNRs estimation in our upcoming JWST DOLPHOT pho- +tometry paper. +The catalogs provided by DOLPHOT are subsequently in- +spected 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 pu- +rity. 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 com- +mon 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 in- +vestigated by members of our team. +Full characterization of uncertainties in resolved stel- +lar 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 in- +cluded 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. +6. DISCUSSION +6.1. Color-Magnitude Diagrams +Figure 3 shows the NIRCam SW CMDs for all three tar- +gets 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 rang- +ing 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 ∼ 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 selec- +tion 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 metal- +licities and ages needed to characterize our datasets. +We +have adjusted these isochrones to the distances and redden- +ings 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 recom- +mended 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 ∼ 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 ∼ 21 and F150W ∼ 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⊙) available from the standard PARSEC +stellar library in the SW filters and to slightly higher masses +(0.12 M⊙) 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⊙ and find that that the minimum mass +for hydrogen burning is 0.078 ≤ M < 0.08 M⊙ for a metal- +licity of [Fe/H]= −1.7 dex and an age of 13 Gyr. This trans- +lates to a magnitude range of 28.2 < mF 090W ≤ 29.5 mag +in M92. This depth is quite remarkable considering our ob- +servations only consistent of ∼ 1050s in each filter. In com- +parison, the faintest stars in the HST WFC3/IR CMD (panel +e) are a few magnitudes brighter despite ∼ 1200s of inte- +gration 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 nom- +inally higher than the metallicity of M92 ([Fe/H] = +−2.23 dex) derived from high-resolution APOGEE spec- +troscopy (M´esz´aros et al. 2020). This discrepancy is because +the current version of the PARSEC models are Solar-scaled, +whereas M92 is highly α-enhanced with [α/Fe] ∼ 0.5 dex. +Well-established corrections can be applied to match Solar- +scaled models with α-enhanced populations (e.g., Salaris +et al. 1993). For M92, the corrective factor for the PAR- +SEC models results in values of [Fe/H] +∼ 0.5 dex higher +than derived from spectroscopy. PARSEC models with α- + +JWST RESOLVED STELLAR POPULATIONS I +13 +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 α-enhancement of M92 (M´esz´aros et al. 2020). The SW NIRCam CMD extends from our saturation +limit (F090W +∼ 19) to below the lowest-mass star from the standard PARSEC models (M = 0.09 M⊙) and into the expected hydrogen +burning limit regime (0.078 ≤ M < 0.08 M⊙). The LW CMDs reach slightly higher limits (M = 0.12 M⊙). 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 ∼ 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. +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 ∼ 0.05 mag. The source of +this offset is not due to distance, reddening, age, or metal- +licity 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 chem- +ically distinct populations. +Like many luminous Galactic +GCs, M92 exhibits multiple populations with distinct abun- +dance patterns (e.g., M´esz´aros et al. 2020). Other GCs with +deep CMDs and similar abundance patterns show a broad- +ening 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 varia- +tions expressed via water lines (e.g., Dotter et al. 2015; Van- +denBerg et al. 2022). Finally, we note that some of the scatter + +18 +[Fe/H]=-2.0 +18 +(b) +(a) +(c) +[Fe/H]=-1.7 +18 +20 +[Fe/H]=-1.4 +20 +22 +F090W +L50W +20 +.50W +24 +22 +22 +26 +24 +24 +28 +26 +26 +0.5 +1.0 +1.5 +0.00 +0.25 +0.50 +0.0 +0.5 +F090W-F150W +F150W-F277W +F150W-F444W +10 +(d) +(e) HST WFC3/IR +(f) HST/ACS +18 +10 +15 +277W +20 +F160W +814W +15 +20 +20 +24 +25 +26 +25 +-0.25 +0.00 +0.25 +0.50 +0.00 +0.25 +0.50 +0 +1 +2 +3 +F277W-F444W +F110W-F160W +F475W-F814W14 +WEISZ ET AL. +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⊙), 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. +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 +α-enhanced stellar population (e.g., Longeard et al. 2018; Si- +mon 2019). The selected isochrones provide a good fit of the + +(a) +[Fe/H]=-2.0 +(b) HST/ACS +18 +18 +[Fe/H]=-1.7 +[Fe/H]=-1.4 +20 +20 +F090W +22 +F814W +22 +24 +24 +26 +26 +28 +28 +30 +30 +0 +1 +2 +0 +1 +2 +F090W-F150W +F606W-F814W +(c) +(d) +18 +18 +20 +20 +50W +22 +60M +22 +5 +24 +3 +出 +26 +24 +28 +26 +30 +0 +1 +2 +-0.5 +0.0 +0.5 +F150W-F360M +F360M-F480MJWST RESOLVED STELLAR POPULATIONS I +15 +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 ∼ 1.5 mag below the oldest MSTO; it is deeper than the HST CMD +despite ∼ 7000s less integration time. The isochrones indicate the variety of stellar ages present in WLM. The LW CMD shown in panel (d) +extends ∼ 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. +MSTO in the HST CMD (panel b) suggesting the adopted pa- +rameters (i.e., age, metallicity, distance, extinction) are rea- +sonable, 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⊙). +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⊙, whereas our data extend to 0.09 M⊙. The large dy- +namic range of stellar masses in Draco II provides excellent +leverage for a low-mass IMF measurement. Tight constraints + +20 +20 +(a) +(b) +HST/ +22 +ACS +22 +F090W +.4W +24 +24 +13 Gyr +5 Gyr +0.5 Gyr +1 +26 +26 +0.05 Gyr +8 +F +28 +28 +30 +30 +0.0 +1.0 +2.2 +-1 +0 +1 +2 +F090W-F150W +F475W-F814W +20 +18 +(c) +(d) +22 +20 +F090W +24 +F250M +22 +26 +24 +28 +26 +30 - +-1 +0 +1 +2 +-0.25 +0.00 +0.25 +0.50 +F090W-F250M +F250M-F430M16 +WEISZ ET AL. +on the IMF in Draco II could provide a new means of distin- +guishing whether faint stellar systems are dark-matter domi- +nated 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 σ ∼ 0.5 dex (e.g., Li et al. +2017; Longeard et al. 2018; Fu et al. 2022), which may con- +tribute to this scatter. Background galaxies are also a source +of contamination and likely contribute to the scatter, partic- +ularly at the faintest magnitudes. The combination of very +deep imaging and the sparsity of Draco II’s stellar popula- +tion mean that background galaxies are a large source of con- +tamination. 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 compara- +bly low stellar mass as the SW CMD, albeit at lower SNR. +The LW CMD (panel d) is much shallower; though the pri- +mary 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 quali- +tative 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 com- +pared 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 satu- +ration. +The CMDs of WLM exhibit a wide variety of stellar se- +quences 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., Dal- +canton et al. 2012b; Williams et al. 2014; Gull et al. 2022). +For example, the HB slopes to fainter values at bluer wave- +lengths as hot blue HB stars are less luminous at IR wave- +lengths. Similarly, red stars (e.g., RGB, AGB) become more +luminous as the IR wavelengths are closer to their peak tem- +peratures compared to optical wavelengths. +Figure 6 shows that our NIRCam SW CMD of WLM is at +∼ 1 mag deeper than the HST/ACS CMD despite similar in- +tegration times (∼ 54200s for NIRCam versus ∼ 61400s for +ACS). This increase owes primarily to the increased sensitiv- +ity of JWST at these wavelengths. McQuinn et al. (in prep.) +is deriving the SFHs from both datasets to quantify the capa- +bilites of JWST imaging for detailed SFH determinations. +More broadly, our CMDs of WLM are the deepest in ex- +istence 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 ex- +tend to less luminous stars on the MS. +Though not as deep as the SW data, the LW is remark- +ably 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 metallic- +ity of [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 ac- +curacy of the NIRCam ETC in practice. +In Figure 7, we consider the SNRs for stars in M92 as re- +ported by DOLPHOT against what is reported by the NIR- +Cam ETC. M92 is the best of our three targets for this ex- +ploratory 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. +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 ≤ 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., back- +ground galaxies, diffraction spike artifacts) from our analy- +sis. Finally, we exclude the brightest stars as they may be af- +fected 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 distri- +butions 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. + +JWST RESOLVED STELLAR POPULATIONS I +17 +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 (∼ 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. +For the ETC scene, we used a K5V star (Teff = 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 ∼ 5% +level. For simplicity, we adopted a single stellar atmosphere +model for this calculation. +We adopted an extinction of AV = 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 obser- +vations. We computed the SNR in the F090W and F150Ws +filter in 0.5 mag steps from 19 to 30 mag in each filter, renor- +malizing after extinction was applied. +The result is a smooth variation in SNR as a function of +magnitude. We interpolated the results onto a finer magni- +tude grid for clearer comparison with the DOLPHOT results. +Interpolation errors are < 1%. +To compute the SNR, we used the default aperture pho- +tometry setup in the NIRCam ETC. Specifically, this uses +an aperture radius of 0.1′′ and performs background subtrac- +tion 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× the PSF FWHM specified in JDOX7 as rec- +ommended 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 ∼ 20%, which is +within the bounds of our uncertainty range. The small struc- +tures in the residuals over this range are due to finite numbers +of real stars in each bin. There are some noticeable devia- +tions from unity at bright magnitudes for both F090W and +F150W. We believe that these may be due to saturation ef- +fects 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 +7 https://jwst-docs.stsci.edu/jwst-near-infrared-camera/ +nircam-performance/nircam-point-spread-functions + +F090W ETC +F150W ETC +DOLPHOT +DOLPHOT +800 +800 +600 +600 +NR +S400 +400 +200 +200 +0 +DOLPHOT / ETC +Z +2 +1 +1 +0 +0 +20 +22 +24 +26 +28 +18 +20 +22 +24 +26 +F090W +F150W18 +WEISZ ET AL. +overly concerning as small variations in the PSF shape (Dol- +phin 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 cal- +culation 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 ETC8. 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 explo- +ration 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 motiva- +tion, planning, implementation, execution, and present NIR- +Cam 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 NIR- +Cam and NIRISS modules for DOLPHOT, a widely +used stellar crowded field photometry package. We +used our ERS targets to test these modules for a va- +riety of image properties (e.g., various filter combina- +tions, 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, +8 https://etc.stsci.edu/etcstatic/users guide/1 3 imaging.html +along with theoretical PSF models for all NIRCam and +NIRISS filters are publicly available on our team web- +site 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 deep- +est 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⊙). +The F090W−F150W CMD of Draco II reaches the the +bottom of the stellar sequence (0.09 M⊙) in the stan- +dard PARSEC models. The F090W−F150W CMD of +WLM extends ∼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 ∼ +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 in- +cludes the known sensitivity of color to oxygen abun- +dance (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 ∼ 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 selec- +tion effects and/or subtle mismatches between theoret- +ical and observed PSFs at the faint end. We caution +that this preliminary comparison does not capture ef- +fects 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. + +JWST RESOLVED STELLAR POPULATIONS I +19 +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 Universi- +ties 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 obser- +vations 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. +Facilities: JWST(NIRCAM), JWST(NIRISS) +REFERENCES +2016, JWST User Documentation (JDox), JWST User +Documentation Website +Albers, S. M., Weisz, D. R., Cole, A. A., et al. 2019, MNRAS, 490, +5538, doi: 10.1093/mnras/stz2903 +Anderson, J., & King, I. R. 2000, PASP, 112, 1360, +doi: 10.1086/316632 +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, N., Covey, K. R., & Meyer, M. R. 2010, ARA&A, 48, +339, doi: 10.1146/annurev-astro-082708-101642 +Battaglia, G., Taibi, S., Thomas, G. F., & Fritz, T. K. 2022, A&A, +657, A54, doi: 10.1051/0004-6361/202141528 +Baumgardt, H., Faller, J., Meinhold, N., McGovern-Greco, C., & +Hilker, M. 2022, MNRAS, 510, 3531, +doi: 10.1093/mnras/stab3629 +Beaton, R. L., Freedman, W. L., Madore, B. F., et al. 2016, ArXiv +e-prints. https://arxiv.org/abs/1604.01788 +Bechtol, K., Drlica-Wagner, A., Balbinot, E., et al. 2015, ApJ, 807, +50, doi: 10.1088/0004-637X/807/1/50 +Bono, G., Stetson, P. B., Walker, A. R., et al. 2010, PASP, 122, +651, doi: 10.1086/653590 +Boyer. 2010, Is Dust Forming on the Red Giant Branch in 47 Tuc? +Boyer, M. L., Woodward, C. E., van Loon, J. T., et al. 2006, AJ, +132, 1415, doi: 10.1086/506518 +Boyer, M. L., McQuinn, K. B. W., Barmby, P., et al. 2015, ApJS, +216, 10, doi: 10.1088/0067-0049/216/1/10 +Boyer, M. L., McQuinn, K. B. W., Groenewegen, M. A. T., et al. +2017, ApJ, 851, 152, doi: 10.3847/1538-4357/aa9892 +Boyer, M. L., Williams, B. F., Aringer, B., et al. 2019, ApJ, 879, +109, doi: 10.3847/1538-4357/ab24e2 +Boylan-Kolchin, M., & Weisz, D. R. 2021, MNRAS, 505, 2764, +doi: 10.1093/mnras/stab1521 +Boylan-Kolchin, M., Weisz, D. R., Johnson, B. D., et al. 2015, +MNRAS, 453, 1503, doi: 10.1093/mnras/stv1736 +Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127, +doi: 10.1111/j.1365-2966.2012.21948.x +Brown, T. M., Smith, E., Ferguson, H. C., et al. 2006, ApJ, 652, +323, doi: 10.1086/508015 +Brown, T. M., Tumlinson, J., Geha, M., et al. 2012, ApJL, 753, +L21, doi: 10.1088/2041-8205/753/1/L21 +—. 2014, ApJ, 796, 91, doi: 10.1088/0004-637X/796/2/91 +Bullock, J. S., & Boylan-Kolchin, M. 2017, ARA&A, 55, 343, +doi: 10.1146/annurev-astro-091916-055313 +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, B., Hunter, D. A., Holtzman, J. A., et al. 1992, AJ, 104, +1721, doi: 10.1086/116355 +Casey, A. R., & Schlaufman, K. C. 2015, ApJ, 809, 110, +doi: 10.1088/0004-637X/809/2/110 +Chaboyer, B., Demarque, P., Kernan, P. J., & Krauss, L. M. 1996, +Science, 271, 957, doi: 10.1126/science.271.5251.957 +Chabrier, G., Baraffe, I., Allard, F., & Hauschildt, P. 2000, ApJ, +542, 464, doi: 10.1086/309513 +Chastenet, J., Sandstrom, K., Chiang, I.-D., et al. 2019, ApJ, 876, +62, doi: 10.3847/1538-4357/ab16cf +Chen, Y., Bressan, A., Girardi, L., et al. 2015, MNRAS, 452, 1068, +doi: 10.1093/mnras/stv1281 +Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102, +doi: 10.3847/0004-637X/823/2/102 +Cole, A. A., Weisz, D. R., Dolphin, A. E., et al. 2014, ApJ, 795, 54, +doi: 10.1088/0004-637X/795/1/54 +Cole, A. A., Skillman, E. D., Tolstoy, E., et al. 2007, ApJL, 659, +L17, doi: 10.1086/516711 + +20 +WEISZ ET AL. +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, C. 2013, ARA&A, 51, 393, +doi: 10.1146/annurev-astro-082812-141017 +Conroy, C., Villaume, A., van Dokkum, P. G., & Lind, K. 2018, +ApJ, 854, 139, doi: 10.3847/1538-4357/aaab49 +Conroy, C., Bonaca, A., Cargile, P., et al. 2019, ApJ, 883, 107, +doi: 10.3847/1538-4357/ab38b8 +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, M., Gennaro, M., Kalirai, J. S., Cohen, R. E., & Brown, +T. M. 2018, ApJ, 864, 147, doi: 10.3847/1538-4357/aad805 +Dalcanton, J. J., Williams, B. F., Seth, A. C., et al. 2009, ApJS, +183, 67, doi: 10.1088/0067-0049/183/1/67 +Dalcanton, J. J., Williams, B. F., Melbourne, J. L., et al. 2012a, +ApJS, 198, 6, doi: 10.1088/0067-0049/198/1/6 +Dalcanton, J. J., Williams, B. F., Lang, D., et al. 2012b, ApJS, 200, +18, doi: 10.1088/0067-0049/200/2/18 +Dalcanton, J. J., Fouesneau, M., Hogg, D. W., et al. 2015, ApJ, +814, 3, doi: 10.1088/0004-637X/814/1/3 +Di Valentino, E., Anchordoqui, L. A., Akarsu, ¨O., et al. 2021, +Astroparticle Physics, 131, 102605, +doi: 10.1016/j.astropartphys.2021.102605 +Dieball, A., Bedin, L. R., Knigge, C., et al. 2019, MNRAS, 486, +2254, doi: 10.1093/mnras/stz996 +—. 2016, ApJ, 817, 48, doi: 10.3847/0004-637X/817/1/48 +Dohm-Palmer, R. C., Skillman, E. D., Gallagher, J., et al. 1998, +AJ, 116, 1227, doi: 10.1086/300514 +Dolphin, A. E. 2000, PASP, 112, 1383, doi: 10.1086/316630 +—. 2002, MNRAS, 332, 91, +doi: 10.1046/j.1365-8711.2002.05271.x +—. 2016, ArXiv e-prints. https://arxiv.org/abs/1605.01461 +Dotter, A., Chaboyer, B., Jevremovi´c, D., et al. 2008, ApJS, 178, +89, doi: 10.1086/589654 +Dotter, A., Ferguson, J. W., Conroy, C., et al. 2015, MNRAS, 446, +1641, doi: 10.1093/mnras/stu2170 +Draine, B. T. 2007, Dust Masses, PAH Abundances, and Starlight +Intensities in the SINGS Galaxy Sample +Drlica-Wagner, A., Bechtol, K., Rykoff, E. S., et al. 2015, ApJ, +813, 109, doi: 10.1088/0004-637X/813/2/109 +Ekstr¨om, S., Georgy, C., Eggenberger, P., et al. 2012, A&A, 537, +A146, doi: 10.1051/0004-6361/201117751 +El-Badry, K., Weisz, D. R., & Quataert, E. 2017, ArXiv e-prints. +https://arxiv.org/abs/1701.02347 +El-Badry, K., Wetzel, A., Geha, M., et al. 2016, ApJ, 820, 131, +doi: 10.3847/0004-637X/820/2/131 +Eldridge, J. J., & Stanway, E. R. 2022, arXiv e-prints, +arXiv:2202.01413. https://arxiv.org/abs/2202.01413 +Eldridge, J. J., Stanway, E. R., Xiao, L., et al. 2017, PASA, 34, +e058, doi: 10.1017/pasa.2017.51 +Escala, I., Gilbert, K. M., Kirby, E. N., et al. 2020, ApJ, 889, 177, +doi: 10.3847/1538-4357/ab6659 +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, A., & Norris, J. E. 2015, ARA&A, 53, 631, +doi: 10.1146/annurev-astro-082214-122423 +Freedman, W. L., Hughes, S. M., Madore, B. F., et al. 1994, ApJ, +427, 628, doi: 10.1086/174172 +Freedman, W. L., Madore, B. F., Gibson, B. K., et al. 2001, ApJ, +553, 47, doi: 10.1086/320638 +Freedman, W. L., Madore, B. F., Hoyt, T., et al. 2020, ApJ, 891, 57, +doi: 10.3847/1538-4357/ab7339 +Fritz, T. K., Battaglia, G., Pawlowski, M. S., et al. 2018, ArXiv +e-prints, arXiv:1805.00908. https://arxiv.org/abs/1805.00908 +Fu, S. W., Weisz, D. R., Starkenburg, E., et al. 2022, ApJ, 925, 6, +doi: 10.3847/1538-4357/ac3665 +Gallart, C., Zoccali, M., & Aparicio, A. 2005, ARA&A, 43, 387, +doi: 10.1146/annurev.astro.43.072103.150608 +Gallart, C., Monelli, M., Mayer, L., et al. 2015, ApJL, 811, L18, +doi: 10.1088/2041-8205/811/2/L18 +Geha, M., Willman, B., Simon, J. D., et al. 2009, ApJ, 692, 1464, +doi: 10.1088/0004-637X/692/2/1464 +Geha, M., Brown, T. M., Tumlinson, J., et al. 2013, ApJ, 771, 29, +doi: 10.1088/0004-637X/771/1/29 +Gennaro, M., & Robberto, M. 2020, ApJ, 896, 80, +doi: 10.3847/1538-4357/ab911a +Gennaro, M., Geha, M., Tchernyshyov, K., et al. 2018a, ApJ, 863, +38, doi: 10.3847/1538-4357/aaceff +Gennaro, M., Tchernyshyov, K., Brown, T. M., et al. 2018b, ApJ, +855, 20, doi: 10.3847/1538-4357/aaa973 +Gilbert, K., Tollerud, E. J., Anderson, J., et al. 2019a, BAAS, 51, +540. https://arxiv.org/abs/1904.01074 +Gilbert, K. M., Kirby, E. N., Escala, I., et al. 2019b, ApJ, 883, 128, +doi: 10.3847/1538-4357/ab3807 +Gilbert, K. M., Kalirai, J. S., Guhathakurta, P., et al. 2014, ApJ, +796, 76, doi: 10.1088/0004-637X/796/2/76 +Gilbert, K. M., Quirk, A. C. N., Guhathakurta, P., et al. 2022, ApJ, +924, 116, doi: 10.3847/1538-4357/ac3480 +Girardi, L., Williams, B. F., Gilbert, K. M., et al. 2010, ApJ, 724, +1030, doi: 10.1088/0004-637X/724/2/1030 +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, K. D., Clayton, G. C., Misselt, K. A., Landolt, A. U., & +Wolff, M. J. 2003, ApJ, 594, 279, doi: 10.1086/376774 +Gordon, K. D., Fouesneau, M., Arab, H., et al. 2016, ApJ, 826, +104, doi: 10.3847/0004-637X/826/2/104 + +JWST RESOLVED STELLAR POPULATIONS I +21 +Gordon, K. D., Misselt, K. A., Bouwman, J., et al. 2021, ApJ, 916, +33, doi: 10.3847/1538-4357/ac00b7 +Gordon, K. D., Bohlin, R., Sloan, G. C., et al. 2022, AJ, 163, 267, +doi: 10.3847/1538-3881/ac66dc +Governato, F., Brook, C., Mayer, L., et al. 2010, Nature, 463, 203, +doi: 10.1038/nature08640 +Grebel, E. K., & Gallagher, III, J. S. 2004, ApJL, 610, L89, +doi: 10.1086/423339 +Green, G. M., Schlafly, E., Zucker, C., Speagle, J. S., & Finkbeiner, +D. 2019, ApJ, 887, 93, doi: 10.3847/1538-4357/ab5362 +Guhathakurta, P., Yanny, B., Schneider, D. P., & Bahcall, J. N. +1992, AJ, 104, 1790, doi: 10.1086/116359 +Gull, M., Weisz, D. R., Senchyna, P., et al. 2022, ApJ, 941, 206, +doi: 10.3847/1538-4357/aca295 +Harris, J., & Zaritsky, D. 2001, ApJS, 136, 25, +doi: 10.1086/321792 +Harris, W. E. 2010, arXiv e-prints, arXiv:1012.3224. +https://arxiv.org/abs/1012.3224 +Hayashi, C., & Nakano, T. 1963, Progress of Theoretical Physics, +30, 460, doi: 10.1143/PTP.30.460 +Hidalgo, S. L., Aparicio, A., Mart´ınez-Delgado, D., & Gallart, C. +2009, ApJ, 705, 704, doi: 10.1088/0004-637X/705/1/704 +Hidalgo, S. L., Pietrinferni, A., Cassisi, S., et al. 2018, ApJ, 856, +125, doi: 10.3847/1538-4357/aab158 +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, J. A., Afonso, C., & Dolphin, A. 2006, ApJS, 166, 534, +doi: 10.1086/507074 +Hunter, D. A., Shaya, E. J., Holtzman, J. A., et al. 1995, ApJ, 448, +179, doi: 10.1086/175950 +Ji, A. P., Frebel, A., Chiti, A., & Simon, J. D. 2016, Nature, 531, +610, doi: 10.1038/nature17425 +Jones, O. C., Meixner, M., Justtanont, K., & Glasse, A. 2017, ApJ, +841, 15, doi: 10.3847/1538-4357/aa6bf6 +Kalirai, J. S., Richer, H. B., Anderson, J., et al. 2012, AJ, 143, 11, +doi: 10.1088/0004-6256/143/1/11 +Kalirai, J. S., Anderson, J., Dotter, A., et al. 2013, ApJ, 763, 110, +doi: 10.1088/0004-637X/763/2/110 +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, N., Wetzel, A. R., Simon, J. D., et al. 2015, ArXiv +e-prints. https://arxiv.org/abs/1503.01785 +Kallivayalil, N., Sales, L. V., Zivick, P., et al. 2018, ApJ, 867, 19, +doi: 10.3847/1538-4357/aadfee +Kirby, E. N., Gilbert, K. M., Escala, I., et al. 2020, AJ, 159, 46, +doi: 10.3847/1538-3881/ab5f0f +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, S. E., Belokurov, V., Torrealba, G., & Evans, N. W. +2015, ApJ, 805, 130, doi: 10.1088/0004-637X/805/2/130 +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, M. R. 2014, ArXiv e-prints. +https://arxiv.org/abs/1402.0867 +Krumholz, M. R., McKee, C. F., & Bland-Hawthorn, J. 2019, +ARA&A, 57, 227, doi: 10.1146/annurev-astro-091918-104430 +Kumar, S. S. 1963, ApJ, 137, 1121, doi: 10.1086/147589 +Laevens, B. P. M., Martin, N. F., Sesar, B., et al. 2014, ApJL, 786, +L3, doi: 10.1088/2041-8205/786/1/L3 +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, B. P. M., Martin, N. F., Bernard, E. J., et al. 2015b, ApJ, +813, 44, doi: 10.1088/0004-637X/813/1/44 +Leaman, R., Cole, A. A., Venn, K. A., et al. 2009, ApJ, 699, 1, +doi: 10.1088/0004-637X/699/1/1 +Leaman, R., Venn, K. A., Brooks, A. M., et al. 2013, ApJ, 767, +131, doi: 10.1088/0004-637X/767/2/131 +Levesque, E. M. 2018, ApJ, 867, 155, +doi: 10.3847/1538-4357/aae776 +Li, T. S., Simon, J. D., Drlica-Wagner, A., et al. 2017, ApJ, 838, 8, +doi: 10.3847/1538-4357/aa6113 +Longeard, N., Martin, N., Starkenburg, E., et al. 2018, MNRAS, +1901, doi: 10.1093/mnras/sty1986 +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, L. A., Krumholz, M. R., Bolatto, A. D., et al. 2014, ApJ, +795, 121, doi: 10.1088/0004-637X/795/2/121 +Madore, B. F., Freedman, W. L., Hatt, D., et al. 2018, ApJ, 858, 11, +doi: 10.3847/1538-4357/aab7f4 +Maraston, C.; Daddi, E. R. A. C. A. D. M. P. C. P. A. P. N. 2006, +The Astrophysical Journal +Marigo, P., Girardi, L., Bressan, A., et al. 2017, ApJ, 835, 77, +doi: 10.3847/1538-4357/835/1/77 +Marini, E., Dell’Agli, F., Di Criscienzo, M., et al. 2020, MNRAS, +493, 2996, doi: 10.1093/mnras/staa353 +Martin, N. F., Geha, M., Ibata, R. A., et al. 2016, MNRAS, 458, +L59, doi: 10.1093/mnrasl/slw013 +Massey, P. 2003, ARA&A, 41, 15, +doi: 10.1146/annurev.astro.41.071601.170033 +Mateo, M. L. 1998, ARA&A, 36, 435, +doi: 10.1146/annurev.astro.36.1.435 + +22 +WEISZ ET AL. +McConnachie, A. W. 2012, AJ, 144, 4, +doi: 10.1088/0004-6256/144/1/4 +McConnachie, A. W., Ibata, R., Martin, N., et al. 2018, ApJ, 868, +55, doi: 10.3847/1538-4357/aae8e7 +McKee, C. F., & Ostriker, E. C. 2007, ARA&A, 45, 565, +doi: 10.1146/annurev.astro.45.051806.110602 +McQuinn, K. B. W., Boyer, M., Skillman, E. D., & Dolphin, A. E. +2019a, ApJ, 880, 63, doi: 10.3847/1538-4357/ab2627 +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, K. B. W., Skillman, E. D., Dolphin, A. E., & Mitchell, +N. P. 2015. https://arxiv.org/abs/1505.00791v1 +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, K. B. W., van Zee, L., & Skillman, E. D. 2019b, ApJ, +886, 74, doi: 10.3847/1538-4357/ab4c37 +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, M., Galliano, F., Hony, S., et al. 2010, A&A, 518, L71, +doi: 10.1051/0004-6361/201014662 +Melbourne, J., Williams, B. F., Dalcanton, J. J., et al. 2012, ApJ, +748, 47, doi: 10.1088/0004-637X/748/1/47 +Melotte, P. J. 1926, MNRAS, 86, 636, doi: 10.1093/mnras/86.8.636 +M´esz´aros, S., Masseron, T., Garc´ıa-Hern´andez, D. A., et al. 2020, +MNRAS, 492, 1641, doi: 10.1093/mnras/stz3496 +Milone, A. P., Marino, A. F., Cassisi, S., et al. 2012, ApJL, 754, +L34, doi: 10.1088/2041-8205/754/2/L34 +Milone, A. P., Marino, A. F., Bedin, L. R., et al. 2014, MNRAS, +439, 1588, doi: 10.1093/mnras/stu030 +—. 2017, MNRAS, 469, 800, doi: 10.1093/mnras/stx836 +Monelli, M., Hidalgo, S. L., Stetson, P. B., et al. 2010, ApJ, 720, +1225, doi: 10.1088/0004-637X/720/2/1225 +Oey, M. S. 1996, ApJ, 467, 666, doi: 10.1086/177642 +Ostriker, E. C., McKee, C. F., & Leroy, A. K. 2010, ApJ, 721, 975, +doi: 10.1088/0004-637X/721/2/975 +Paresce, F., Shara, M., Meylan, G., et al. 1991, Nature, 352, 297, +doi: 10.1038/352297a0 +Patel, E., Kallivayalil, N., Garavito-Camargo, N., et al. 2020, ApJ, +893, 121, doi: 10.3847/1538-4357/ab7b75 +Pe˜narrubia, J., Ludlow, A. D., Chanam´e, J., & Walker, M. G. 2016, +MNRAS, 461, L72, doi: 10.1093/mnrasl/slw090 +Pearson, S., Clark, S. E., Demirjian, A. J., et al. 2022, ApJ, 926, +166, doi: 10.3847/1538-4357/ac4496 +Pellegrini, E. W., Baldwin, J. A., & Ferland, G. J. 2011, ApJ, 738, +34, doi: 10.1088/0004-637X/738/1/34 +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, G., Milone, A. P., Bedin, L. R., et al. 2015, AJ, 149, 91, +doi: 10.1088/0004-6256/149/3/91 +Ricotti, M., & Gnedin, N. Y. 2005, ApJ, 629, 259, +doi: 10.1086/431415 +Riess, A. G., Macri, L., Casertano, S., et al. 2011, ApJ, 730, 119, +doi: 10.1088/0004-637X/730/2/119 +Riess, A. G., Macri, L. M., Hoffmann, S. L., et al. 2016, ApJ, 826, +56, doi: 10.3847/0004-637X/826/1/56 +Riess, A. G., Yuan, W., Macri, L. M., et al. 2021, arXiv e-prints, +arXiv:2112.04510. https://arxiv.org/abs/2112.04510 +Rigby, J., Perrin, M., McElwain, M., et al. 2022, arXiv e-prints, +arXiv:2207.05632. https://arxiv.org/abs/2207.05632 +Rubio, M., Elmegreen, B. G., Hunter, D. A., et al. 2015, Nature, +525, 218, doi: 10.1038/nature14901 +Sacchi, E., Richstein, H., Kallivayalil, N., et al. 2021, ApJL, 920, +L19, doi: 10.3847/2041-8213/ac2aa3 +Salaris, M., Chieffi, A., & Straniero, O. 1993, ApJ, 414, 580, +doi: 10.1086/173105 +Sana, H., de Mink, S. E., de Koter, A., et al. 2012, Science, 337, +444, doi: 10.1126/science.1223344 +Sarajedini, A., Dotter, A., & Kirkpatrick, A. 2009, ApJ, 698, 1872, +doi: 10.1088/0004-637X/698/2/1872 +Sarajedini, A., Bedin, L. R., Chaboyer, B., et al. 2007, AJ, 133, +1658, doi: 10.1086/511979 +Schechter, P. L., Mateo, M., & Saha, A. 1993, PASP, 105, 1342, +doi: 10.1086/133316 +Schlafly, E. F., Meisner, A. M., Stutz, A. M., et al. 2016, ApJ, 821, +78, doi: 10.3847/0004-637X/821/2/78 +Schlaufman, K. C., & Casey, A. R. 2014, ApJ, 797, 13, +doi: 10.1088/0004-637X/797/1/13 +Schlawin, E., Leisenring, J., Misselt, K., et al. 2020, AJ, 160, 231, +doi: 10.3847/1538-3881/abb811 +Simon, J. D. 2019, arXiv e-prints, arXiv:1901.05465. +https://arxiv.org/abs/1901.05465 +Simon, J. D., & Geha, M. 2007, ApJ, 670, 313, +doi: 10.1086/521816 +Skillman, E. D., Hidalgo, S. L., Weisz, D. R., et al. 2014, ApJ, 786, +44, doi: 10.1088/0004-637X/786/1/44 +Skillman, E. D., Monelli, M., Weisz, D. R., et al. 2017, ApJ, 837, +102, doi: 10.3847/1538-4357/aa60c5 +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, S. T., Patel, E., Fardal, M. A., et al. 2020, ApJ, 901, 43, +doi: 10.3847/1538-4357/abaf49 + +JWST RESOLVED STELLAR POPULATIONS I +23 +Starkenburg, E., Oman, K. A., Navarro, J. F., et al. 2017a, +MNRAS, 465, 2212, doi: 10.1093/mnras/stw2873 +Starkenburg, E., Martin, N., Youakim, K., et al. 2017b, MNRAS, +471, 2587, doi: 10.1093/mnras/stx1068 +Stetson, P. B. 1987, PASP, 99, 191, doi: 10.1086/131977 +—. 1994, PASP, 106, 250, doi: 10.1086/133378 +Stinson, G., Seth, A., Katz, N., et al. 2006, MNRAS, 373, 1074, +doi: 10.1111/j.1365-2966.2006.11097.x +Stinson, G. S., Dalcanton, J. J., Quinn, T., Kaufmann, T., & +Wadsley, J. 2007, ApJ, 667, 170, doi: 10.1086/520504 +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, E. 1996, ApJ, 462, 684, doi: 10.1086/177182 +Tolstoy, E., Hill, V., & Tosi, M. 2009, ARA&A, 47, 371, +doi: 10.1146/annurev-astro-082708-101650 +Tosi, M., Greggio, L., & Focardi, P. 1989, Ap&SS, 156, 295, +doi: 10.1007/BF00646381 +Tully, R. B., Pomar`ede, D., Graziani, R., et al. 2019, ApJ, 880, 24, +doi: 10.3847/1538-4357/ab2597 +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, R. P., Fardal, M., Besla, G., et al. 2012, ApJ, 753, 8, +doi: 10.1088/0004-637X/753/1/8 +VandenBerg, D. A., Bergbusch, P. A., Dotter, A., et al. 2012, ApJ, +755, 15, doi: 10.1088/0004-637X/755/1/15 +VandenBerg, D. A., Edvardsson, B., Casagrande, L., & Ferguson, +J. W. 2022, MNRAS, 509, 4189, doi: 10.1093/mnras/stab2996 +Vargas, L. C., Geha, M., Kirby, E. N., & Simon, J. D. 2013, ApJ, +767, 134, doi: 10.1088/0004-637X/767/2/134 +Vargas, L. C., Geha, M. C., & Tollerud, E. J. 2014, ApJ, 790, 73, +doi: 10.1088/0004-637X/790/1/73 +Venn, K. A., Irwin, M., Shetrone, M. D., et al. 2004, AJ, 128, 1177, +doi: 10.1086/422734 +Ventura, Paolo; D’Antona, F. M. I. G. R. 2001, The Astrophysical +Journal +Warfield, J. T., Kallivayalil, N., Zivick, P., et al. 2023, MNRAS, +519, 1189, doi: 10.1093/mnras/stac3647 +Weisz, D., & Boylan-Kolchin, M. 2019, BAAS, 51, 1. +https://arxiv.org/abs/1901.07571 +Weisz, D. R., Dolphin, A. E., Skillman, E. D., et al. 2014a, ApJ, +789, 147, doi: 10.1088/0004-637X/789/2/147 +—. 2014b, ApJ, 789, 148, doi: 10.1088/0004-637X/789/2/148 +Wetzel, A. R., Hopkins, P. F., Kim, J.-h., et al. 2016, ApJL, 827, +L23, doi: 10.3847/2041-8205/827/2/L23 +Williams, B. F., Lang, D., Dalcanton, J. J., et al. 2014, ApJS, 215, +9, doi: 10.1088/0067-0049/215/1/9 +Williams, B. F., Durbin, M. J., Dalcanton, J. J., et al. 2021, ApJS, +253, 53, doi: 10.3847/1538-4365/abdf4e +Willman, B., & Strader, J. 2012, AJ, 144, 76, +doi: 10.1088/0004-6256/144/3/76 +Wolf, M. 1909, Astronomische Nachrichten, 183, 187, +doi: 10.1002/asna.19091831204 +Yanchulova Merica-Jones, P., Sandstrom, K. M., Johnson, L. C., +et al. 2017, ApJ, 847, 102, doi: 10.3847/1538-4357/aa8a67 +—. 2021, ApJ, 907, 50, doi: 10.3847/1538-4357/abc48b +Zivick, P., Kallivayalil, N., van der Marel, R. P., et al. 2018, ApJ, +864, 55, doi: 10.3847/1538-4357/aad4b0 +