# The JWST Resolved Stellar Populations Early Release Science Program IV: The
Star Formation History of the Local Group Galaxy WLM

Kristen. B. W. McQuinn Rutgers University, Department of Physics and
Astronomy, 136 Frelinghuysen Road, Piscataway, NJ 08854, USA  Max J. B. Newman
Rutgers University, Department of Physics and Astronomy, 136 Frelinghuysen
Road, Piscataway, NJ 08854, USA  Alessandro Savino Department of Astronomy,
University of California, Berkeley, CA 94720, USA  Andrew E. Dolphin Raytheon
Technologies, 1151 E. Hermans Road, Tucson, AZ 85756, USA  Steward
Observatory, University of Arizona, 933 N. Cherry Avenue, Tucson, AZ 85719,
USA  Daniel R. Weisz Department of Astronomy, University of California,
Berkeley, CA 94720, USA  Benjamin F. Williams Department of Astronomy,
University of Washington, Box 351580, U.W., Seattle, WA 98195-1580, USA
Martha L. Boyer Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  Roger E. Cohen Rutgers University, Department of
Physics and Astronomy, 136 Frelinghuysen Road, Piscataway, NJ 08854, USA
Matteo Correnti INAF Osservatorio Astronomico di Roma, Via Frascati 33, 00078,
Monteporzio Catone, Rome, Italy  ASI-Space Science Data Center, Via del
Politecnico, I-00133, Rome, Italy  Andrew A. Cole School of Natural Sciences,
University of Tasmania, Private Bag 37, Hobart, Tasmania 7001, Australia
Marla C. Geha Department of Astronomy, Yale University, New Haven, CT 06520,
USA  Mario Gennaro Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  The William H. Miller III Department of Physics &
Astronomy, Bloomberg Center for Physics and Astronomy, Johns Hopkins
University, 3400 N. Charles Street, Baltimore, MD 21218, USA  Nitya
Kallivayalil Department of Astronomy, The University of Virginia, 530
McCormick Road, Charlottesville, VA 22904, USA  Karin M. Sandstrom Department
of Astronomy & Astrophysics, University of California San Diego, 9500 Gilman
Drive, La Jolla, CA 92093, USA  Evan D. Skillman University of Minnesota,
Minnesota Institute for Astrophysics, School of Physics and Astronomy, 116
Church Street, S.E., Minneapolis, MN 55455, USA  Jay Anderson Space Telescope
Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Alberto
Bolatto Department of Astronomy, University of Maryland, College Park, MD
20742, USA  Joint Space-Science Institute, University of Maryland, College
Park, MD 20742, USA  Michael Boylan-Kolchin Department of Astronomy, The
University of Texas at Austin, 2515 Speedway, Stop C1400, Austin, TX, 787125,
USA  Christopher T. Garling Department of Astronomy, The University of
Virginia, 530 McCormick Road, Charlottesville, VA 22904, USA  Karoline M.
Gilbert Space Telescope Science Institute, 3700 San Martin Dr., Baltimore, MD
21218, USA  The William H. Miller III Department of Physics & Astronomy,
Bloomberg Center for Physics and Astronomy, Johns Hopkins University, 3400 N.
Charles Street, Baltimore, MD 21218, USA  Léo Girardi Padova Astronomical
Observatory, Vicolo dell’Osservatorio 5, Padova, Italy  Jason S. Kalirai John
Hopkins Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD
20723, USA  Alessandro Mazzi Dipartimento di Fisica e Astronomia Galileo
Galilei, Università di Padova, Vicolo dell’Osservatorio 3, I-35122 Padova,
Italy  Giada Pastorelli Padova Astronomical Observatory, Vicolo
dell’Osservatorio 5, Padova, Italy  Hannah Richstein Department of Astronomy,
The University of Virginia, 530 McCormick Road, Charlottesville, VA 22904, USA
Jack T. Warfield Department of Astronomy, The University of Virginia, 530
McCormick Road, Charlottesville, VA 22904, USA

###### Abstract

We present the first star formation history (SFH) and age-metallicity relation
(AMR) derived from resolved stellar populations imaged with the JWST NIRCam
instrument. The target is the Local Group star-forming galaxy WLM at 970 kpc.
The depth of the color-magnitude diagram (CMD) reaches below the oldest main
sequence turn-off with a SNR$=10$ at M${}_{F090W}=+4.6$ mag; this is the
deepest CMD for any galaxy that is not a satellite of the Milky Way. We use
Hubble Space Telescope (HST) optical imaging that overlaps with the NIRCam
observations to directly evaluate the SFHs derived based on data from the two
great observatories. The JWST and HST-based SFHs are in excellent agreement.
We use the metallicity distribution function measured from stellar spectra to
confirm the trends in the AMRs based on the JWST data. Together, these results
confirm the efficacy of recovering a SFH and AMR with the NIRCam F090W$-$F150W
filter combination and provide validation of the sensitivity and accuracy of
stellar evolution libraries in the near-infrared relative to the optical for
SFH recovery work. From the JWST data, WLM shows an early onset to star
formation, followed by an extended pause post-reionization before star
formation re-ignites, which is qualitatively similar to what has been observed
in the isolated galaxies Leo A and Aquarius. Quantitatively, 15% of the
stellar mass formed in the first Gyr, while only 10% formed over the next
$\sim$5 Gyr; the stellar mass then rapidly doubled in $\sim 2.5$ Gyr, followed
by constant star formation over the last $\sim 5$ Gyr.

Stellar populations (1622), Local Group (929), Hertzsprung Russell diagram
(725), JWST (2291), Hubble Space Telescope (761)

††facilities: JWST, Hubble Space Telescope††software: This research made use
of Astropy,333http://www.astropy.org a community-developed core Python package
for Astronomy (Astropy Collaboration et al., 2013, 2018, 2022); DOLPHOT
(Dolphin, 2000, 2016); and match (Dolphin, 2002, 2013).

##  1 Introduction

The James Webb Space Telescope (JWST) is capable of imaging stars in nearby
galaxies to deeper photometric depths, at higher angular resolution, and with
greater photometric precision than ever before possible. Such deep imaging is
information-rich, and can provide robust constraints on the stellar mass
assembly of galaxies over cosmic timescales. Specifically, color-magnitude
diagrams (CMDs) from high-fidelity photometry of stars, including faint, old,
low-mass stars (i.e., stars below the oldest main sequence turn-off; oMSTO)
enable the reconstruction of robust star formation histories (SFHs) with high
temporal resolution at all ages. These SFHs help elucidate the fundamental
process of star formation that drives much of the cosmic baryon cycle and
provide key benchmarks for testing galaxy formation and evolution theories.
JWST therefore will transform our understanding of the growth of low-mass
galaxies via archeological studies of resolved stars in nearby galaxies.

###  1.1 HST’s SFH Legacy

High temporal resolution SFHs from CMD-fitting techniques were originally made
possible because of the Hubble Space Telescope’s (HST’s) sensitivity and image
quality, and the ability to perform precision point-spread function (PSF)
photometry of resolved stars down to faint magnitudes (e.g., Dolphin, 2000,
2002; Tolstoy et al., 2009). These SFHs have been instrumental in shaping our
understanding of how galaxies build their stellar mass as a function of time.
The scientific insights gained with the SFHs from HST data are quite diverse
and, in large part, are determined by the photometric depth of a given data
set.

For example, SFHs have been derived for many galaxies within the Local Group
(i.e., very nearby systems) from photometric catalogs, with the majority of
those catalogs reaching depths below the oMSTO. The oMSTO feature helps to
break the age-metallicity degeneracy for older-age stars in a CMD, thereby
facilitating robust SFH recovery with temporal resolution as fine as
log($\delta$t) $=0.05$ at the oldest lookback times. These SFHs characterize
the properties of satellite galaxies around the Milky Way and M31, including
investigating quenching timescales and possible host-galaxy dependencies
(e.g., Monelli et al., 2010; Weisz et al., 2014; Skillman et al., 2017; Weisz
et al., 2019; Sacchi et al., 2021; McQuinn et al., 2023a, b; Savino et al.,
2023). SFHs of the Large and Small Magellanic Clouds from similarly deep HST
data and ground-based data have revealed synchronized bursts of star formation
that are likely interaction-driven (Harris & Zaritsky, 2009; Weisz et al.,
2013; Massana et al., 2022). SFHs are currently being derived from $>100$ HST
pointings across the Clouds in an effort to quantify the radial variations in
their histories (Roger Cohen et al. in preparation). SFHs have also
constrained the early evolution of a handful of gas-rich galaxies located
within the Local Group at farther distances from the MW and M31 than the
satellite systems, revealing intriguing differences on when these systems
began to form their stellar mass in earnest (e.g., Cole et al., 2007; Hidalgo
et al., 2011; Skillman et al., 2014; Cole et al., 2014; Albers et al., 2019).

Table 1: Properties and Observational Details of WLM WLM Properties
---
$M_{V}$ (mag) |  $-$14.2 (1)
$r_{h}$ (′) | 7.8 (1)
E(B$-$V) (mag) | 0.03 (2)
Distance (kpc) | 968${}^{+41}_{-40}$ (3)
$\mu$ (mag) | 24.93$\pm$0.09 (3)
Supergiant stars $\langle$[Z]$\rangle$ |  $-0.87\pm 0.06$ (4)
Gas-phase 12$+$log(O/H) | 7.83$\pm 0.06$ (5)
RGB stars $\langle$[Fe/H]$\rangle$ |  $-$1.14; $\sigma=0.33$ (6)
JWST NIRCam Observations
PID | ERS-1334
RA (J2000) | 00:01:57.429
Dec (J200) |  $-$15:28:52.49
F090W (s) | 30492
F150W (s) | 23707
F090W 50% Completeness (mag) | 29.23 ($+$4.30)
F150W 50% Completeness (mag) | 27.89 ($+$2.96)
log($M_{*}$/M⊙), Full FoV, PARSEC | $7.12\pm 0.01$
log($M_{*}$/M⊙), Full FoV, BaSTI | $7.13\pm 0.01$
log($M_{*}$/M⊙), ACS FoV, PARSEC | $6.54\pm 0.01$
log($M_{*}$/M⊙), ACS FoV, BaSTI | $6.58\pm 0.01$
log($M_{*}$/M⊙), Joint, PARSEC | $6.64\pm 0.01$
log($M_{*}$/M⊙), Joint, BaSTI | $6.65\pm 0.01$
$\tau_{10}$ PARSEC; BaSTI (Gyr) |  $12.61^{+0.06}_{-0.06}$; $13.12^{+0.02}_{-0.03}$
$\tau_{25}$ PARSEC; BaSTI (Gyr) |  $7.63^{+0.05}_{-0.03}$; $7.87^{+0.06}_{-0.03}$
$\tau_{50}$ PARSEC; BaSTI (Gyr) |  $5.08^{+0.05}_{-0.01}$; $5.17^{+0.02}_{-0.02}$
$\tau_{90}$ PARSEC; BaSTI (Gyr) |  $1.18^{+0.01}_{-0.01}$; $1.11^{+0.01}_{-0.01}$
HST ACS Observations
PID | HST-GO-13768
RA (J2000) | 00:01:57.319
Dec (J200) |  $-$15:31:22.52
F475W (s) | 27360
F814W (s) | 34050
F475W 50% Completeness (mag) | 28.28 ($+$3.35)
F814W 50% Completeness (mag) | 27.20 ($+$2.27)
log($M_{*}$/M⊙), NIRCam FoV, PARSEC | $6.60\pm 0.01$
log($M_{*}$/M⊙), NIRCam FoV, BaSTI | $6.58\pm 0.01$

The first in-depth look at the time-resolved history of a spiral galaxy has
come from measurements of the recent and lifetime SFHs of M31, and have
included tracking changes in the different structural features of the galaxy
and providing a measure of the chemical evolution history of the system
(Richardson et al., 2008; Lewis et al., 2015; Williams et al., 2017; Telford
et al., 2019). Similar efforts are under way for the lower-mass spiral M33
(Williams et al., 2021).

Finally, SFHs have been reconstructed for numerous galaxies outside the Local
Group (i.e., at slightly farther distances). The photometric depth achieved
with HST imaging is more limited, reaching below the horizontal branch (HB),
$\sim 4-5$ mag below the tip of the red giant branch (TRGB), for a few
galaxies (e.g., Leo P, UGC4483; McQuinn et al., 2015b; Sacchi et al., 2021),
but more typically $1-3$ magnitudes below the TRGB. Given the depth of such
data, SFHs can still be characterized but with significantly less temporal
resolution at older look-back times (i.e., the oldest-age time bin can be as
large as several Gyr). Yet, as these field galaxies are primarily gas-rich,
star-forming systems, the SFHs derived from the shallower imaging are useful
in that they robustly constrain recent ($\sim 1-2$ Gyr) star formation
activity with fine temporal resolution (log($\delta$t) $=0.10$; e.g., Aloisi
et al., 1999; McQuinn et al., 2010, 2015a, 2015b; Cignoni et al., 2018;
Annibali & Tosi, 2022; Hunter et al., 2022).

To date, almost all SFH determinations from CMD-fitting have been done using
data collected with optical filters, with the notable exceptions of the SFH
maps performed on near-infrared data from the ESO/VISTA survey of the
Magellanic Clouds (Rubele et al., 2018; Mazzi et al., 2021). These studies
demonstrate that the CMD-fitting method of SFH recovery works as well for
near-infrared filters as for optical filters. The comparison between the NIR-
derived and the previous optical-derived SFH maps by Harris & Zaritsky (2009)
showed good quantitative agreement at all ages larger than $\sim 10^{8}$ yr.
In some small areas of the Magellanic Clouds, near-infrared photometry has
already shown clear advantages over optical photometry by providing sharper
CMD features in regions severely affected by differential extinction and by
better sampling the evolved stars (especially the age-sensitive sub-giant
branch) in regions with a high total extinction. The potential advantages of
near-infrared filters have been little explored outside the Magellanic Clouds,
mainly because of the intrinsic difficulty to reach deep magnitudes using
ground-based near-infrared cameras. This difficulty can now be superseded with
the JWST which provides a pathway for SFH work from deep near-infrared CMDs in
targeted fields, which we explore in detail. Looking to the future, the large-
area near-infrared observations from the Nancy Grace Roman Space Telescope
will offer the opportunity to recover SFHs of galaxies residing in many
different environments and to study stellar population structures that can
span large physical scales, such as stellar streams and galaxy halos.

###  1.2 JWST’s Advantages for SFHs

JWST’s sensitivity, high resolution, and infrared capabilities enable the
robust reconstruction of SFHs of nearby galaxies more efficiently, out to
larger distances, and in different galactic environments than is possible with
HST. JWST’s larger aperture (6.5m vs. HST’s 2.4m mirror) enables imaging of
resolved stars to photometric depths below the oMSTO in galaxies outside the
Local Group. This capability opens up the exploration of the early history of
galaxies in varied environments, including isolated environments that provide
unique laboratories for probing secular evolution and testing galaxy formation
models. JWST’s higher resolution and wavelength coverage make morphological
and color-based indicators more effective at removing background galaxies,
thereby improving the purity of photometric catalogs and SFH fits (Warfield et
al., 2023). Cooler stars are intrinsically brighter in the near infrared,
which increases observing efficiency when targeting evolved stars (such as RGB
and asymptotic giant branch, or AGB, stars) and, importantly for ancient SFHs,
oMSTO stars. For example, a metal-poor oMSTO star ($M_{*}$ $=0.76$ M⊙; 13 Gyr;
[M/H] $=-1.4$) has absolute magnitudes of 4.4, 3.6, 3.5, and 3.0 in the F475W,
F814W, F090W, and F150W filters, respectively (based on the PARSEC stellar
library); this is a gain in brightness of $\sim 1.4$ mag moving from the F475W
to the F150W filter. Finally, JWST offers potential gains in accurately and
precisely recovering SFHs in metal-rich galaxies with significant dust
reservoirs that have correspondingly higher extinction.

Figure 1: Ground-based optical image of WLM (image credit: ESO, VST/Omegacam
Local Group Survey) with footprints of the NIRCam (solid), NIRISS (dotted),
HST ACS (dashed), and HST WFC3/UVIS (dashed-dotted) fields overlaid.

###  1.3 The SFH Goals of the JWST Early Release Science Resolved Stellar
Populations Program

The JWST Early Release Science Resolved Stellar Populations program (ERS-1334)
was designed to provide data reduction and analysis tools for resolved star
science with JWST and to test JWST’s ability to recover lifetime SFHs from
resolved stars, paving the way for future studies. The details on the overall
ERS program are presented in Weisz et al. (2023), with individual science
results to be reported separately in upcoming papers from the ERS team. Here,
we present the first SFH results from JWST ERS data using the well-established
CMD-fitting technique as implemented in match (e.g., Dolphin, 2002; McQuinn et
al., 2010; Weisz et al., 2011; Skillman et al., 2017; Savino et al., 2023).

The aims of this SFH analysis are fourfold. First, we will quantify the
efficacy of recovering SFHs with the F090W - F150W filter combination. These
filters were chosen over the F070W and F115W filters to balance the competing
needs of a wide color baseline (which improves metallicity sensitivity in the
CMD), signal-to-noise requirements, and the sampling of the PSF. Second, we
will test the sensitivity and accuracy of stellar evolution libraries for
recovering SFHs and age-metallicty relations or AMRs (i.e., color$-$Teff
transformations and photometric calibrations) with the JWST near-infrared
data. Third, we will compare the SFH results derived from the individual HST
and JWST CMDs with a SFH derived by simultaneously fitting the JWST and HST
CMDs. Finally, we will explore the stellar mass assembly of WLM and compare
with other gas-rich low-mass galaxies for which the requisite deep imaging
data have been obtained.

###  1.4 The star-forming, Local Group galaxy WLM

The target selected for the JWST Near Infrared Camera (NIRCam) based SFH is
the well-studied galaxy Wolf–Lundmark–Melotte (WLM), a gas-rich, low-mass
($M_{*}$ $=4.3\times 10^{7}$ M⊙; McConnachie, 2012) system located near the
outskirts of the Local Group (at a distance of $=968^{+41}_{-40}$ kpc; Albers
et al., 2019). An overview of WLM’s properties is provided in Table 1.

WLM was chosen primarily because of its proximity, isolation and the rich
suite of existing data sets. Specifically, deep HST Advanced Camera for
Surveys (ACS; Ford et al., 1998) imaging of the stellar disk is available that
overlaps with the NIRCam field of view (see Section 2). Although the NIRCam
data are deeper than the ACS data (50% completeness limits of
M${}_{F090W}=+4.30$ mag vs. M${}_{F475W}=+3.35$), the photometric depth of the
ACS data still reaches below the oMSTO. Thus, the SFH derived from the ACS
data facilitates a detailed comparison with the SFH derived here from the
NIRCam data.

Figure 2: JWST NIRCam images from Module A (left) and Module B (right) of the
observed field in WLM using F090W (blue), F150W (green), and an average of
F250W and F430W (red). Each module is 2.2′ on a side, which is equivalent to a
physical scale of 0.6 kpc at the distance of WLM. Image Credit: NASA, ESA,
CSA, Kristen McQuinn (Rutgers University), Zolt G. Levay, Alyssa Pagan
(STScI).

From the previously published ACS-based SFH, it appears that the central part
of WLM formed $\sim$50% of its stellar mass within the last $\sim 5$ Gyr, and
only $\sim 20$% of its stellar mass at early times (Albers et al., 2019). This
implies that WLM experienced a period of quiescence post-reionization ($z\sim
6$), which has only been noted in the two other gas-rich, isolated low-mass
($M_{*}$ $<10^{8}$ M⊙) galaxies in the Local Group with the requisite data
quality, Leo A and Aquarius (Cole et al., 2007, 2014). Such a gap in the SFH
of low-mass galaxies is somewhat surprising, but given the small sample size,
it is unclear whether a pause in star formation is common or atypical.
Regardless, the NIRCam data can provide independent confirmation of the HST-
based SFH results. Spectroscopic observations of individual stars are also
available, which provide robust measurements of the stellar metallicity
distribution in WLM (Leaman et al., 2009) allowing us to test the AMR derived
as part of the CMD-fitting approach.

This paper is organized as follows. We briefly summarize the observations and
data processing of the JWST and the HST data in Section 2. The SFHs
methodology and results, including analysis of the SFHs derived from stars in
the overlapping footprint between NIRCam and ACS and a comparison of the AMR
with an independently measured MDF, are presented in Sections 3 and 4. We
summarize our findings in Section 5.

##  2 Observations and Data Reduction

###  2.1 Overview of JWST NIRCam Data

Shown in Figure 1, JWST observed a central region of the stellar disk in WLM
with NIRCam (Rieke et al., 2023) as the primary instrument (solid lines) and
an outer field with Near Infrared Imager and Slitless Spectrograph (NIRISS;
Doyon et al., 2023) in parallel (dotted lines). For NIRCam, we selected orient
constraints such that one NIRCam module overlapped a region with the deepest
optical HST imaging available (dashed lines, HST-GO-13678; Albers et al.,
2019), while the other module overlapped a region with HST UV, optical, and IR
imaging from various programs (not shown; PIDs: HST-GO-6798, HST-GO-11079,
HST-GO-15275, HST-GO-16162), and ALMA observations where CO clouds were
identified (Rubio et al., 2015). This carefully chosen areal coverage with
such comprehensive, multi-wavelength ancillary data enables multiple science
goals including: a comparison of the SFHs derived from JWST and HST presented
in this work, a study of spatial variations in the SFHs, an investigation of
the evolved stars, and an exploration of the stellar populations co-spatial
with the CO clouds. Dictated by the orient constraints used for the NIRCam
placement, the NIRISS field covered an outer region of the galaxy to the
southeast. Here, we focus on the NIRCam data; analysis of the less-populated
NIRISS imaging will be presented in a future paper.

Table 1 lists the coordinates of our observed fields, and the total
integration times per filter for NIRCam from ERS-1334. These data were
obtained from the Mikulski Archive for Space Telescopes (MAST) at the Space
Telescope Science Institute. The specific observations analyzed can be
accessed via https://doi.org/10.17909/t6pm-x063 (catalog DOI:
10.17909/t6pm-x063). We refer the reader to Weisz et al. (2023) for a detailed
description of the observing strategy. Note that while the full NIRCam data
includes imaging from the Long Wavelength (LW) F250W and F430M filters, we
only use the Short Wavelength (SW) F090W and F150W filters to derive the SFH
as the data are deeper and this filter pair is more sensitive to changes in
stellar ages.

Figure 2 presents color images for the two NIRCam modules created using the
F090W filter (blue), F150W filter (green), and the sum of the F250M and F430M
filters (red). The image demonstrates the exquisite resolution and depth of
the NIRCam imaging that will be exceptional for resolved star science in
nearby galaxies. Careful inspection of the fields reveal changes in the
stellar populations, from small variations in the stellar density, to stellar
sources still somewhat embedded in their nascent clouds, to extremely red
stars that are dusty, AGB candidates.

###  2.2 NIRCam Photometry and Artificial Star Tests

We performed point-spread function (PSF) photometry on the NIRCam images using
the photometry software package
DOLPHOT111http://americano.dolphinsim.com/dolphot/nircam.html (Dolphin, 2000,
2016) with the new beta-version of a JWST NIRCam-specific module (Weisz et
al., submitted). As inputs, we used the F150W stage 3 drizzled i2d.fits file
from the JWST data pipeline as the astrometric reference image for identifying
point sources, and the cal.fits files processed with the JWST calibration
pipeline version 1.9.6 and the pmap 1075 reference files to measure the flux
of each identified source. The cal.fits files and the i2d.fits file were
internally aligned by the JWST pipeline.

Our photometric reduction with DOLPHOT used model PSFs generated with the
WebbPSF simulation tool (version 1.1.0). The WebbPSF tool adopts in-flight
optical performance data and incorporates time-dependent optical path delay
(OPD) maps based on the telescope configuration. We used the OPD map of the
NIRCam instrument around the time our observations were taken. Despite being
well-matched to the telescope conditions, the WebbPSF models appear to be
slightly more sharply peaked than the actual PSF of the images (i.e., the
modelled PSFs have more flux in the central regions) at the $\sim 2$% percent
level, which currently limit the relative precision of our photometry (see
Weisz et al., submitted for details).

Before running photometry, we masked out bad pixels, identified saturated
pixels, and applied pixel area maps using the routine nircammask included as
part of the DOLPHOT distribution. The photometric parameters used to run
DOLPHOT were chosen after extensive testing of the photometric recovery from
NIRCam (Weisz et al., submitted). The parameters are very similar to those
adopted for HST WFC3/IR imaging in the PHAT program (Williams et al., 2014).
These DOLPHOT parameters were optimized for the NIRCam imaging based on the
current PSF models and may need adjusting if the PSF model changes.

The photometry was filtered for well-recovered point sources. We required a
signal-to-noise ratio $\geq$4 in each filter, sharp2 $\leq$ 0.0225 in each
filter, and crowd $\leq$ 0.5 in each filter. The sharpness parameter is a
measure of how peaked or broad a source is relative to the PSF and helps to
reject image artifacts and background galaxies, respectively. The crowding
parameter measures how much brighter a source would have been if stars nearby
on the sky had not been fit simultaneously.

Figure 3: CMD from the NIRCam stellar catalog recovered from both modules
which includes close to 600,000 sources after applying our quality cuts.
Representative uncertainties per magnitude are shown on the right. The red
contours are a 2-D histogram of CMD regions with higher source densities;
lighter shades of red trace the peak concentration of sources. The CMD is
well-populated by stars in all major phases of evolution, providing an
excellent data set to test the SFH recovery from NIRCam data. The CMD is the
deepest obtained to date for any galaxy that is not in close proximity to the
Milky Way. The 50% completeness limit is marked with a dashed line; see Table
1 for values. Figure 4: CMDs of stars within the NIRCam - ACS matched field of
view. The NIRCam CMD (left) is $\sim$2 mag deeper than the ACS CMD (right) and
has a factor of $\sim 3$ more stars; 50% completeness limits are marked as
dashed lines on the respective CMD. Note that the completeness in the NIRCam
CMD is slighter deeper than the data from the full field of view shown in
Figure 3 due to the change in object density between Modules A and B. Similar
to Figure 3, the contours are a 2-D histogram of CMD regions with higher
source densities; lighter shades of color trace the peak concentration of
sources. While the color baseline of the F090W$-$F150W and F475W$-$F814W
filter combinations are similar, the features in the NIRCam CMD become
narrower as the near infrared wavelengths approach the Rayleigh-Jeans regime.
However, even with the narrower features in the F090W and F150W filters, the
different stellar evolution branches are well-separated in the NIRCam CMD.

We also ran a large number (2.3M) of artificial star tests (ASTs) per NIRCam
module to measure the bias and completeness of the data and the photometric
errors. While fewer ASTs (of order a few 100k) would have been sufficient for
our SFH-recovery purposes, we ran such a large number of ASTs for data
exploration and testing purposes for this first SFH fit with NIRCam data.
Artificial stars were injected throughout the images with a flat spatial
distribution (similar to the distribution of the stellar populations in each
NIRCam module) and then recovered with DOLPHOT using the same parameters set
for the photometry. We then applied the same quality cuts used for filtering
the photometry and marked any star that did not meet our quality requirements
as unrecovered. Based on the ASTs, the 50% completeness limits averaged over
the full NIRCam stellar catalogs are $m_{F090W}=29.23$ (M${}_{F090W}=+4.30$)
mag and $m_{F150W}=27.89$ (M${}_{F150W}=+2.96$) mag; these values are also
listed in Table 1.

Figure 3 presents the NIRCam CMD with close to 600,000 well-recovered point
sources; mean $1\,\sigma$ uncertainties per magnitude bin are shown on the
right. Stellar populations with a wide range of different ages and masses are
well-represented in the WLM field, creating a comprehensive CMD with various
branches of stellar evolution that can be compared with existing data and with
different stellar models. Specifically, the CMD includes younger stars in the
upper main sequence and blue/red helium burning sequences, intermediate and
older age stars on the red giant and asymptotic giant branches and in the red
clump, and older stars in the horizontal branch, sub-giant branch, and lower
main sequence.

###  2.3 Co-Spatial HST ACS stellar catalogs

The JWST NIRCam pointing intentionally targeted a field in WLM that overlapped
with existing deep HST ACS optical imaging https://doi.org/10.17909/gs15-rb46
(catalog DOI: 10.17909/gs15-rb46). The region in common between the two data
sets, seen in Figure 1, allows for a well-matched and detailed comparison of
the resolved star photometry recovered from each telescope and the SFHs
reconstructed separately from infrared vs. optical data. In addition, the
overlap in areal coverage also allowed us to perform a joint photometric
reduction of the JWST and HST data together and recover the SFH by
simultaneously fitting the infrared and optical CMDs. Here, we briefly
describe the reduction of the ACS data and the creation of stellar catalogs
from the NIRCam - ACS overlapping field of view; we describe the simultaneous
photometric reduction in Section 2.4.

The HST ACS observations were taken as part of HST-GO-13768, described in
detail in Albers et al. (2019), and include imaging in the F475W and F814W
filters. The data were processed with the same tools and methodology used on
the JWST data. Specifically, the HST-only photometry was performed on the HST
pipeline-processed flc.fits files using the ACS module of DOLPHOT with a F814W
drizzled image. The same quality cuts used for the NIRCam data were applied to
create the final ACS stellar catalog. These cuts are similar to those used on
the ACS data in Albers et al. (2019), except we use a slightly more stringent
cut on sharpness (which increases the purity of the catalog at the expense of
completeness). Approximately 500,000 ASTs were run on the ACS data in the
region overlapping with the NIRCam data.

From the full stellar catalogs of the NIRCam Module B (the only module that
overlapped with ACS) and the ACS data, we selected sources that lie within the
common footprint, excluding sources that fall in any of the chip gaps. Figure
4 presents the CMDs of these stars, with representative uncertainties per
magnitude bin. The photometry from both NIRCam and ACS reach below the oMSTO,
although, as expected given the differences in the telescopes and observing
strategies, the NIRCam data has a higher S/N at faint magnitudes.
Quantitatively, the 50% completeness limits for the NIRCam data are
$m_{F090W}=29.18$ (M${}_{F090W}=+4.25$) mag and $m_{F150W}=28.29$
(M${}_{F150W}=+3.36$) mag, compared with $m_{F475W}=28.28$
(M${}_{F475W}=+3.35$) mag and $m_{F814W}=27.20$ (M${}_{F814W}=+2.27$) mag for
the ACS data; the ACS values are also listed in Table 1. The completeness
limit for the F150W filter in this region of WLM is somewhat fainter than that
in the full NIRCam field of view, which reflects the overall lower object
crowding in this region (Weisz et al., submitted). This difference in crowding
has a greater impact on the F150W photometry due to the wavelength coverage
and the larger PSF in this bandpass relative to the F090W.

The total number of high-confidence stars recovered photometrically in the
NIRCam data is $\sim 3$ times higher than the number in the ACS data in the
matched field of view. The higher star counts reflect the higher completeness
of the NIRCam data at faint magnitudes where star counts naturally increase
for any typical initial mass function (IMF). It also reflects that the lower
main sequence stars are brighter in the near infrared than in the blue F475W
filter. For example, an F150W absolute magnitude of $+4.3$ corresponds to an
oMSTO star with a mass of 0.65 M⊙ (based on the PARSEC library) whereas in the
F475W filter this same absolute magnitude corresponds to a star with 0.72 M⊙.
At brighter magnitudes, where neither data set is significantly impacted by
incompleteness (e.g., F090W $<$ 26 mag), the number of recovered stars from
the two instruments are approximately equal. We compare the SFHs derived
separately from these catalogs in Section 4.3.

Besides photometric depth, completeness, and star counts, there are other
differences in the CMDs. Most notable are the slightly different CMD
architectures due to differences in the narrower CMD features in the near
infrared. While the width of the color baselines in the F090W$-$F150W and
F475W$-$F814W filter combinations are similar, the features in the NIRCam CMD
become narrower as the near infrared wavelengths approach the Rayleigh-Jeans
regime of a star’s spectral energy distribution for bluer sources.

###  2.4 Simultaneous Photometric Reduction of the NIRCam and ACS Images

We also performed simultaneous, cross-observatory PSF photometry using DOLPHOT
on the NIRCam and ACS data. We used the same methodology and parameters
described above, with the F090W i2d.fits file as the reference frame, the
cal.fits files as the science images for NIRCam, the flc.fits files for ACS,
and applied the same quality cuts to the output catalogs. For the ASTs, we
generated an input list of $\sim 1$M sources with realistic spectral energy
distributions across the wavelength range covered by the F475W - F150W
filters. We applied the same treatment to the ASTs as before. The stellar
catalogs are similar to those generated from the individual photometry runs,
but with slightly fewer stars passing the quality cuts. We recovered 177,000
stars in the NIRCam filters, compared with 202,000 in the NIRCam-only catalog,
and 58,000 in the ACS filters, compared with 62,000 in the ACS-only catalog.
The 50% completeness limits for the multi-observatory photometry are
$m_{F090W}=28.77$ mag, $m_{F150W}=28.09$ mag, $m_{F475W}=27.74$ mag, and
$m_{F814W}=26.86$ mag. These are somewhat brighter than the limits from
photometry performed on each data set separately, similar to what has been
noted when processing, for example, HST ACS and WFC3/IR data simultaneously
(Williams et al., 2014).

Figure 5: The NIRCam IR - ACS optical olor-color plane populated with point
sources recovered in both NIRCam filters (F090W, F150W) and both ACS filters
(F475W, F814W) from the cross-observatory photometric reduction. Dominant
features trace the main sequence, the red clump, and the RGB (see text for
details); the increase in photometric uncertainties for fainter sources in the
middle color-color range is also seen. The blue shaded region is where the
majority of foreground stars from the MW are expected to lie; as expected
given the high Galactic latitude, there is no obvious population of foreground
contaminants in the cleaned catalogs.

Figure 5 presents the NIRCam IR - ACS optical color-color plane with point
sources that are well-recovered in all four filters from the simultaneous
photometric reduction. The tightness of the stellar sequences in the color-
color emphasizes the high-fidelity of the photometry. Evident in the plot are
a number of features including: (i) the red clump over-density at F090W-F150W
$\sim 0.8$, F475W-F814W $\sim 1.3$; (ii), the tight RGB sequence at
F090W-F150W $\>0.8$, F475W-F814W $\>1.3$; (iii) the richly populated main
sequence at F090W-F150W $\lesssim 0.4$, F475W-F814W $\lesssim 0.6$; and (iv)
the photometric spread at fainter magnitudes at F090W-F150W $\sim 0.3-0.6$,
F475W-F814W $\sim 0.4-1.5$. We do not see a prominent galaxy sequence in the
color-color plot, which demonstrates the power of the high-resolution of
NIRCam and ACS imaging combined with accurate PSF photometry with DOLPHOT and
carefully selected quality cuts. The shaded blue at redder optical and IR
colors represents the region where, if present, the majority of foreground MW
stars would lie. Given the higher Galactic latitude of WLM (b$=-73^{\circ}$),
we do not expect a significant population of foreground stars, which is
confirmed by the low source counts in this region that are not coincident with
the RGB sequence or the few redder sources that are likely AGB stars. Note
that bluer, foreground dwarfs are predominantly brighter than the magnitude
ranges in our ACS and NIRCam CMDs and, therefore, not a concern.

##  3 The Star Formation History Methodology

###  3.1 CMD-fitting Technique

The SFHs and AMRs were derived using the numerical CMD-fitting software match
(Dolphin, 2002). match employs the well-established technique of comparing an
observed CMD to a grid of synthetic simple stellar populations (SSPs) of
different ages and metallicities (e.g., Tolstoy et al., 2009). The SSPs
generated assume an IMF, a binary fraction, and a set of stellar evolution
isochrones, and take into account photometric completeness determined from the
ASTs. The modelled CMDs are linearly combined until the best-fit to the
observed CMD is found based on a Poisson likelihood statistic.

The SFHs were derived using two stellar evolution libraries with solar-scaled
abundance patterns updated with NIRCam in-flight filter transmission curves
and Vega zeropoints, namely PARSEC (Bressan et al., 2012) and BaSTI (Hidalgo
et al., 2018). We assumed a Kroupa IMF (Kroupa, 2001) and a binary fraction of
35% with a flat mass ratio distribution for the secondaries. The SFH solutions
were fit with an age grid of log(t)$=6.6-10.15$ using a time resolution
log($\delta$t$)=0.1$ dex for ages less than log(t)$=9$ and
log($\delta$t$)=0.05$ dex for older ages, and a metallicity grid [M/H]$=-2.2$
to $-0.3$ with a resolution of 0.1 dex. The upper bound on the metallicity
range was chosen to be well-above the spectroscopically measured present-day
metallicity of [Fe/H] $\sim-0.8$ (Bresolin et al., 2006; Urbaneja et al.,
2008). We also enforced a restriction that the metallicity monotonically
increases in time. For the separate SFH fits to the NIRCam data that overlap
with the ACS field, we also required that the AMR from NIRCam matched the
best-fitting AMR from the ACS data, thereby providing a one-to-one comparison
of the recovered SFHs. We also experimented with fitting the NIRCam data
without requiring a matching AMR to the ACS data. The resulting NIRCam SFHs
were in excellent agreement with the results requiring a matched AMR. The
independently fit AMRs were also in good agreement with the ACS AMRs with the
initial and final values of [M/H] and overall shape of the AMR a close match
for each stellar library.

We limited the fitting to stars brighter than the 50% completeness limits in
each filter in a given CMD, as listed in Table 1. We note that models of the
AGB phase of stellar evolution are uncertain, but given the small number of
AGB stars in the data, the inclusion of these stars in the catalogs does not
impact the derived SFHs. Following the same procedure used in Albers et al.
(2019), we adopted the distance modulus of 24.93 mag measured from the TRGB
luminosity in the HST ACS data that overlap with our NIRCam field. We also
assumed the same foreground extinction value of $A_{V}=$0.10 mag based on
Galactic dust maps from Schlegel et al. (1998) with re-calibration by Schlafly
& Finkbeiner (2011), an $R_{V}=3.1$, and the reddening law of Fitzpatrick
(1999). WLM is at a high Galactic latitude where foreground contamination is
expected to be minimal, as demonstrated in Figure 5. Nonetheless, following
best practices, we included a model of foreground stars in the Milky Way in
the fits, generated with the TRILEGAL simulation tool (Girardi et al., 2012).
Confirming that foreground stars are minimally present in our stellar
catalogs, the SFH solutions with and without the inclusion of the foreground
model were identical.

Figure 6: Example fits to the NIRCam (subplots a, b) and ACS (subplots c, d)
stellar catalogs in the common field of view. For each data set, fits are
shown using PARSEC (subplots a, c) and BaSTI (subplots b, d) stellar
libraries. For each data set $+$ stellar library combination, we show four
sub-panels. The top two panels show the observed data in Hess diagram format
and the modelled Hess diagram. The bottom two panels show the residual (the
observed Hess diagram$-$model Hess diagram) and residual significance (defined
as the observed Hess diagram$-$model Hess diagram weighted by the variance in
each pixel). For direct comparison between fits, the residual significance
plots are fixed to the same colorbar range.

Statistical uncertainties due to the finite number of stars in the CMD were
estimated using a Hybrid Markov Chain Monte Carlo approach (Dolphin, 2013).
The SFH solutions derived with the two stellar libraries show the impact of
different assumptions used in the stellar models, and therefore give some
indication of the systematic uncertainties due to stellar evolution. However,
with only two libraries, these differences likely underestimate the true
systematic uncertainties of the solutions. Future work will include fits from
additional stellar libraries once they have been updated with the in-flight
performance of NIRCam, as well as systematic uncertainty estimates using Monte
Carlo simulations that are tuned based on solutions from multiple stellar
libraries (e.g., Dolphin, 2012). As a consistency check for our ACS SFH
solution, we compared our results to those derived from the full ACS field of
view presented in the Appendix of Albers et al. (2019), which includes fits to
the BaSTI and PARSEC stellar libraries used in the present work, as well as
the older Padua models (Girardi et al., 2010) and the MIST models (Choi et
al., 2016). We found the solutions to be in excellent agreement and well
within the uncertainties in Albers et al. (2019).

###  3.2 The Best-Fits to the Independent NIRCam and ACS CMDs

Figure 6 presents the CMD-fits to the stellar catalogs from the overlapping
region in the NIRCam and ACS data. These include independent solutions from
the NIRCam and ACS catalogs fit with the PARSEC and BaSTI stellar libraries.
Each data set$+$library solution has four subplots, as described in the figure
caption. For interpreting the quality of the fits, the most useful diagnostic
is the residual significance shown in the lower right for each solution. Note
that the scale of the significance color bar has been fixed to the same range
so differences between each data$+$model can be directly compared. While we
only show the results for the data in the overlapping region between NIRCam
and ACS, the general patterns in these residual significance plots are
representative of the solutions from the full NIRCam catalogs.

Overall, the modelled CMDs are a very good representation of the data, with
the exception of a few regions. Specifically, the red clump (RC) area is not
well-modelled, which has been noted in numerous previous studies with ACS
optical observations (e.g., Gallart et al., 2005). It is this area that sets
the extrema for the ranges in the residual significance. In addition, the
modelled blue helium burning (BHeB) sequences are somewhat redder (i.e.,
cooler) than the observed sequences. This has also been noted previously based
on optical observations (e.g., McQuinn et al., 2011), and the possible causes
of the disagreement have been explored theoretically (e.g., Tang et al.,
2014).

Smaller differences are seen between the models and the data for RGB stars.
The PARSEC and BaSTI models create a slightly redder (i.e., cooler) RGB
relative to both the NIRCam and the ACS data. A similar offset was reported at
the level of 0.05 mag based on the JWST ERS data on M92 (Weisz et al., 2023);
it has also been previously noted for a number of stellar models in near
infrared 2MASS broadband filters across a range of metallicities in Galactic
globular clusters (Cohen et al., 2015). The slight mismatches between model
and data have similar trends in the JWST and HST bands, suggesting there are
not inherent biases in using the JWST over HST bands for SFH fitting, although
the mismatches are somewhat more pronounced in the JWST residuals plots.

A color offset between the model and data can indicate the presence of
internal extinction that should be considered in the fits. As internal
extinction can be treated as a free parameter in match, we explored whether
adding extinction to the fits would improve the solutions. Briefly, we varied
extinction up to 0.50 mag, in increments of $A_{V}=0.05$ mag, and iteratively
re-fit the CMD with each stellar library. For the BaSTI models, extinction did
not reduce the residual significance in the fits. For the PARSEC models,
internal extinction of $\sim$0.30 ($\sim$0.15) mag compensated for the offset
in color between the modelled and observed RGB sequence for the NIRCam (ACS)
data, with slightly lower values in the residual significance. However, while
the SFH solutions with and without the extinction were consistent with each
other, the inclusion of internal extinction resulted in lower stellar
metallicities in the AMRs; these lower metallicities are a poor match to
spectroscopic abundances (see below), are inconsistent with the expectation of
minimal extinction in such a metal-poor galaxy (e.g., McQuinn et al., 2015a),
and are inconsistent with the lack of internal extinction found based on the
ACS data reported in Albers et al. (2019). Thus, for our final fits, while we
continued to include a foreground Galactic extinction of $A_{V}=0.10$ mag (see
§3.1), we did not adopt any internal extinction.

###  3.3 The Best-Fits to the Simultaneous NIRCam and ACS CMDs

We also used the photometry and ASTs run simultaneously on the JWST and HST
images to fit the NIRCam and ACS CMDs together. The modelled CMDs and
residuals between the observed and modelled data from both libaries were
qualitatively similar to the results from fitting the NIRCam and ACS data
separately. We show these results in the Appendix, which can be compared to
Figure 6. Qunatitatively evaluating the fits is not straightforward because,
on the one hand, using two CMDs in the fit increases the information available
to derive a SFH, but on the other hand, the fits are based on different
numbers of stars and the more complex data can disallow some solutions. One
basis for evaluation is the Poisson likelihood statistic returned by match.
Using this as a measure of the fit quality, we find the likelihood statistic
from modelling the 2-CMDs simultaneously was comparable to the combined values
from modelling each CMD independently using the PARSEC library, but slightly
worse based on BaSTI library. This would suggest that there are not obvious
advantages to using both NIRCam and ACS CMDs to recover a SFH over just a
single CMD. Another basis for evaluating the fits is to compare the best-
fitting SFHs and AMRs from each method, which are presented in Section 4.3.

##  4 The Star Formation History of WLM

###  4.1 SFH and AMR from the NIRCam data

Figure 7 presents the best-fitting cumulative SFHs (left) and AMRs (right)
from the NIRCam full field of view with solutions using the PARSEC (solid
lines) and BaSTI (dashed lines) stellar libraries. The top x-axis converts
lookback time to redshift based on the Planck Collaboration et al. (2020)
cosmology. Shading on the SFHs represent statistical uncertainties only. The
vertical gray region encompasses the approximate range of the epoch of
reionization from $z\sim 10-6$, corresponding to a lookback time from
13.3$-$12.9 Gyr.

The SFH and AMR results from both libraries are in excellent agreement with
one another. The differences between SFH solutions give some indication of the
systematic uncertainties, but additional stellar libraries with updated JWST
zeropoints are needed before the full range of systematic uncertainties can be
estimated. We also find that the total stellar mass formed based on the SFH
fits from each stellar library are in excellent agreement which provides an
additional quantitative measure of the SFH results. These values are listed in
Table 1.

The SFH of WLM (in the region imaged by NIRCam) shows that star formation
occurred at early times followed by an interruption post-reionization ($z\sim
6$; lookback time of 12.9 Gyr). This pause in star formation extended over
$\sim 3$ Gyr after which there was a marked increase in the star formation
activity. To provide a quantitative measure that can be compared with other
galaxies, we calculate the timescales “$\tau$” over which different fractions
of the stellar mass were formed. Focusing on just the PARSEC models for
simplicity and interpolating the SFH in Figure 7, we find that $\sim$10% of
the stellar mass formed within a lookback time $\tau_{10}$ $=12.61\pm 0.06$
Gyr, followed by a pause until $\sim$10 Gyr ago, and then the mass more than
doubles reaching $\tau_{25}$ at a lookback time $=7.63^{+0.05}_{-0.03}$ Gyr;
the uncertainties represent statistical uncertainties determined using a
Markov Chain Monte Carlo approach. The stellar mass then doubled again in
$\sim$2.5 Gyr with $\tau_{50}$ $=5.08^{+0.05}_{-0.01}$ Gyr. Finally, the
lookback time for 90% of the mass to form is $\tau_{90}$ $=1.18\pm 0.01$ Gyr.
We provide values of $\tau_{10}$, $\tau_{25}$, $\tau_{50}$, and $\tau_{90}$
for both the PARSEC and BaSTI models in Table 1. We discuss implications of
the extended pause in star formation post-reionization in Section 5.

Figure 7: Global cumulative SFH (top) and AMR (bottom) derived from NIRCam
imaging of the main stellar disk in WLM using the PARSEC (solid lines) and
BaSTI (dashed lines) libraries. Shaded regions represent the statistical
uncertainties on the fit based on the finite numbers of stars in the CMD. The
dotted black line in the top panel represents a constant star formation rate.
The earliest value shown for the AMR corresponds to the first time bin with a
stellar mass measurement.

###  4.2 Comparison of NIRCam-based AMRs with Spectroscopic MDFs

As a check on the fits, we compare the best-fitting AMR solutions from the
NIRCam data with trends based on spectroscopic abundances of stars of
different ages and from gas-phase abundances measured from Hii regions in WLM.
First, for a comparison of the AMR at intermediate ages, we use the
metallicity distribution reported in Leaman et al. (2009) from 44 RGB stars
that are spatially located in a similar region of the NIRCam data (labeled as
the ‘bar’ region by these authors). They find a mean metallicity [Fe/H]
$=-1.14\pm 0.04$ with an intrinsic spread of $\sigma=0.33$ dex based on
converting summed equivalent widths of calcium II triplet lines from medium
resolution (R $\sim$ 3400) spectroscopy to an [Fe/H] metallicity scale. This
is slightly higher than the mean [Fe/H] value of $-1.28,\sigma=0.38$ dex found
for a larger sample of 126 stars in WLM that cover the stellar disk out to
larger radii (Leaman et al., 2013). The mean [Fe/H] $=-1.14$ can be compared
with the [M/H]222Note that the spectroscopically measured metallicity
corresponds to the [Fe/H], while the AMR from CMD-fitting is an estimate of
the mean stellar metallicity quantity [M/H] that is used in stellar models.
range from $-1.2$ to $-0.8$ in the AMRs at intermediate times seen in Figure
7, which suggests general agreement between the spectroscopically-determined
stellar metallicity distribution function and AMR trends.

While a good general comparison, the AMRs are based on the composite stellar
populations, whereas the spectroscopic MDFs were created from stars in a
limited CMD region, namely stars that lie on the RGB and that are within
$\sim$1 mag below the TRGB. Thus, to compare the AMRs with the MDFs more
directly, we used the best-fitting SFH and AMR solutions to generate synthetic
photometry for each stellar library. We then selected stars that lie on the
RGB within $\sim 1$ mag of the TRGB and calculated the mean [M/H] values and
the standard deviations. Assuming a normal distribution, we find a
$\langle$[M/H]$\rangle=-0.94;\sigma=0.25$ dex for the BaSTI models and
$\langle$[M/H]$\rangle=-0.92;\sigma=0.22$ dex for the PARSEC models, in good
agreement with the $\langle$[Fe/H]$\rangle=-1.14;\sigma=0.33$ dex reported in
Leaman et al. (2009).

Figure 8 shows that the [M/H] distributions (top panel) and cumulative
distribution functions (bottom panel) from both libraries are consistent with
the [Fe/H] values for the 44 stars from Leaman et al. (2009). The largest
difference is the presence of a metal-poor tail in the spectroscopically
determined [Fe/H] values, which is absent in the AMR-based distribution
functions. We note that the lack of a metal-poor tail in the modeled
distributions is consistent with the corresponding lack of metal-poor blue
horizontal branch stars in both the ACS and the NIRCam CMDs. A two-sided
Kolmogorov–Smirnov (K–S) test performed on the cumulative metallicity
distribution functions from the Leaman et al. data and our values showed there
is 26% probability of the results being drawn from the same distribution for
the PARSEC models and 54% for the BaSTI models; the difference in the K-S
tests is likely driven by the different peaks and widths of the distributions,
which is seen most clearly in the normalized histograms in the top plot.
Regardless of the subtle differences, this is quite good agreement given the
somewhat different spatial regions sampled, relatively smaller number of
spectroscopic measurements, and the different techniques used in the
comparison.

Figure 8: Top panel: Comparison of the spectroscopically determined [Fe/H] MDF
from Leaman et al. (2009) with the [M/H] MDFs estimated from the SFH and AMR
derived with the PARSEC and BaSTI models, all normalized to total star counts.
Bottom panel: The same information shown as cumulative distribution functions
(CDFs). Based on 2-sample KS tests, the modelled CDFs have a high probability
of being drawn from the spectroscopic CDF. See text for details.

For a comparison of the AMR results at more recent times, we consider
spectroscopic measurements of young stars and the present-day gas abundance.
Specifically, the metallicity of 8 young A and B supergiant stars was reported
to have a mean [Fe/H] of $-0.87\pm 0.06$ (Urbaneja et al., 2008, see also
Bresolin et al. 2006). These values are in very good agreement with the
measured gas-phase oxygen abundance from Hii regions (e.g., 12$+$log(O/H)
$=7.83\pm 0.06$; Lee et al., 2005). We also note that there is evidence that
WLM hosts young stars with higher metallicities based on the study of two
supergiants (Venn et al., 2003) and one massive O star (Telford et al., 2021).
For comparison, the AMRs at the present day show [M/H] values ranging from
$-0.6$ to $-0.8$, depending on the stellar library, which is again in good
agreement with the spectra-based values.

Figure 9: SFHs (top) and AMRs (bottom) derived by fitting the NIRCam (red) and
ACS (blue) stellar catalogs from the overlapping region using the PARSEC
models (solid) and the BaSTI models (dashed). Also shown are the solutions
derived by simultanesouly fitting the NIRCam and ACS CMDs (green). The shaded
regions represent statistical uncertainties. The SFHs are all in excellent
agreement.

###  4.3 Comparison of NIRCam-based and ACS-based SFHs

Here, we focus on a quantitative comparison of the NIRCam- and ACS-based SFHs.
The fits are based solely on the stellar catalogs from the overlapping
footprint between NIRCam and ACS, as seen in Figure 1, rather than the
catalogs from the full field of view of either instrument.

Figure 9 presents the SFH (top) and AMR (bottom) solutions from NIRCam data
(red) and ACS data (blue) using the PARSEC (solid lines) and BaSTI (dashed
lines) models. Shaded regions represent statistical uncertainties on the fits.
The SFH results for NIRCam and ACS data from both the PARSEC and BaSTI
libraries are in excellent agreement with each other at all lookback times.
These results confirm the efficacy of the F090W - F150W filter combination and
stellar evolution libraries in the near-infrared for deriving SFHs relative to
the HST optical filters.

Figure 9 also shows the SFHs from the simultaneous fits to the NIRCam and ACS
CMDs (green lines). These solutions are also in excellent agreement with the
SFHs and AMRs derived independently from the NIRCam data and from the ACS data
(i.e., single CMD-fitting). The agreement indicates that the SFHs recovered by
fitting just one CMD in the optical or in the near-infrared are not
significantly biased compared to solutions recovered from simultaneously
fitting two CMDs that intrinsically contain more information, and demonstrates
a degree of robustness for the single CMD fit.

As a further comparison of the different SFH fits, we calculate the total
stellar mass recovered from the different data sets (NIRCam, ACS, NIRCAM+ACS)
and stellar libraries (PARSEC, BaSTI) in the overlapping field of view. The
values are in close agreement ranging from log($M_{*}$/M⊙) of 6.54 to 6.65,
providing additional evidence of the consistency between the SFH recovered
from the two observatories. See Table 1 for individual values.

Compared to the SFH results derived from the full NIRCam field of view (Figure
7), the SFHs from the overlapping NIRCam - ACS region (Figure 9) show a
slightly slower start to star formation. This suggests a lower median stellar
age at larger radii. Similarly, the AMRs in the overlapping NIRCam - ACS data
have slightly lower values than the AMRs from the full NIRCam field,
suggesting lower mean metallicities at larger radii.

Albers et al. (2019), who reconstructed the SFH from the full ACS data set,
also report the SFH from an outer field in WLM using deep HST data from the
WFC3 instrument. Compared with the SFHs from regions imaged with the ACS and
NIRCam, these results suggest a strong radial age gradient. Quantitatively,
the inner fields formed 50% of their stellar mass $\sim 5$ Gyr ago compared
with $\sim 10$ Gyr in the outer field located only $\sim 0.4$ kpc off the
minor axis. Future work from our team will combine the JWST NIRCam field, the
different HST fields, and the JWST NIRISS field observed as part of the ERS
program to explore this in greater detail (Roger Cohen et al. in preparation).

##  5 Discussion and Summary

###  5.1 On the Efficacy of NIRCam Imaging for SFH Work

In this work, we use deep JWST NIRCam imaging of the Local Group star-forming
galaxy, WLM, from the ERS-1334 program to derive the SFH and AMR of the
central field in the galaxy using two sets of stellar libraries (PARSEC,
BaSTI). We have leveraged existing optical imaging obtained with the HST ACS
instrument on an overlapping region (Albers et al., 2019) to perform a one-to-
one comparison of NIRCam-based and ACS-based SFH results. We find the SFHs and
AMRs derived independently and simultaneously fit using data from both great
observatories to be in excellent agreement for the two stellar libraries. In
addition, the NIRCam-based AMRs agree with spectroscopic metallicity
distribution functions, providing an independent check on the recovered
solutions.

This first detailed look at a SFH using NIRCam data confirms the efficacy of
recovering the SFH with the F090W - F150W filter combination relative to the
workhorse optical filters on HST that have been used for SFH work for the past
two decades. These results also provide validation of the sensitivity and
accuracy of stellar evolution libraries in the infrared relative to the
optical. With JWST’s greater sensitivity and resolution, detailed and robust
SFHs can be recovered with greater efficiency than has been possible with HST
and for galaxies at greater distances. It also validates that future SFH
results from JWST can be directly compared with existing SFH studies from HST
data without complications of significant biases due to observations from
these different observatories and wavelength coverage.

###  5.2 The Post-Reionization Pause in Star Formation in WLM

To date, only a few gas-rich isolated dwarf irregular (dIrr) galaxies have
robustly reconstructed ancient SFHs from deep CMDs in the literature. In order
of decreasing mass, these include IC 1613 (Skillman et al., 2003), WLM (Albers
et al., 2019, and the present work), Leo A (Cole et al., 2007), and Aquarius
(Cole et al., 2014). While the most massive of these galaxies (IC1613; $M_{*}$
$=1\times 10^{8}$ M⊙) shows relatively constant star formation across cosmic
time, the remaining three galaxies ($M_{*}$ $\sim 2\times 10^{6}$ to $4\times
10^{7}$ M⊙) exhibit some level of early star formation followed by a pause
that occurs post-reionization. These galaxies hosted a similarly early onset
of star formation but then a temporary quiescent period ensues as, presumably,
the metal-poor gas present in and/or accreted onto the galaxies was prevented
from cooling efficiently, with myriad heating sources (e.g., cosmological
reionization, the local far-UV radiation field, heating from internal stellar
feedback, etc.) potentially playing important roles.

Although this is a very small sample, the emerging pattern is intriguing,
suggesting that even in the stellar mass range of $10^{6}-10^{7}$ M⊙, galaxies
experience a post-reionization pause in star formation. Similarly deep CMDs of
additional isolated dIrr galaxies are needed to confirm and further
investigate this trend. JWST is currently the only facility with the
capabilities to increase the sample size of isolated galaxies (i.e., galaxies
outside the Local Group) with robust ancient SFHs.

This work is based in part on observations made with the NASA/ESA/CSA James
Webb Space Telescope. The data were obtained from the Mikulski Archive for
Space Telescopes at the Space Telescope Science Institute, which is operated
by the Association of Universities for Research in Astronomy, Inc., under NASA
contract NAS 5-03127 for JWST. These observations are associated with program
ERS-1334. This research is also based in part on observations made with the
NASA/ESA Hubble Space Telescope obtained from the Space Telescope Science
Institute, which is operated by the Association of Universities for Research
in Astronomy, Inc., under NASA contract NAS 5–26555. These observations are
associated with program HST-GO-13678. Support for this work was provided by
NASA through grants ERS-1334 from the Space Telescope Science Institute, which
is operated by AURA, Inc., under NASA contract NAS5-26555. This research has
made use of NASA Astrophysics Data System Bibliographic Services and the
NASA/IPAC Extragalactic Database (NED), which is operated by the Jet
Propulsion Laboratory, California Institute of Technology, under contract with
the National Aeronautics and Space Administration. LG and AM acknowledge
financial support from Padova University, Department of Physics and Astronomy
Research Project 2021 (PRD 2021). For completeness, in Figure 10 we present
the results from simultaneously fitting the NIRCam and ACS CMDs. The figure
has the same format as Figure 6: the top panels show the fits to the NIRCam
data, the bottom panels show the fits to the ACS data, with the PARSEC models
(left) and the BaSTI models (right). The patterns between the observed and
modelled CMDs seen in residual significance plots (lower right panels in each
sub-plot) are quite similar to those from the single CMD fits shown in Figure
6.

Figure 10: Results from the simultaneous fits to the NIRCam (subplots a, b)
and ACS (subplots c, d) stellar catalogs in the common field of view. The
figure format is the same as shown in Figure 6.

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