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The HST Legacy Archival Uniform Reduction of Local Group Imaging (LAURELIN). I. Photometry and Star Formation Histories for 36 Ultra-faint Dwarf Galaxies

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Pith's one-line read By uniformly reducing deep space-based imaging of 36 ultra-faint dwarf galaxies, this paper finds they stopped forming stars about 12.5 billion years ago, near the end of reionization, with a hint that LMC and first-infall satellites…

desk verdict Solid, useful data-release paper for UFD SFHs; the body's 5σ environmental delay is not robust and should be downgraded to the abstract's 2σ upper limit. read the letter →

arxiv 2505.18252 v2 pith:H75DPMTX submitted 2025-05-23 astro-ph.GA

classification astro-ph.GA
keywords DwarfgalaxiesGalaxyevolutionstellarcontentLocalGroupReionizationStarformationhistoriesquenchingtime
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that the star formation histories of 36 ultra-faint dwarf galaxies, measured in a single uniform way from deep space-based imaging, all end at roughly the same ancient epoch: on average, 80% of their stellar mass was in place $12.48 \pm 0.18$ Gyr ago, corresponding to $z \approx 4.6$ and matching the end of cosmic reionization in standard cosmology. It also tries to show that the few galaxies now linked to the Large Magellanic Cloud or on first infall into the Milky Way quenched later than long-term Milky Way satellites, with the delay capped at about 800 Myr at $2\sigma$ significance. A third, practical claim is that reliable star formation histories from the ancient main-sequence turnoff require an effective luminosity of $M_{V,\mathrm{eff}} \leq -2.5$, about 100 stars at the turnoff, and that pushing signal-to-noise above roughly 100 does not buy additional precision. A reader should care because these smallest galaxies are the most direct surviving record of how reionization switched off star formation in the lowest-mass dark matter halos.

What carries the argument

The object that carries the argument is the ancient main-sequence turnoff (MSTO) region of each galaxy's color-magnitude diagram: the count and color spread of stars around the turnoff encode the age distribution of the oldest stellar populations. The pipeline is uniform resolved-star photometry in two filters, followed by maximum-likelihood fitting of synthetic Hess diagrams with stellar models that cover the extremely low metallicities of ultra-faint dwarfs, including simple-stellar-population fits to fix distance and foreground dust per galaxy and additive models for foreground stars and background galaxies. Quenching times are read off the cumulative star formation histories: $\tau_{80}$ is the lookback time at which the cumulative stellar mass fraction reaches 0.8, and uncertainties come from Hamiltonian Monte Carlo sampling of the fit.

What would settle it

Recompute the inverse-variance-weighted group averages after removing the single most ancient, highest-weight long-term Milky Way galaxy (Boo I): if the $5\sigma$ delay drops to roughly $2\sigma$ or below, the environmental signal rests on one object. Alternatively, measure $\tau_{80}$ for a sample of isolated field ultra-faint dwarfs beyond the Local Group with $M_{V,\mathrm{eff}} \leq -2.5$; if they quench as late as the LMC and first-infall groups, the modern kinematic grouping does not trace the reionization-era environment and the delay is not a patchy-reionization signature.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central result is an ensemble quenching epoch: defining quenching as the lookback time by which 80% of the stellar mass formed ($\tau_{80}$), the 36 galaxies quench at an average $12.48 \pm 0.18$ Gyr ago, or $z = 4.6^{+0.6}_{-0.5}$ in $\Lambda$CDM, which the authors identify as consistent with reionization-driven suppression of star formation. The secondary result is environmental: ultra-faint dwarfs classified as LMC satellites or first-infall objects show a quenching delay of about 800 Myr or less relative to long-term Milky Way satellites at $2\sigma$ significance in the fiducial analysis, with inverse-variance-weighted averages producing larger differences (up to $5\sigma$) that depend strongly on one ancient, high-weight galaxy. The paper also establishes a practical limit: below $M_{V,\mathrm{eff}} \approx -2.5$, corresponding to roughly 100 main-sequence-turnoff stars, the stochastic sparsity of the stellar population dominates and per-object star formation histories lose the precision needed to test patchy reionization.

Load-bearing premise

The interpretation of the 800 Myr delay as evidence of patchy reionization assumes that a galaxy's current kinematic classification (long-term Milky Way satellite, first infall, or LMC satellite) reflects the density of its environment at the time of reionization, an assumption the paper itself says is not well constrained.

Editorial extensions

If this is right

  • If the average quenching time is correct, the ultra-faint dwarfs as a population are direct fossils of reionization: their star formation ended just as the intergalactic ultraviolet background finished reionizing the Local Group volume.
  • The roughly 800 Myr upper limit on the quenching delay sets the scale of the patchy-reionization signal that future samples must either confirm as real or rule out as a small-sample artifact.
  • The $M_{V,\mathrm{eff}} \leq -2.5$ threshold gives survey designers a selection rule: fainter systems cannot deliver per-object star formation histories precise enough for quenching-time differences, so future programs should target the roughly 100-turnoff-star regime.
  • Because precision saturates at a signal-to-noise ratio near 100 at the turnoff, investing in wider footprints rather than deeper integrations is the higher-yield strategy for the faintest galaxies.
  • The public photometry catalogs, with distances and extinctions measured on the same system, allow any group to reproduce the ensemble averages and test alternative grouping schemes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The delay signal's physical interpretation stands or falls with the assumption, which the paper explicitly flags, that today's kinematic classes trace reionization-era environments; if they do not, the 800 Myr difference measures something about orbital histories rather than patchy reionization.
  • A natural extension the paper leaves implicit is to apply the same uniform pipeline to isolated field ultra-faint dwarfs outside the Local Group; if isolated systems quench as late as the LMC and first-infall groups, the environmental interpretation would need revision.
  • The gap between the $2\sigma$ unweighted result and the $5\sigma$ weighted result implies that the true significance of the delay depends on how much weight one gives to the oldest, most precisely measured long-term Milky Way satellites; re-deriving the averages with a jackknife over those galaxies would be a cheap, decisive robustness test.
  • Future wide-field space telescopes with the same filters could enlarge the LMC and first-infall samples from three to five objects to tens, which is the direct way to beat the stochastic noise that currently caps the precision.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper presents a homogeneous reduction of HST ACS/WFC F606W/F814W imaging for 36 ultra-faint dwarf galaxies, producing public photometric catalogs, artificial-star-based completeness, SSP distance and extinction measurements, and MATCH-based star formation histories. The main scientific results are an ensemble mean quenching time of 12.48 ± 0.18 Gyr ago (defined as the lookback time by which 80% of stellar mass formed), a comparison of quenching times among kinematic groups (long-term Milky Way satellites, first-infall systems, and LMC satellites), and a recommended threshold of M_V,eff ≤ −2.5 (about 100 MSTO stars) for robust MSTO-based SFH measurement. The paper also validates its distances against RR Lyrae distances for 18 galaxies and compares its SFHs with previous literature measurements.

Significance. If the results hold, this paper provides a valuable homogeneous legacy dataset for Local Group science: uniformly reduced photometry and SFHs for 36 UFDs, a practical design threshold for future MSTO-based SFH programs, and an independent empirical anchor for reionization-quenching scenarios. The distance validation against RR Lyrae stars is a particular strength, as is the public data release and the explicit discussion of which galaxies yield reliable SFHs. The central astrophysical claims are, however, more modest than the 5σ language in parts of the paper: the robustly supported result is an approximately 2σ, ≲800 Myr delay, which the abstract and conclusions state correctly. The paper's significance therefore rests on the uniformity and legacy value of the data products, and on the ensemble quenching time, more than on the environmental delay claim.

major comments (4)
  1. [§4.3, Table 4] The claim of a quenching delay 'at up to 5σ significance' is not robust as presented. Table 4 gives a weighted long-term MW τ80 of 13.38 ± 0.05 Gyr, and this value is dominated by Boötes I, whose Table 3 τ80 is 13.40+0.00−0.06, with the zero upper uncertainty reflecting truncation at the 13.8 Gyr grid boundary. Because §3.3 states that only random MCMC uncertainties are used and no systematic floor is applied, the inverse-variance weighting over-credits boundary-pinned ancient systems. Adding a conservative 0.3–0.5 Gyr systematic floor to each galaxy's σ(τ80) would substantially reduce Boo I's weight and move the weighted mean toward the unweighted value, dropping the reported significances to roughly 2σ. I request that the authors either remove or explicitly qualify the 5σ statement, or recompute the weighted averages with a systematic floor and report the resulting significances.
  2. [§3.3, Figure 9, Table 3] The absolute quenching time of 12.48 ± 0.18 Gyr is quoted with statistical errors only, while Table 3 shows many τ80 uncertainties with +0.00 upper errors (e.g., Boo I, Sag II, ComBer), indicating fits pinned to the oldest grid edge. In addition, Figure 6 reports no formal uncertainties for the SSP-based distances, and those distance uncertainties are not propagated into the SFHs. Because the fits also assume a fixed 10–13.8 Gyr age range, BaSTI models, a Kroupa IMF, a 0.35 binary fraction, and a monotonic age–metallicity relation, the absence of systematic uncertainties makes the precision of the ensemble mean potentially misleading. I ask the authors to quantify the sensitivity of τ80 to these choices for at least a subset of galaxies, or to re-label the 12.48 ± 0.18 Gyr value explicitly as a model-dependent estimate whose systematic error is not yet evaluated.
  3. [Abstract and §5 vs. §4.3 and Table 4] The abstract and conclusions state a delay of '≲800 Myr at 2σ', but §4.3 reports culled weighted differences of Δτ80 = 1.65 ± 0.31 Gyr for long-term MW versus LMC and 1.33 ± 0.41 Gyr for long-term MW versus first-infall. These statements are mutually inconsistent as written: a measured 1.65 Gyr delay cannot simultaneously be an upper limit of 800 Myr. Please clarify which estimator (unweighted vs. weighted, culled vs. full sample) underlies the headline 800 Myr bound, and state explicitly whether this is an upper limit on the true physical delay or on the detectable signal after accounting for the systematic and grouping caveats acknowledged in §4.3.
  4. [§4.3] The interpretation of the quenching delay as an environmental signal depends on the premise that today's kinematic groups (long-term MW, first infall, LMC) trace different large-scale density environments at the epoch of reionization. The paper explicitly acknowledges in §4.3 that 'the orbital histories are not well-constrained back to the reionization-era' and that some simulations place all present-day UFDs in comparably low-density regions. Since this premise is not independently tested in the paper, the delay should be presented as conditional on that assumption. If the authors wish to retain the stronger environmental interpretation, they should add a robustness test using alternative group assignments, or show explicitly how the inferred delay changes under plausible reionization-era orbit scenarios.
minor comments (5)
  1. [§2.1] The phrase 'of of' appears in the sentence 'spanning an absolute V magnitude range of of −7.1 ≤ M_V ≤ +0.0'; please correct the typo.
  2. [§4.3] The word 'unceratinties' in the discussion of unweighted averages is a typo for 'uncertainties'; please correct it.
  3. [Table 3] Many τ80 entries have +0.00 upper uncertainties, which readers will immediately recognize as grid-boundary truncation; a table note explicitly stating that +0.00 upper errors indicate the 13.8 Gyr fitting boundary would improve clarity and prevent misinterpretation of these as genuinely zero upper uncertainty.
  4. [Figure 6 and §3.3] The caption of Figure 6 correctly states that no formal uncertainties are calculated for the SSP distances, but the text in §3.3 does not explain how distance or extinction uncertainties enter the SFH uncertainties; a sentence on whether these are propagated, marginalized, or ignored would help the reader interpret the reported 68% intervals.
  5. [§4.2] The comparison with Brown+14 notes different adopted oldest-age limits (14.1 vs. 13.7 Gyr) but does not state whether this could bias the τ80 comparison for the six galaxies in common; a brief comment on the expected size of this effect would be useful.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: quenching times are CMD-fit outputs benchmarked against independent RR Lyrae distances and literature SFHs.

full rationale

The paper's central quantities (distances, extinctions, SFHs, tau80 values, ensemble averages, and quenching delays) are all outputs of a uniform CMD-fitting pipeline (DOLPHOT/MATCH/BaSTI), not inputs. The distance and extinction fits in Section 3.1 assume ancient SSP ages, but they are validated against independent RR Lyrae distances for 18 of 36 galaxies, with |Delta mu| <= 0.02 mag on average (Section 3.1 and Figure 6), and the SFH fits themselves allow ages from 6 to 13.8 Gyr (Section 3.3), so the resulting quenching times are not forced by the distance prior. The ensemble quenching time of 12.48 +/- 0.18 Gyr and the kinematic-group delays are compared against, and found consistent with, independent external measurements (Brown et al. 2014; Sacchi et al. 2021; Simon et al. 2021, 2023; Gallart et al. 2021), rather than being derived from those papers. Kinematic group assignments come from Gaia-proper-motion orbital studies (Kallivayalil et al. 2018; Patel et al. 2020; Pace et al. 2022) and are external to the SFH fits; the paper explicitly acknowledges that mapping present-day orbits to reionization-era environments is an assumption and uncertain (Section 4.3). The self-citations are to standard methodology (MATCH, Savino et al. 2023, 2025) and prior comparison samples; none functions as an unverified uniqueness theorem or a fitted input renamed as a prediction. The paper explicitly flags missing systematic uncertainties ('We do not compute systematic uncertainties', Section 3.3) and the kinematic-to-environment caveat ('the orbital histories are not well-constrained back to the reionization-era', Section 4.3); these are honest limitations that affect robustness, not circular steps. The body transparently reports the weighted/unweighted discrepancy and settles on a 2-sigma upper limit (about 800 Myr) in the abstract and conclusions, so the 5-sigma weighted value is presented as method-dependent rather than a separate predicted quantity. No load-bearing step reduces by construction to its own inputs.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claim rests on standard resolved-stellar-population tools (DOLPHOT, MATCH, BaSTI) and on two domain assumptions that are partially acknowledged in the paper: that the stellar models are correct at very low metallicity, and that current kinematic grouping reflects reionization-era environments. The main free parameters are the per-galaxy distance, extinction, and the SFH mass fractions themselves, all fitted to the CMDs. No new physical entities are introduced.

free parameters (5)
  • Distance modulus per galaxy (μ) = 16.58 to 22.65 mag (Table 2)
    Fitted by MATCH SSP grid search over ±0.4 mag around literature values; used to convert photometry to absolute magnitudes and SFHs.
  • Foreground extinction per galaxy (AV) = 0.01 to 0.50 mag (Table 2)
    Fitted via SSP grid search between limits set by SFD98/S&F11 and Delchambre+23 dust maps.
  • SSP age and metallicity per galaxy = log t 10.08-10.13, [Fe/H] -3.1 to -1.5 (Table 2)
    Grid search over age 10-13.8 Gyr and [Fe/H] -3.2 to -1.4; these are the fiducial SSP parameters used to set distance and extinction.
  • Foreground/background scaling factors = Free in CMD fit
    MATCH allows linear scaling of foreground and background CMD components to vary freely for each galaxy.
  • SFH age-bin mass fractions = 36 galaxies x ~36 age bins
    The primary output of MATCH; τ80 and τ90 are derived from these fitted mass fractions.
assumptions (6)
  • domain assumption BaSTI stellar models accurately represent ancient, extremely metal-poor ([Fe/H] < -2) populations with [α/Fe]=0.4
    Used throughout for SSP distance fits and SFH fitting (§3.1, §3.3). If the models' age-color calibration is wrong at low metallicity, all ages shift.
  • domain assumption The MATCH maximum-likelihood CMD fitting, with Poisson statistics and the -zinc monotonic age-metallicity constraint, produces unbiased SFHs
    The -zinc option imposes a monotonically increasing metallicity with time, mitigating age-metallicity degeneracy but assuming chemical evolution (§3.3).
  • domain assumption IMFs and binary parameters: Kroupa IMF (0.08-120 M⊙), binary fraction 0.35, metallicity dispersion 0.2 dex
    Fixed inputs to the stellar population models (§3.3). Incorrect binary fraction or IMF slope would bias mass fractions and thus τ80.
  • domain assumption The kinematic groups (long-term MW, first infall, LMC) inferred from Gaia proper motions trace distinct reionization-era environments
    Load-bearing for the environmental delay claim (§4.3). The paper acknowledges this assumption is uncertain.
  • domain assumption The age grid upper limit of 13.8 Gyr and the F606W-F814W filter pair allow the true star formation history of UFDs to be recovered
    Many galaxies have τ80 near the grid boundary (e.g., Boo I τ80 = 13.40); the narrow color baseline of F606W-F814W is suboptimal for metallicity/age separation (§3.3, §4.1).
  • domain assumption The RR Lyrae distance comparison validates the SSP distances
    Used to argue distances are accurate to ≤2% (§3.1), but scatter is ~0.2 mag and no formal uncertainties are assigned to the SSP distances.

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Cite this review

Pith. "Pith review of The HST Legacy Archival Uniform Reduction of Local Group Imaging (LAURELIN). I. Photometry and Star Formation Histories for 36 Ultra-faint Dwarf Galaxies." pith.science (2026). https://pith.science/paper/H75DPMTX

@misc{pith2026250518252,
  author       = {Pith},
  title        = {Pith review of: The HST Legacy Archival Uniform Reduction of Local Group Imaging (LAURELIN). I. Photometry and Star Formation Histories for 36 Ultra-faint Dwarf Galaxies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H75DPMTX}},
  note         = {Machine review of arXiv:2505.18252}
}
abstract

We present uniformly measured resolved stellar photometry and star formation histories (SFHs) for 36 nearby ($\lesssim$ 400 kpc) ultra-faint dwarf galaxies (UFDs; $-7.1 \le M_V \le +0.0$) from new and archival HST imaging. We measure homogeneous distances to all systems via isochrone fitting and find good agreement ($\le$ 2%) for the 18 UFDs that have literature RR Lyrae distances. From the ensemble of SFHs, we find: (i) an average quenching time (here defined as the lookback time by which 80% of the stellar mass formed, $\tau_{80}$) of 12.48 $\pm$ 0.18 Gyr ago ($z = 4.6_{-0.5}^{+0.6}$), which is compatible with reionization-based quenching scenarios; and (ii) modest evidence of a delay ($\lesssim$ 800 Myr) in quenching times of UFDs thought to be satellites of the LMC or on their first infall, relative to long-term Galactic satellites, which is consistent with previous findings. We show that robust SFH measurement via the ancient main sequence turnoff (MSTO) requires a minimum effective luminosity (i.e., luminosity within the observed field of view) of $M_V \leq -2.5$, which corresponds to $\sim$100 stars around the MSTO. We also find that increasing the S/N above $\sim$100 at the MSTO does not improve SFH precision, which remains dominated by stochastic effects associated with the number of available stars. A main challenge driving the precision of UFD SFHs is limitations in the accuracy of foreground dust maps. We make all photometry catalogs public as the first data release of a larger HST archival program targeting all dwarf galaxies within $\sim$1.3 Mpc.

Figures

Figures reproduced from arXiv: 2505.18252 by the authors.

Figure 1
Figure 1. Size-magnitude relation for known Local Group dwarf galaxies within 1.5 Mpc. Filled blue points are the galaxies used in this work, and open orange circles are other local galaxies not studied here, either because they are too massive to be considered UFDs or because they lack HST imaging. Data obtained from the Local Volume Database (A. B. Pace 2024). and GO-12549 (PI Brown, 6 galaxies). Other archival programs inc… view at source ↗
Figure 2
Figure 2. Footprints of all HST observations used in this work (blue filled patches) overlaid on DSS2 imaging cutouts. Open black ellipses show the galaxy profiles at one half-light radius. the number of star-finding iterations, which is set by SecondPass.) We adopt somewhat stricter quality cuts on our pho￾tometry than the GST (“good star”) cuts applied to PHAT & PHATTER, as our data have much lower crowding. These cuts, app… view at source ↗
Figure 3
Figure 3. Application of successive culling criteria to CMDs of Hercules, with the fourth panel showing the final version of the catalog used in the remainder of this work. The stellar population signal of the galaxy remains constant throughout, whereas noise, artifacts, and contaminant populations are dramatically reduced. tributions of the aforementioned quality metrics. In our case, we require: • Sharpness2 + Roundness2 + … view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Color-magnitude diagrams of all galaxies, with all photometric quality and membership cuts applied. The integrated magnitudes in each panel reflect the effective luminosity, i.e., the luminosity within HST’s field of view for each galaxy [PITH_FULL_IMAGE:figures/full_…
Figure 5
Figure 5. Figure 5: As [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Comparisons of our SSP-based distance and ex￾tinction measurements with literature values. Upper panel: comparison of our distance moduli with RR Lyrae results from P. Nagarajan et al. (2022, blue triangles) and A. K. Vivas et al. (2020, orange inverted triangles). Err…
Figure 7
Figure 7. Figure 7: Example foreground and background components for Hercules, shown as blue density maps with scatter plots of the CMD overlaid. The color-magnitude box we use for SFH fitting is outlined in black on each panel. In this case, the NOIRLab Source Catalog (NSC, left panel) i…
Figure 8
Figure 8. Figure 8: Example MSTO fitting for Hercules. Upper row, left to right: observed and modeled Hess diagrams, and the unbinned CMD for reference. Lower row: Hess diagram residuals (observed – modeled) and residual significance, and bins with residual significance over 3σ. The compl…
Figure 9
Figure 9. Figure 9: Best-fit SFHs for all UFDs (blue lines) with 68% confidence intervals (light blue shaded regions). The epoch of reionization is marked with the gray vertical bands (12.87 – 13.33 Gyr ago in ΛCDM; Planck Collaboration et al. 2020; M. Boylan-Kolchin & D. R. Weisz 2021). …
Figure 10
Figure 10. Figure 10: Error on τ80 (half the width of the 68% confidence interval) against the effective V magnitude of the HST observations, MV eff = MV − 2.5 log10(f⋆) (left), and the number of stars within ±1 mag of the F606W oMSTO (right; 2.5 < MF606W < 4.5), colored by S/N at the F606…
Figure 11
Figure 11. Figure 11: Comparison of the SFHs derived in this work (blue) with the SFHs reported by T. M. Brown et al. (2014, gray patches). We find excellent agreement in most cases, with only minor exceptions. = +0.4 (D. A. VandenBerg et al. 2014a,b), and scaled￾solar MIST (A. Dotter 2016…
Figure 12
Figure 12. Figure 12: As [PITH_FULL_IMAGE:figures/full_fig_p017_12.png]
Figure 13
Figure 13. Figure 13: Average SFHs by kinematic group for our full galaxy sample (left column) and galaxies with MV eff less than −2.5 (right column), computed as simple averages (upper row) and inverse variance-weighted averages (lower row). We see significant differences in τ80 and τ90 (…

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Reference graph

Works this paper leans on

155 extracted references · 16 canonical work pages · cited by 3 Pith papers

  1. [1]

    2022,, Instrument Science Report WFC3 2022-5, 55 pages

    Anderson, J. 2022,, Instrument Science Report WFC3 2022-5, 55 pages

  2. [2]

    Anderson, J., & King, I. R. 2000, PASP, 112, 1360, doi: 10.1086/316632

  3. [3]

    Anderson, J., & King, I. R. 2006,, Instrument Science Report ACS 2006-01 21 http://americano.dolphinsim.com/dolphot/dolphot.pdf Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-388...

  4. [4]

    2018, ApJL, 856, L22, doi: 10.3847/2041-8213/aab14d 22 Durbin et al

    Aubert, D., Deparis, N., Ocvirk, P., et al. 2018, ApJL, 856, L22, doi: 10.3847/2041-8213/aab14d 22 Durbin et al. T able 5.Description of columns in the DOLPHOT photometric catalogs. Column Type Unit Description ID str Unique source identifier within the field RA float32 deg Right Ascension (ICRS) DEC float32 deg Declination (ICRS) X float32 pix X position...

  5. [5]

    J., Hack, W., Cara, M., et al

    Avila, R. J., Hack, W., Cara, M., et al. 2015, in Astronomical Society of the Pacific Conference Series, Vol. 495, Astronomical Data Analysis Software an Systems XXIV (ADASS XXIV), ed. A. R. Taylor & E. Rosolowsky, 281, doi: 10.48550/arXiv.1411.5605

  6. [6]

    2017,, Instrument Science Report WFC3 2017-19, 4 pages

    Bajaj, V. 2017,, Instrument Science Report WFC3 2017-19, 4 pages

  7. [7]

    2020, in American Astronomical Society Meeting Abstracts, Vol

    Bajaj, V., & Anderson, J. 2020, in American Astronomical Society Meeting Abstracts, Vol. 235, American Astronomical Society Meeting Abstracts #235, 109.07

  8. [8]

    F., & Fritz, T

    Battaglia, G., Taibi, S., Thomas, G. F., & Fritz, T. K. 2022, A&A, 657, A54, doi: 10.1051/0004-6361/202141528

Show all 155 references
  1. [9]

    2015, ApJ, 807, 50, doi: 10.1088/0004-637X/807/1/50

    Bechtol, K., Drlica-Wagner, A., Balbinot, E., et al. 2015, ApJ, 807, 50, doi: 10.1088/0004-637X/807/1/50

  2. [10]

    D., Bolton, J

    Becker, G. D., Bolton, J. S., Madau, P., et al. 2015, MNRAS, 447, 3402, doi: 10.1093/mnras/stu2646

  3. [11]

    Lacey, C. G. 2003, MNRAS, 343, 679, doi: 10.1046/j.1365-8711.2003.06709.x

  4. [13]

    2007, ApJ, 668, 949, doi: 10.1086/521385 Photometry and SFHs for 36 UFDs 23

    Besla, G., Kallivayalil, N., Hernquist, L., et al. 2007, ApJ, 668, 949, doi: 10.1086/521385 Photometry and SFHs for 36 UFDs 23

  5. [14]

    2015, ApJ, 807, 154, doi: 10.1088/0004-637X/807/2/154

    Bland-Hawthorn, J., Sutherland, R., & Webster, D. 2015, ApJ, 807, 154, doi: 10.1088/0004-637X/807/2/154

  6. [15]

    S., & Ricotti, M

    Bovill, M. S., & Ricotti, M. 2009, ApJ, 693, 1859, doi: 10.1088/0004-637X/693/2/1859

  7. [16]

    Boylan-Kolchin, M., & Weisz, D. R. 2021, MNRAS, 505, 2764, doi: 10.1093/mnras/stab1521

  8. [17]

    R., Bullock, J

    Boylan-Kolchin, M., Weisz, D. R., Bullock, J. S., & Cooper, M. C. 2016, MNRAS, 462, L51, doi: 10.1093/mnrasl/slw121

  9. [18]

    A., & Veljanoski, J

    Breddels, M. A., & Veljanoski, J. 2018b, A&A, 618, A13, doi: 10.1051/0004-6361/201732493

  10. [19]

    M., Tumlinson, J., Geha, M., et al

    Brown, T. M., Tumlinson, J., Geha, M., et al. 2014, ApJ, 796, 91, doi: 10.1088/0004-637X/796/2/91

  11. [20]

    2019, MNRAS, 483, 1314, doi: 10.1093/mnras/sty2913

    Frings, J. 2019, MNRAS, 483, 1314, doi: 10.1093/mnras/sty2913

  12. [21]

    S., Kravtsov, A

    Bullock, J. S., Kravtsov, A. V., & Weinberg, D. H. 2000, ApJ, 539, 517, doi: 10.1086/309279

  13. [22]

    Strigari, L. E. 2010, ApJ, 710, 408, doi: 10.1088/0004-637X/710/1/408

  14. [23]

    L., Sand, D

    Carlin, J. L., Sand, D. J., Mu˜ noz, R. R., et al. 2017, AJ, 154, 267, doi: 10.3847/1538-3881/aa94d0

  15. [24]

    C., Magnier, E

    Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, arXiv e-prints, arXiv:1612.05560, doi: 10.48550/arXiv.1612.05560

  16. [25]

    P., et al

    Cignoni, M., Sabbi, E., van der Marel, R. P., et al. 2015, ApJ, 811, 76, doi: 10.1088/0004-637X/811/2/76

  17. [26]

    J., Fouesneau, M., Hogg, D

    Dalcanton, J. J., Fouesneau, M., Hogg, D. W., et al. 2015, ApJ, 814, 3, doi: 10.1088/0004-637X/814/1/3 D’Aloisio, A., McQuinn, M., & Trac, H. 2015, ApJL, 813, L38, doi: 10.1088/2041-8205/813/2/L38

  18. [27]

    R., Ocvirk, P., et al

    Dawoodbhoy, T., Shapiro, P. R., Ocvirk, P., et al. 2018, MNRAS, 480, 1740, doi: 10.1093/mnras/sty1945 de Jong, J. T. A., Rix, H. W., Martin, N. F., et al. 2008, AJ, 135, 1361, doi: 10.1088/0004-6256/135/4/1361 de Jong, J. T. A., Yanny, B., Rix, H.-W., et al. 2010, ApJ, 714, 66...

  19. [28]

    Delchambre, L., Bailer-Jones, C. A. L., Bellas-Velidis, I., et al. 2023, A&A, 674, A31, doi: 10.1051/0004-6361/202243423

  20. [29]

    2016,, Astrophysics Source Code Library, record ascl:1608.013 http://ascl.net/1608.013

    Dolphin, A. 2016,, Astrophysics Source Code Library, record ascl:1608.013 http://ascl.net/1608.013

  21. [30]

    Dolphin, A. E. 2000, PASP, 112, 1383, doi: 10.1086/316630

  22. [31]

    Dolphin, A. E. 2002, MNRAS, 332, 91, doi: 10.1046/j.1365-8711.2002.05271.x

  23. [32]

    Dolphin, A. E. 2012, ApJ, 751, 60, doi: 10.1088/0004-637X/751/1/60

  24. [33]

    Dolphin, A. E. 2013, ApJ, 775, 76, doi: 10.1088/0004-637X/775/1/76

  25. [34]

    2016, ApJS, 222, 8, doi: 10.3847/0067-0049/222/1/8

    Dotter, A. 2016, ApJS, 222, 8, doi: 10.3847/0067-0049/222/1/8

  26. [35]

    S., et al

    Drlica-Wagner, A., Bechtol, K., Rykoff, E. S., et al. 2015, ApJ, 813, 109, doi: 10.1088/0004-637X/813/2/109

  27. [36]

    D., Pendleton, B

    Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. 1987, Physics Letters B, 195, 216, doi: 10.1016/0370-2693(87)91197-X

  28. [37]

    1992, MNRAS, 256, 43P, doi: 10.1093/mnras/256.1.43P

    Efstathiou, G. 1992, MNRAS, 256, 43P, doi: 10.1093/mnras/256.1.43P

  29. [38]

    P., Charlot, S., et al

    Endsley, R., Stark, D. P., Charlot, S., et al. 2021, MNRAS, 502, 6044, doi: 10.1093/mnras/stab432

  30. [39]

    2021, MNRAS, 507, 4211, doi: 10.1093/mnras/stab2437

    Engler, C., Pillepich, A., Pasquali, A., et al. 2021, MNRAS, 507, 4211, doi: 10.1093/mnras/stab2437

  31. [40]

    N., et al

    Escala, I., Wetzel, A., Kirby, E. N., et al. 2018, MNRAS, 474, 2194, doi: 10.1093/mnras/stx2858

  32. [41]

    Ferraro, S., & Smith, K. M. 2018, PhRvD, 98, 123519, doi: 10.1103/PhysRevD.98.123519

  33. [42]

    P., Cooper, M

    Fillingham, S. P., Cooper, M. C., Kelley, T., et al. 2019, arXiv e-prints, arXiv:1906.04180, doi: 10.48550/arXiv.1906.04180

  34. [43]

    A., Magnier, E

    Flewelling, H. A., Magnier, E. A., Chambers, K. C., et al. 2020, ApJS, 251, 7, doi: 10.3847/1538-4365/abb82d

  35. [44]

    S., McCarthy, I

    Font, A. S., McCarthy, I. G., & Belokurov, V. 2021, MNRAS, 505, 783, doi: 10.1093/mnras/stab1332

  36. [45]

    K., Battaglia, G., Pawlowski, M

    Fritz, T. K., Battaglia, G., Pawlowski, M. S., et al. 2018, A&A, 619, A103, doi: 10.1051/0004-6361/201833343

  37. [46]

    S., & Hook, R

    Fruchter, A. S., & Hook, R. N. 2002, PASP, 114, 144, doi: 10.1086/338393

  38. [47]

    W., Weisz, D

    Fu, S. W., Weisz, D. R., Starkenburg, E., et al. 2023, ApJ, 958, 167, doi: 10.3847/1538-4357/ad0030

  39. [48]

    R., Oh, S

    Furlanetto, S. R., Oh, S. P., & Briggs, F. H. 2006, PhR, 433, 181, doi: 10.1016/j.physrep.2006.08.002

  40. [49]

    R., Zaldarriaga, M., & Hernquist, L

    Furlanetto, S. R., Zaldarriaga, M., & Hernquist, L. 2004, ApJ, 613, 1, doi: 10.1086/423025

  41. [50]

    2007, in IAU Symposium, Vol

    Gallart, C., & LCID Team. 2007, in IAU Symposium, Vol. 241, Stellar Populations as Building Blocks of Galaxies, ed. A. Vazdekis & R. Peletier, 290–294, doi: 10.1017/S1743921307008186

  42. [51]

    2021, ApJ, 909, 192, doi: 10.3847/1538-4357/abddbe

    Gallart, C., Monelli, M., Ruiz-Lara, T., et al. 2021, ApJ, 909, 192, doi: 10.3847/1538-4357/abddbe

  43. [52]

    T., Kallivayalil, N., McQuinn, K

    Garling, C. T., Kallivayalil, N., McQuinn, K. B. W., et al. 2024, arXiv e-prints, arXiv:2407.19534, doi: 10.48550/arXiv.2407.19534

  44. [53]

    F., et al

    Garrison-Kimmel, S., Wetzel, A., Hopkins, P. F., et al. 2019, MNRAS, 489, 4574, doi: 10.1093/mnras/stz2507 24 Durbin et al

  45. [54]

    E., Monaco, L., et al

    Gilmore, G., Norris, J. E., Monaco, L., et al. 2013, ApJ, 763, 61, doi: 10.1088/0004-637X/763/1/61

  46. [55]

    2017, http://ascl.net/1708.004

    Ginsburg, A., Parikh, M., Woillez, J., et al. 2017, http://ascl.net/1708.004

  47. [56]

    M., Brasseur, C

    Ginsburg, A., Sip˝ ocz, B. M., Brasseur, C. E., et al. 2019, AJ, 157, 98, doi: 10.3847/1538-3881/aafc33

  48. [57]

    2021,, v0.4.5, Zenodo Zenodo, doi: 10.5281/zenodo.591669

    Ginsburg, A., Sip˝ ocz, B., Parikh, M., et al. 2021,, v0.4.5, Zenodo Zenodo, doi: 10.5281/zenodo.591669

  49. [58]

    2012, The DrizzlePac Handbook (Baltimore: STScI)

    Gonzaga, S., Hack, W., Fruchter, A., & Mack, J. 2012, The DrizzlePac Handbook (Baltimore: STScI)

  50. [59]

    2019, ApJ, 887, 93, doi: 10.3847/1538-4357/ab5362

    Finkbeiner, D. 2019, ApJ, 887, 93, doi: 10.3847/1538-4357/ab5362

  51. [60]

    J., Dencheva, N., & Fruchter, A

    Hack, W. J., Dencheva, N., & Fruchter, A. S. 2013, in Astronomical Society of the Pacific Conference Series, Vol. 475, Astronomical Data Analysis Software and Systems XXII, ed. D. N. Friedel, 49

  52. [61]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

  53. [62]

    L., Pietrinferni, A., Cassisi, S., et al

    Hidalgo, S. L., Pietrinferni, A., Cassisi, S., et al. 2018, ApJ, 856, 125, doi: 10.3847/1538-4357/aab158

  54. [63]

    L., Mack, J., Avila, R., et al

    Hoffmann, S. L., Mack, J., Avila, R., et al. 2021, in American Astronomical Society Meeting Abstracts, Vol. 53, American Astronomical Society Meeting Abstracts, 216.02

  55. [64]

    2016, ApJ, 832, 21, doi: 10.3847/0004-637X/832/1/21

    Homma, D., Chiba, M., Okamoto, S., et al. 2016, ApJ, 832, 21, doi: 10.3847/0004-637X/832/1/21

  56. [65]

    Hunter, J. D. 2007, Computing in Science and Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

  57. [66]

    2017, ApJ, 848, 85, doi: 10.3847/1538-4357/aa8c80

    Jeon, M., Besla, G., & Bromm, V. 2017, ApJ, 848, 85, doi: 10.3847/1538-4357/aa8c80

  58. [67]

    P., Simon, J

    Ji, A. P., Simon, J. D., Frebel, A., Venn, K. A., & Hansen, T. T. 2019, ApJ, 870, 83, doi: 10.3847/1538-4357/aaf3bb

  59. [68]

    2024, ApJ, 976, 93, doi: 10.3847/1538-4357/ad82de

    Jin, X., Yang, J., Fan, X., et al. 2024, ApJ, 976, 93, doi: 10.3847/1538-4357/ad82de

  60. [69]

    P., Besla, G., Anderson, J., & Alcock, C

    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

  61. [70]

    V., Zivick, P., et al

    Kallivayalil, N., Sales, L. V., Zivick, P., et al. 2018, ApJ, 867, 19, doi: 10.3847/1538-4357/aadfee

  62. [71]

    2020, MNRAS, 494, 2200, doi: 10.1093/mnras/staa639

    Katz, H., Ramsoy, M., Rosdahl, J., et al. 2020, MNRAS, 494, 2200, doi: 10.1093/mnras/staa639

  63. [72]

    2023, ApJ, 959, 31, doi: 10.3847/1538-4357/acfe08

    Kim, J., Jeon, M., Choi, Y., et al. 2023, ApJ, 959, 31, doi: 10.3847/1538-4357/acfe08

  64. [73]

    N., Lanfranchi, G

    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

  65. [74]

    E., Walker, M

    Koposov, S. E., Walker, M. G., Belokurov, V., et al. 2018, MNRAS, 479, 5343, doi: 10.1093/mnras/sty1772

  66. [75]

    2001, MNRAS, 322, 231, doi: 10.1046/j.1365-8711.2001.04022.x

    Kroupa, P. 2001, MNRAS, 322, 231, doi: 10.1046/j.1365-8711.2001.04022.x

  67. [76]

    Laevens, B. P. M., Martin, N. F., Bernard, E. J., et al. 2015, ApJ, 813, 44, doi: 10.1088/0004-637X/813/1/44

  68. [77]

    L., Babusiaux, C., & Cox, N

    Lallement, R., Vergely, J. L., Babusiaux, C., & Cox, N. L. J. 2022, A&A, 661, A147, doi: 10.1051/0004-6361/202142846

  69. [78]

    R., Dolphin, A

    Lewis, A. R., Dolphin, A. E., Dalcanton, J. J., et al. 2015, ApJ, 805, 183, doi: 10.1088/0004-637X/805/2/183

  70. [79]

    Y., Alvarez, M

    Li, T. Y., Alvarez, M. A., Wechsler, R. H., & Abel, T. 2014, ApJ, 785, 134, doi: 10.1088/0004-637X/785/2/134

  71. [80]

    A., et al

    Longeard, N., Martin, N., Ibata, R. A., et al. 2021, MNRAS, 503, 2754, doi: 10.1093/mnras/stab604

  72. [81]

    2012, ApJ, 746, 109, doi: 10.1088/0004-637X/746/1/109

    Lunnan, R., Vogelsberger, M., Frebel, A., et al. 2012, ApJ, 746, 109, doi: 10.1088/0004-637X/746/1/109

  73. [82]

    2022,, Instrument Science Report ACS 2022-03, 37 pages Mart ´ ınez-V´ azquez, C

    Mack, J., Hack, W., Burger, M., et al. 2022,, Instrument Science Report ACS 2022-03, 37 pages Mart ´ ınez-V´ azquez, C. E., Vivas, A. K., Gurevich, M., et al. 2019, MNRAS, 490, 2183, doi: 10.1093/mnras/stz2609

  74. [83]

    2010, in Proceedings of the 9th Python in Science Conference, ed

    McKinney, W. 2010, in Proceedings of the 9th Python in Science Conference, ed. S. van der Walt & Millman,

  75. [84]

    Jarrod, Proceedings of the Python in Science Conference (SciPy), 56–61, doi: 10.25080/Majora-92bf1922-00a

  76. [85]

    McQuinn, K. B. W., Mao, Y.-Y., Buckley, M. R., et al. 2023, ApJ, 944, 14, doi: 10.3847/1538-4357/acaec9

  77. [86]

    McQuinn, K. B. W., Mao, Y.-Y., Tollerud, E. J., et al. 2024, ApJ, 967, 161, doi: 10.3847/1538-4357/ad429b

  78. [87]

    McQuinn, K. B. W., Skillman, E. D., Cannon, J. M., et al. 2010, ApJ, 721, 297, doi: 10.1088/0004-637X/721/1/297

  79. [88]

    McQuinn, K. B. W., Skillman, E. D., Dolphin, A., et al. 2015, ApJ, 812, 158, doi: 10.1088/0004-637X/812/2/158

  80. [89]

    2007, MNRAS, 377, 1043, doi: 10.1111/j.1365-2966.2007.11489.x

    McQuinn, M., Lidz, A., Zahn, O., et al. 2007, MNRAS, 377, 1043, doi: 10.1111/j.1365-2966.2007.11489.x

  81. [90]

    L., et al

    Monelli, M., Gallart, C., Hidalgo, S. L., et al. 2010, ApJ, 722, 1864, doi: 10.1088/0004-637X/722/2/1864 Mu˜ noz, J. A., Madau, P., Loeb, A., & Diemand, J. 2009, MNRAS, 400, 1593, doi: 10.1111/j.1365-2966.2009.15562.x Mu˜ noz, R. R., Cˆ ot´ e, P., Santana, F. A., et al. 2018, ...

  82. [91]

    J., Carlin, J

    Mutlu-Pakdil, B., Sand, D. J., Carlin, J. L., et al. 2018, ApJ, 863, 25, doi: 10.3847/1538-4357/aacd0e

  83. [92]

    R., & El-Badry, K

    Nagarajan, P., Weisz, D. R., & El-Badry, K. 2022, ApJ, 932, 19, doi: 10.3847/1538-4357/ac69e6

  84. [93]

    L., Dey, A., Olsen, K., et al

    Nidever, D. L., Dey, A., Olsen, K., et al. 2018, AJ, 156, 131, doi: 10.3847/1538-3881/aad68f

  85. [94]

    L., Dey, A., Fasbender, K., et al

    Nidever, D. L., Dey, A., Fasbender, K., et al. 2021, AJ, 161, 192, doi: 10.3847/1538-3881/abd6e1

  86. [95]

    2000, A&AS, 143, 23, doi: 10.1051/aas:2000169 Photometry and SFHs for 36 UFDs 25

    Ochsenbein, F., Bauer, P., & Marcout, J. 2000, A&AS, 143, 23, doi: 10.1051/aas:2000169 Photometry and SFHs for 36 UFDs 25

  87. [96]

    G., et al

    Ocvirk, P., Aubert, D., Sorce, J. G., et al. 2020, MNRAS, 496, 4087, doi: 10.1093/mnras/staa1266

  88. [97]

    2012, ApJ, 744, 96, doi: 10.1088/0004-637X/744/2/96

    Okamoto, S., Arimoto, N., Yamada, Y., & Onodera, M. 2012, ApJ, 744, 96, doi: 10.1088/0004-637X/744/2/96

  89. [98]

    Pace, A. B. 2024, arXiv e-prints, arXiv:2411.07424, doi: 10.48550/arXiv.2411.07424

  90. [99]

    B., Erkal, D., & Li, T

    Pace, A. B., Erkal, D., & Li, T. S. 2022, ApJ, 940, 136, doi: 10.3847/1538-4357/ac997b

  91. [100]

    2020, ApJ, 893, 121, doi: 10.3847/1538-4357/ab7b75

    Patel, E., Kallivayalil, N., Garavito-Camargo, N., et al. 2020, ApJ, 893, 121, doi: 10.3847/1538-4357/ab7b75

  92. [101]

    2014, ApJ, 793, 113, doi: 10.1088/0004-637X/793/2/113

    Pentericci, L., Vanzella, E., Fontana, A., et al. 2014, ApJ, 793, 113, doi: 10.1088/0004-637X/793/2/113

  93. [102]

    2004, ApJ, 612, 168, doi: 10.1086/422498

    Pietrinferni, A., Cassisi, S., Salaris, M., & Castelli, F. 2004, ApJ, 612, 168, doi: 10.1086/422498

  94. [103]

    2021, ApJ, 908, 102, doi: 10.3847/1538-4357/abd4d5 Planck Collaboration, Aghanim, N., Akrami, Y., et al

    Pietrinferni, A., Hidalgo, S., Cassisi, S., et al. 2021, ApJ, 908, 102, doi: 10.3847/1538-4357/abd4d5 Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 641, A6, doi: 10.1051/0004-6361/201833910

  95. [104]

    2022, ApJ, 933, 217, doi: 10.3847/1538-4357/ac7226

    Richstein, H., Patel, E., Kallivayalil, N., et al. 2022, ApJ, 933, 217, doi: 10.3847/1538-4357/ac7226

  96. [105]

    Ricotti, M., & Gnedin, N. Y. 2005, ApJ, 629, 259, doi: 10.1086/431415

  97. [106]

    E., Ellis, R

    Robertson, B. E., Ellis, R. S., Dunlop, J. S., McLure, R. J., & Stark, D. P. 2010, Nature, 468, 49, doi: 10.1038/nature09527 Rodriguez Wimberly, M. K., Cooper, M. C., Fillingham, S. P., et al. 2019, MNRAS, 483, 4031, doi: 10.1093/mnras/sty3357

  98. [107]

    2021, ApJL, 920, L19, doi: 10.3847/2041-8213/ac2aa3

    Sacchi, E., Richstein, H., Kallivayalil, N., et al. 2021, ApJL, 920, L19, doi: 10.3847/2041-8213/ac2aa3

  99. [108]

    V., Navarro, J

    Sales, L. V., Navarro, J. F., Kallivayalil, N., & Frenk, C. S. 2017, MNRAS, 465, 1879, doi: 10.1093/mnras/stw2816

  100. [109]

    2009, MNRAS, 395, L6, doi: 10.1111/j.1745-3933.2009.00627.x

    Salvadori, S., & Ferrara, A. 2009, MNRAS, 395, L6, doi: 10.1111/j.1745-3933.2009.00627.x

  101. [110]

    B., Wetzel, A., Tollerud, E., Sanderson, R

    Santistevan, I. B., Wetzel, A., Tollerud, E., Sanderson, R. E., & Samuel, J. 2023, MNRAS, 518, 1427, doi: 10.1093/mnras/stac3100

  102. [111]

    R., Skillman, E

    Savino, A., Weisz, D. R., Skillman, E. D., et al. 2023, ApJ, 956, 86, doi: 10.3847/1538-4357/acf46f

  103. [112]

    R., Dolphin, A

    Savino, A., Weisz, D. R., Dolphin, A. E., et al. 2025, ApJ, 979, 205, doi: 10.3847/1538-4357/ada24f

  104. [113]

    S., Fattahi, A., et al

    Sawala, T., Frenk, C. S., Fattahi, A., et al. 2016, MNRAS, 456, 85, doi: 10.1093/mnras/stv2597

  105. [114]

    F., & Finkbeiner, D

    Schlafly, E. F., & Finkbeiner, D. P. 2011, ApJ, 737, 103, doi: 10.1088/0004-637X/737/2/103

  106. [115]

    J., Finkbeiner, D

    Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525, doi: 10.1086/305772

  107. [116]

    Simon, J. D. 2019, ARA&A, 57, 375, doi: 10.1146/annurev-astro-091918-104453

  108. [117]

    Thompson, I. B. 2010, ApJ, 716, 446, doi: 10.1088/0004-637X/716/1/446

  109. [118]

    D., Li, T

    Simon, J. D., Li, T. S., Erkal, D., et al. 2020, ApJ, 892, 137, doi: 10.3847/1538-4357/ab7ccb

  110. [119]

    D., Brown, T

    Simon, J. D., Brown, T. M., Drlica-Wagner, A., et al. 2021, ApJ, 908, 18, doi: 10.3847/1538-4357/abd31b

  111. [120]

    D., Brown, T

    Simon, J. D., Brown, T. M., Mutlu-Pakdil, B., et al. 2023, ApJ, 944, 43, doi: 10.3847/1538-4357/aca9d1

  112. [121]

    D., Li, T

    Simon, J. D., Li, T. S., Ji, A. P., et al. 2024, ApJ, 976, 256, doi: 10.3847/1538-4357/ad85dd

  113. [122]

    M., Bryan, G

    Simpson, C. M., Bryan, G. L., Johnston, K. V., et al. 2013, MNRAS, 432, 1989, doi: 10.1093/mnras/stt474

  114. [123]

    M., Grand, R

    Simpson, C. M., Grand, R. J. J., G´ omez, F. A., et al. 2018, MNRAS, 478, 548, doi: 10.1093/mnras/sty774

  115. [124]

    D., Tolstoy, E., Cole, A

    Skillman, E. D., Tolstoy, E., Cole, A. A., et al. 2003, ApJ, 596, 253, doi: 10.1086/377635

  116. [125]

    D., Monelli, M., Weisz, D

    Skillman, E. D., Monelli, M., Weisz, D. R., et al. 2017, ApJ, 837, 102, doi: 10.3847/1538-4357/aa60c5

  117. [126]

    M., & Ferraro, S

    Smith, K. M., & Ferraro, S. 2017, PhRvL, 119, 021301, doi: 10.1103/PhysRevLett.119.021301

  118. [127]

    2013, MNRAS, 432, L51, doi: 10.1093/mnrasl/slt035

    Sobacchi, E., & Mesinger, A. 2013, MNRAS, 432, L51, doi: 10.1093/mnrasl/slt035

  119. [128]

    Somerville, R. S. 2002, ApJL, 572, L23, doi: 10.1086/341444

  120. [129]

    G., Ocvirk, P., Aubert, D., et al

    Sorce, J. G., Ocvirk, P., Aubert, D., et al. 2022, MNRAS, 515, 2970, doi: 10.1093/mnras/stac2007 STSCI Development Team. 2012,, Astrophysics Source Code Library, record ascl:1212.011 http://ascl.net/1212.011 The Matplotlib Development Team. 2023,, v3.8.1, Zenodo Zenodo, doi: 1...

  121. [130]

    2010, ApJ, 708, 1398, doi: 10.1088/0004-637X/708/2/1398 van der Walt, S., Colbert, S

    Tumlinson, J. 2010, ApJ, 708, 1398, doi: 10.1088/0004-637X/708/2/1398 van der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, Computing in Science and Engineering, 13, 22, doi: 10.1109/MCSE.2011.37

  122. [131]

    2014b, ApJ, 794, 72, doi: 10.1088/0004-637X/794/1/72

    Edvardsson, B. 2014b, ApJ, 794, 72, doi: 10.1088/0004-637X/794/1/72

  123. [132]

    C., Geha, M., Kirby, E

    Vargas, L. C., Geha, M., Kirby, E. N., & Simon, J. D. 2013, ApJ, 767, 134, doi: 10.1088/0004-637X/767/2/134

  124. [133]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2

  125. [134]

    K., Mart ´ ınez-V´ azquez, C., & Walker, A

    Vivas, A. K., Mart ´ ınez-V´ azquez, C., & Walker, A. R. 2020, ApJS, 247, 35, doi: 10.3847/1538-4365/ab67c0 26 Durbin et al

  126. [135]

    2021, The Journal of Open Source Software, 6, 3021, doi: 10.21105/joss.03021

    Waskom, M. 2021, The Journal of Open Source Software, 6, 3021, doi: 10.21105/joss.03021

  127. [136]

    2020,, Astrophysics Source Code Library, record ascl:2012.015 http://ascl.net/2012.015

    Waskom, M., Botvinnik, O., Gelbart, M., et al. 2020,, Astrophysics Source Code Library, record ascl:2012.015 http://ascl.net/2012.015

  128. [137]

    2023,, v0.13.0, Zenodo Zenodo, doi: 10.5281/zenodo.592845

    Waskom, M., Gelbart, M., Botvinnik, O., et al. 2023,, v0.13.0, Zenodo Zenodo, doi: 10.5281/zenodo.592845

  129. [138]

    2015, ApJL, 799, L21, doi: 10.1088/2041-8205/799/2/L21

    Webster, D., Bland-Hawthorn, J., & Sutherland, R. 2015, ApJL, 799, L21, doi: 10.1088/2041-8205/799/2/L21

  130. [139]

    R., Dolphin, A

    Weisz, D. R., Dolphin, A. E., Skillman, E. D., et al. 2014a, ApJ, 789, 148, doi: 10.1088/0004-637X/789/2/148

  131. [140]

    R., Dolphin, A

    Weisz, D. R., Dolphin, A. E., Skillman, E. D., et al. 2014b, ApJ, 789, 147, doi: 10.1088/0004-637X/789/2/147

  132. [141]

    R., Dolphin, A

    Weisz, D. R., Dolphin, A. E., Skillman, E. D., et al. 2015, ApJ, 804, 136, doi: 10.1088/0004-637X/804/2/136

  133. [142]

    R., Savino, A., & Dolphin, A

    Weisz, D. R., Savino, A., & Dolphin, A. E. 2023, ApJ, 948, 50, doi: 10.3847/1538-4357/acc328

  134. [143]

    R., Dolphin, A

    Weisz, D. R., Dolphin, A. E., Dalcanton, J. J., et al. 2011a, ApJ, 743, 8, doi: 10.1088/0004-637X/743/1/8

  135. [144]

    R., Dalcanton, J

    Weisz, D. R., Dalcanton, J. J., Williams, B. F., et al. 2011b, ApJ, 739, 5, doi: 10.1088/0004-637X/739/1/5

  136. [145]

    R., Zucker, D

    Weisz, D. R., Zucker, D. B., Dolphin, A. E., et al. 2012, ApJ, 748, 88, doi: 10.1088/0004-637X/748/2/88

  137. [146]

    R., Deason, A

    Wetzel, A. R., Deason, A. J., & Garrison-Kimmel, S. 2015, ApJ, 807, 49, doi: 10.1088/0004-637X/807/1/49

  138. [147]

    R., Hopkins, P

    Wetzel, A. R., Hopkins, P. F., Kim, J.-h., et al. 2016, ApJL, 827, L23, doi: 10.3847/2041-8205/827/2/L23

  139. [148]

    S., et al

    Wheeler, C., O˜ norbe, J., Bullock, J. S., et al. 2015, MNRAS, 453, 1305, doi: 10.1093/mnras/stv1691

  140. [149]

    F., Dalcanton, J

    Williams, B. F., Dalcanton, J. J., Seth, A. C., et al. 2009, AJ, 137, 419, doi: 10.1088/0004-6256/137/1/419

  141. [150]

    F., Lang, D., Dalcanton, J

    Williams, B. F., Lang, D., Dalcanton, J. J., et al. 2014, ApJS, 215, 9, doi: 10.1088/0067-0049/215/1/9

  142. [151]

    F., Durbin, M

    Williams, B. F., Durbin, M. J., Dalcanton, J. J., et al. 2021, ApJS, 253, 53, doi: 10.3847/1538-4365/abdf4e

  143. [152]

    F., Durbin, M., Lang, D., et al

    Williams, B. F., Durbin, M., Lang, D., et al. 2023, ApJS, 268, 48, doi: 10.3847/1538-4365/acea61

  144. [153]

    R., et al

    Zaremba, D., Venn, K., Hayes, C. R., et al. 2025, arXiv e-prints, arXiv:2503.05927, doi: 10.48550/arXiv.2503.05927

  145. [154]

    2025, arXiv e-prints, arXiv:2507.16245, doi: 10.48550/arXiv.2507.16245

    Zhao, Y., Smith, A., Kannan, R., et al. 2025, arXiv e-prints, arXiv:2507.16245, doi: 10.48550/arXiv.2507.16245

  146. [155]

    Zhu, H., Avestruz, C., & Gnedin, N. Y. 2019, ApJ, 882, 152, doi: 10.3847/1538-4357/ab3794

  147. [156]

    2025, arXiv e-prints, arXiv:2503.02927, doi: 10.48550/arXiv.2503.02927

    Zier, O., Kannan, R., Smith, A., et al. 2025, arXiv e-prints, arXiv:2503.02927, doi: 10.48550/arXiv.2503.02927

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