Pith. sign in

REVIEW 4 major objections 6 minor 1 cited by

Population Models for Star Formation Timescales in Early Galaxies: The First Step Towards Solving Outshining in Star Formation History Inference

T0 review · 4 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A population-level model of bursty star formation, measured from spectral-feature distributions and applied as an informed prior, removes the outshining bias that makes standard galaxy SED fits underestimate masses by about 0.15 dex.

desk verdict Solid negative result on individual burstiness constraints, and a genuinely promising population-level direction whose proof-of-concept still leans on a stationarity assumption the authors honestly flag. read the letter →

arxiv 2504.15255 v2 pith:CJK2K4T4 submitted 2025-04-21 astro-ph.GA

Bingjie Wang , Joel Leja , Hakim Atek , Rachel Bezanson , Emilie Burnham , Pratika Dayal , Robert Feldmann , Jenny E. Greene
show 8 more authors
Benjamin D. Johnson Ivo Labbe Michael V. Maseda Themiya Nanayakkara Sedona H. Price Katherine A. Suess John R. Weaver Katherine E. Whitaker
This is my paper · ORCID
classification astro-ph.GA
keywords galaxyevolutionstarformationhistoryoutshiningburstySEDfittingpopulationinferenceJWSTspectralenergydistribution
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

This paper argues that the long-standing outshining problem—recent star formation drowning out older stellar light in galaxy spectra—can be solved statistically rather than by improving individual fits. Using simulated bursty galaxies, the authors show that standard SED-fitting methods with flexible star formation histories and neutral priors recover average star formation rates but miss tens-of-Myr fluctuations and underestimate stellar masses by about 0.15 dex, even with high signal-to-noise spectroscopy. Refitting the same data with a prior that correctly encodes the bursty expectation reduces median mass and SFR offsets to about 0.04 and 0.05 dex. The paper's proposed remedy is to measure, for a representative galaxy sample, the population distribution of burstiness parameters—amplitude, duration, and slope of recent star formation—and use that distribution as an informed prior for individual galaxies. It demonstrates that comparing the observed distributions of four timescale-sensitive spectral features against model predictions via the Wasserstein distance always recovers the correct burstiness family on its test grid.

What carries the argument

The engine of the argument is a deliberately simplified parameterization of bursty star formation: repeated on/off fluctuations around a star-forming sequence, governed by population-level parameters $\sigma$ (amplitude of specific star formation rate (sSFR) fluctuations in dex), $\delta t$ (duration of each high/low phase), $\alpha$ (power-law slope of SFR over the last 500 Myr), and a phase $\phi$. The paper simulates mock JWST photometry and spectroscopy from this model and tests three inference routes. The decisive tool is the population-level comparison: rather than fitting each galaxy, the observed distribution of four timescale-sensitive spectral features (Balmer break strength, Balmer emission lines, NUV and FUV flux densities) is compared with model-predicted distributions using the Wasserstein distance (a measure of how much probability mass must be moved to turn one distribution into another), and the SFH family with the shortest distance is selected. This works because many galaxies observed at different phases trace out the population's phase space even though a single spectrum cannot.

What would settle it

Take a mock population generated with a non-stationary burstiness model—for example, merger-driven bursts whose amplitude and duration grow with lookback time—and run the proposed population-level Wasserstein fit on its spectral features, then use the inferred prior in individual SED fits; if the resulting mass and SFR posteriors show the old roughly 0.15 dex biases, or the four-feature fit selects the wrong $\sigma$, $\delta t$, and $\alpha$, the central claim is falsified. A simpler check: compare the burstiness distribution measured at high redshift with the same galaxies' star formation histories inferred at later epochs to test stationarity directly.

Watch

Extended reading notes

Core claim

The central discovery is quantitative: state-of-the-art individual SED fitting, even in a systematics-free mock with S/N=20 spectroscopy covering rest-frame 0.12 to 1.06 µm, cannot recover the parameters of a bursty star formation history, and the posterior medians scatter more than the spacing of the model grid. Under a continuity prior the inferred masses are biased low by roughly 0.11–0.18 dex; under a bursty continuity prior the bias is worse, 0.36–0.48 dex. When the same mock data are refit with a prior that knows the correct oscillating SFH model, median offsets drop to about 0.04 dex in mass and about 0.05 dex in SFR, and the detailed recent SFH is recovered. The paper then shows that the H$\alpha$/UV ratio, the standard population-level burstiness indicator, constrains only the fluctuation amplitude. A simultaneous population-level fit using the distributions of Balmer break strength, Balmer emission line flux, and dust-corrected NUV and FUV flux densities recovers the correct amplitude, duration, and slope on the tested grid. The authors conclude that empirically measuring the population distribution of bursty SFHs and applying it as an informed prior is the key to addressing outshining at a statistical level.

Load-bearing premise

The whole solution rests on the assumption that the burstiness measured in the current galaxy population predicts the star formation histories of those galaxies over the past few hundred million years—if bursts are driven by non-stationary processes such as mergers or by changing gas availability, the informed prior will not describe past SFH and the claimed elimination of outshining bias fails.

Editorial extensions

If this is right

  • Standard flexible-SFH fits with neutral priors systematically underestimate stellar masses in bursty, rising-SFH galaxies by about 0.15 dex, so mass functions built from such fits inherit this bias.
  • A prior that encodes the true level of burstiness removes most of the bias: median offsets drop to about 0.04 dex in mass and about 0.05 dex in SFR, and the recent SFH shape is recovered.
  • The H$\alpha$/UV flux ratio alone cannot distinguish burst duration or underlying SFH slope, so it is insufficient as a burstiness constraint.
  • The population-level Wasserstein fit over four spectral-feature distributions identifies the correct fluctuation amplitude, duration, and slope on the tested grid, providing a route to empirically calibrate burstiness priors.
  • Burstiness alone shifts rest-optical fluxes by over two magnitudes at fixed mass and redshift, so flux-limited surveys preferentially select high-amplitude galaxies; completeness corrections require measuring the burstiness distribution first.

Reading between the lines

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

  • Editorial inference: A natural extension is to fit all galaxies together in one statistical model that learns the burstiness distribution and its evolution with cosmic time simultaneously, relaxing the stationarity assumption that current burstiness predicts past star formation.
  • Editorial inference: The Wasserstein grid search is a proof of concept; on real data a continuous density estimate over the SFH parameter space, possibly with simulation-based inference, would be needed to handle dust, metallicity, and selection functions at the same time.
  • Editorial inference: Because flux-limited surveys over-represent galaxies in the bursting phase, the burstiness prior should be anchored to mass-complete or volume-complete samples; otherwise the measured population distribution will overestimate the fluctuation amplitude.
  • Editorial inference: The outshining bias identified here applies to any unresolved galaxy population, not only the early universe, so the same population-prior strategy should transfer to lower-redshift samples with appropriate recalibration.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. This paper uses a simple oscillating star-formation-history (SFH) model, parameterized by burst amplitude sigma, burst duration delta_t, underlying slope alpha, and observed phase phi, to generate mock JWST/NIRSpec Prism and NIRCam observations at z=4 with S/N=20. It then tests whether standard individual SED fitting can recover the burstiness parameters and the stellar mass. The authors find that with neutral continuity or bursty-continuity priors, individual fits do not meaningfully constrain the population-level burstiness parameters, and they report typical stellar-mass underestimates of about -0.11 to -0.18 dex for fine time bins, while the coarser Prospector-alpha model shows near-zero mass bias. Fitting the same spectra with the correct oscillating SFH model reduces median mass and SFR offsets to about 0.04 and 0.05 dex. The paper then evaluates H-alpha/UV ratios as a population-level burstiness indicator, finding that they constrain the fluctuation amplitude but not duration or slope, and proposes a four-feature population-level fit (Balmer break, Balmer emission, NUV, FUV) using Wasserstein distances, which always identifies the correct model on its grid. The proposed solution to outshining is to measure the population distribution of burstiness empirically and apply it as an informed prior for individual SFR(t) inference, under the stated assumption that current population burstiness predicts past SFH.

Significance. If the central claims hold, the paper offers an observationally actionable route toward mitigating outshining in statistical samples: calibrate burstiness priors from population-level spectral-feature distributions and use them in individual SED fits. The controlled experimental design is a strength: the mock observations are generated with the same stellar population synthesis code used in the fitting, providing a systematics-free, no-model-mismatch benchmark, and the paper explicitly enumerates its assumptions. The qualitative conclusion that individual high-S/N spectra cannot break degeneracies among burst amplitude, duration, and slope is well supported by the large spread of posterior medians around the truth. However, the quantitative mass-bias claim is partially confounded by the sampler convergence issue documented in Appendix C, and the population-level validation is a closed-box test on the same generative grid. These issues need to be addressed before the stronger conclusions about solving outshining are accepted.

major comments (4)
  1. [Abstract; Section 3.4.1; Figure 6] The abstract and Section 8 state that standard techniques with flexible SFHs and neutral priors typically underestimate masses in bursty systems by about 0.15 dex, but Figure 6a and the text of Section 3.4.1 show that the Prospector-alpha model, which is one of the standard methods, has near-zero median mass bias (-0.01 and -0.03 dex for the two rising-SFH families). The ~0.15 dex bias appears only for the fine-bin continuity prior. Please reconcile these statements and specify that the mass-bias finding applies to the fine-bin setup, not to all flexible-SFH/neutral-prior methods.
  2. [Appendix C; Section 7.3.1] The interpretation of the mass bias as an outshining effect is confounded by sampler failure. Appendix C shows that fitting the same SED multiple times produces inferred masses that vary substantially and that nautilus consistently identifies the underestimated mass, while Section 7.3.1 states that the mass bias is 'perhaps primarily driven by' the challenge of finding the global maximum on the likelihood surface. This means the ~0.15 dex median offset in Section 3.4.1 cannot cleanly be attributed to outshining or limited information content. Please quantify the sampler contribution (for example, by reporting best-of-many fits, initializing at the truth, or comparing samplers) and revise the abstract and Section 8 conclusions accordingly.
  3. [Section 6.2; Section 6.3] The population-level demonstration is a closed-box test: mock feature distributions are drawn from the same oscillating-SFH model grid that defines the comparison library, and the 'always identifies the correct model' result in Section 6.3 is reported without repeated noise realizations or an uncertainty estimate for the four-feature Wasserstein statistic. Success is therefore partly by construction. Please report success fractions over noise realizations and test against out-of-grid or simulation-based SFHs (for example, FIREbox trajectories) to show that the method does not merely interpolate its own generative grid.
  4. [Section 7.2; Section 7.4] The proposed solution rests on two assumptions that are acknowledged but not tested: stationarity of burstiness statistics over lookback time, and availability of a representative sample. The mocks enforce stationarity by drawing phases uniformly from a fixed oscillating model, and Section 6 assumes complete samples of 300 galaxies, while Section 7.4 shows that flux-limited surveys preferentially select galaxies in the bursting phase, with more than a magnitude of variation at fixed mass and redshift. Because these assumptions are load-bearing for the central claim, the paper should either add a non-stationary test or explicitly rescope the headline conclusion to a proof-of-concept under stationarity, and it should state how the population measurement would be made representative in the presence of the selection effect it identifies.
minor comments (6)
  1. [Figure 5 caption] The caption says the results are shown in the same format as Figure 5, but the cross-reference appears to be intended for Figure 4.
  2. [Figure 6; Figure D.1] The labels 'slowing rising' should read 'slowly rising' in both the figure annotations and the surrounding text.
  3. [Appendix B] The text twice uses 'dust attention' where 'dust attenuation' is intended.
  4. [Section 7.3.2] The phrase 'adjunct time bins' should be 'adjacent time bins'.
  5. [Abstract] The phrase 'JWST have revealed' should be 'JWST has revealed' for grammatical agreement.
  6. [Section 4] The successful correct-model fits are demonstrated on no-dust mocks, and the text notes that dust is important for real data; the abstract's claim that encoding the bursty expectation 'eliminates these biases' should be qualified as applying to the systematics-free, no-dust mock setting.

Circularity Check

1 steps flagged · score 4.0 of 10

Positive solution path is demonstrated on mock data drawn from the same oscillating-SFH model used as the 'correct' prior, so the recovery success is partly a self-consistency test; the negative results are independent and non-circular.

  1. self definitional [Section 4 (Individual SED Fits, Revised with a Correct Model of Burstiness) and Section 6.2-6.3 (population-level fit)]
    "To this end, we fit mock observations using an oscillating SFH model as parameterized in Section 2.1. ... we find that by utilizing the four observables proposed in this paper, the correct SFH model can always be identified."

    The mock observations in Section 2.1 are generated from the very same oscillating SFH parameterization (sigma, delta_t, alpha, phi) that Section 4 adopts as the 'correct' burstiness model and that Section 6 samples on its comparison grid. Section 4's near-zero mass and SFR biases therefore show that fitting with the true generative model recovers the input, while Section 6's 100% success shows that nearest-grid-point matching works when the simulated feature distributions come from that same grid.

full rationale

The paper's negative results are self-contained and not circular: the demonstration that flexible SFH priors fail to recover bursty SFHs from high-S/N mock spectra, and that H-alpha/UV ratios alone cannot constrain duration and slope, are genuine tests with independent content. The self-citations to prior work by the same authors are used for priors, SED settings, and a Balmer-break index; none is a load-bearing uniqueness theorem, so they do not raise the circularity score. The partial circularity is confined to the positive solution path. Section 4 calls the oscillating model 'correct' and then fits mocks generated from that same model, making the unbiased recovery expected by construction. Section 6 validates the population-level method by forward-modeling feature distributions from the same model grid it compares against, so 'always identifies the correct model' is a retrieval test with no model mismatch. The paper is honest about this being a proof of concept and about the critical stationarity assumption in Section 7.2, which reduces the severity. Overall, the central claim that outshining can be solved by empirically measuring burstiness is supported only by self-generated mocks at this stage; the finding is promising but the 'prediction' of recovery is partly built into the setup, giving a score of 4 rather than 0.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claims rest on the oscillating SFH model being representative of real bursty galaxies, on stationarity of burstiness, on the validity of the chosen spectral feature set, and on dust-free mocks. These are acknowledged by the authors as simplifications.

free parameters (4)
  • Oscillating SFH family parameters (sigma, delta_t, alpha, phi) = sigma in {0.3,0.8} dex, delta_t in {20,40} Myr, alpha in {-0.4,-0.65}, phi uniform in [0,2pi] for mocks; denser grid…
    These hand-chosen parameters define the bursty SFH model and the grid over which the population method is tested. The method's 100% success is measured against this specific grid; a coarser or different grid could change results.
  • Grid resolution for population-level fit = 0.1 dex in sigma, 5 Myr in delta_t, 0.05 in alpha
    The claim that the correct model is 'always' identified is with respect to this discrete grid; the paper does not explore sensitivity to grid spacing.
  • Spectral feature set and combination rule = Balmer break strength, Halpha line, dust-corrected NUV and FUV, with four Wasserstein distances added in quadrature
    The choice of these four observables and the quadrature combination is ad hoc to this paper; other features or weighting could alter the result.
  • Signal-to-noise per pixel = S/N = 20
    Observational assumption for mocks; real JWST data vary. The individual-fit failure is shown to persist at higher resolution, but the population method's success at lower S/N is untested.
assumptions (6)
  • domain assumption Current population-level burstiness predicts past SFH (stationarity of the star formation process over a few hundred Myr to Gyr).
    Explicitly stated in Section 7.2 as 'a critical assumption in using population-level burstiness model to solve outshining' and in Section 7.1. If false, the informed prior does not describe past SFHs and outshining is not solved.
  • domain assumption The simplified oscillating SFH parameterization captures the spectral characteristics of real bursty SFHs.
    Section 2.1 states the results depend only on the models capturing the spectral characteristics of bursty SFHs. The paper concedes real SFHs are more complex in Section 7.2.
  • domain assumption Galaxies in similar redshift and mass ranges share similar SFH variation timescales, so they can be grouped into SFH families.
    Section 6 states 'a key assumption that the formation histories of most galaxies vary on similar timescales' and assumes similar environmental conditions at fixed mass and redshift.
  • domain assumption No dust attenuation in the mock observations used for the population-level demonstration.
    Section 2.2 sets dust attenuation to zero; Section 7.2 notes the population-level fits assume no dust and that dust may degrade recovery. The method is not validated under dust.
  • domain assumption All line emission in SED fits is produced by starlight, with no contribution from shocks or AGN.
    Section 3.1 states this standard SED-fitting practice. It affects the Halpha constraint used in the population method.
  • standard math Wasserstein distance is an appropriate distribution-comparison metric and adding the four distances in quadrature preserves model identification power.
    Sections 5 and 6.2 use Wasserstein distance without deriving optimality for this problem. It is a reasonable metric but the choice is unvalidated against alternatives.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Population Models for Star Formation Timescales in Early Galaxies: The First Step Towards Solving Outshining in Star Formation History Inference." pith.science (2026). https://pith.science/paper/CJK2K4T4

@misc{pith2026250415255,
  author       = {Pith},
  title        = {Pith review of: Population Models for Star Formation Timescales in Early Galaxies: The First Step Towards Solving Outshining in Star Formation History Inference},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CJK2K4T4}},
  note         = {Machine review of arXiv:2504.15255}
}
abstract

JWST have revealed temporarily-quenched and ultraviolet-luminous galaxies in the early universe, suggesting enhanced star formation stochasticity. Verifying this hypothesis is critical, yet challenging; outshining, wherein light from young stars dominates the spectral energy distribution, represents perhaps the greatest challenge in inferring the formation histories of unresolved galaxies. In this paper, we take a simple model of burstiness and show that state-of-the-art inference methods with flexible star formation histories (SFHs) and neutral priors, while recovering average star formation rates (SFRs; $\sim0.1$ dex median offset), fail to recover the complexities of fluctuations on tens of Myr timescales, and typically underestimate masses in bursty systems ($\sim0.15$ dex). Surprisingly, detailed SFH recovery is still sensitive to priors even when data quality is optimal, e.g., including high signal-to-noise ($\rm20~pixel^{-1}$) spectroscopy with wide coverage (rest-frame $0.12-1.06~\mu$m). Crucially, however, refitting the same data with a prior correctly encoding the bursty expectation eliminates these biases: median offsets in mass and SFRs decrease to $\sim 0.04$ dex and $\sim 0.05$ dex, respectively. Under the assumption that current population burstiness predicts past SFH, the solution to outshining in modeling statistical samples is empirically measuring recent galaxy SFHs with population modeling. A prototype is H$\alpha$/UV: while helpful, it is insufficient to constrain the expected complex burstiness. To this end, we introduce a more complete, quantitative population-level approach and demonstrate that it promises to recover the typical amplitude, timescale, and slope of the recent SFH to high accuracy. This approach thus has the strong potential to solve outshining using observations from JWST.

Figures

Figures reproduced from arXiv: 2504.15255 by the authors.

Figure 1
Figure 1. Models of star-formation history. (Left) Two examples of our parameterization of the population-level SFH param￾eters. One SFH simulated with a fluctuation amplitude σ = 0.3 dex around the star forming sequence (gray dash-dotted line), a slowly rising slope, α = −0.40, and a short duration of the high/low SFR phase, δt = 20 Myr, is plotted in light brown. One SFH simulated with a greater fluctuation amplitude, durat… view at source ↗
Figure 2
Figure 2. Variations in the F277W and F444W fluxes due to SFH variations alone, with variations due to mass, dust, and redshift removed. For each filter, we include two panels showing the distributions of fluxes from SFHs with a slowly rising slope (α = −0.40), and a steeply rising slope (α = −0.65). The four curves in each panel reflect the changes in fluxes due to various fluctuating amplitudes and durations. For rising SFH… view at source ↗
Figure 3
Figure 3. Examples of SFHs and the corresponding model spectra, illustrating the timescale-sensitive spectral features. SFHs simulated with a slowly rising slope of α = −0.40, a fluctuation amplitude of σ = 0.3 dex around the star forming sequence (gray dotted line) and two durations of δt = 20 Myr and δt = 40 Myr are plotted in light and dark green, respectively in the upper left panel. The resulting model spectra are to the… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Individual SED fits. (a) An example of a well recovered SFH, assuming a continuity prior in the SED fit. The left panel shows the simulated spectrum in black, and the best-fit model spectrum assuming the continuity SFH prior in purple. The middle panel shows the true S…
Figure 5
Figure 5. Figure 5: Individual SED fits. An example of a poorly recovered SFH assuming the continuity prior and the bursty continuity prior are shown in the same format as [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Recovery of mass and averaged SFR from SED fitting, under different model assumptions. (a) The beige and blue histograms show the distributions of difference between the recovered mass and the true mass, assuming the continuity prior and the bursty continuity prior, re…
Figure 7
Figure 7. Figure 7: The accuracy and precision of the inferred population-level SFH parameters from individual fits vary wildly. Each row corresponds to a SFH family. Each panel shows the posterior moments (median / 16th / 84th quantiles) of the population-level SFH parameters, amplitude …
Figure 8
Figure 8. Figure 8: Contrasting the recoveries of the population-level SFH parameters via individual SED fit (Section 3), Hα/UV (Section 5), and population fit (Section 6). The colored data points indicate 50th quantiles of the distributions of posterior medians of the population-level SF…
Figure 9
Figure 9. Figure 9: Inferring SFHs with medium-resolution spectra. The results are presented in the same format as [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 10
Figure 10. Figure 10: With the appropriate model for burstiness, inferring the SFH via SED fitting is indeed feasible. The results here are shown in the same format as [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]
Figure 11
Figure 11. Figure 11: Hα-to-UV flux ratios are sensitive to some, but not all population-level SFH parameters. (Upper) The flux ratios are plotted as functions of observed phases in the first panel, whereas the same flux ratios are plotted as histograms to illustrate their distributions, a…
Figure 12
Figure 12. Figure 12: Proposed timescale-sensitive observables of this paper. The sampled population-level SFH parameters are the same as those used in testing the individual fits. Each row includes the predicted distributions drawn from SFH families with the same slope. The distributions …
Figure 13
Figure 13. Figure 13: Population-level burstiness can shift the distributions of magnitudes at a fixed mass and redshift by > 2 magnitudes. Fluctuation amplitudes in the SFHs are plotted as functions of observed fluxes in F277W band to the left, corresponding to rest-frame optical, and in …

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MINERVA: A NIRCam Medium Band and MIRI Imaging Survey to Unlock the Hidden Gems of the Distant Universe

    astro-ph.GA 2025-07 conditional novelty 6.0 of 10

    The MINERVA survey will expand JWST medium-band imaging area by about 7 times and is forecast to cut photometric-redshift scatter and outlier fractions by factors of 3.7 and 2.6.

Reference graph

Works this paper leans on

136 extracted references · 3 canonical work pages · cited by 1 Pith paper

  1. [1]

    @_ sL ;,찁 ö

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command E...

    arXiv 2021

  2. [2]

    2024, , 274, 12, 10.3847/1538-4365/ad5c69

    Alsing , J., Thorp , S., Deger , S., et al. 2024, , 274, 12, 10.3847/1538-4365/ad5c69

    doi:10.3847/1538-4365/ad5c69 2024

  3. [3]

    2024, , 527, 11372, 10.1093/mnras/stad3902

    Asada , Y., Sawicki , M., Abraham , R., et al. 2024, , 527, 11372, 10.1093/mnras/stad3902

    doi:10.1093/mnras/stad3902 2024

  4. [4]

    2011, , 743, 121, 10.1088/0004-637X/743/2/121

    Atek , H., Siana , B., Scarlata , C., et al. 2011, , 743, 121, 10.1088/0004-637X/743/2/121

    doi:10.1088/0004-637x/743/2/121 2011

  5. [5]

    2023, , 524, 5486, 10.1093/mnras/stad1998

    Atek , H., Chemerynska , I., Wang , B., et al. 2023, , 524, 5486, 10.1093/mnras/stad1998

    doi:10.1093/mnras/stad1998 2023

  6. [6]

    M., D'Eugenio , F., Maiolino , R., et al

    Baker , W. M., D'Eugenio , F., Maiolino , R., et al. 2025, arXiv e-prints, arXiv:2501.09070, 10.48550/arXiv.2501.09070

    arXiv doi:10.48550/arxiv.2501.09070 2025

  7. [7]

    L., Morris , S

    Balogh , M. L., Morris , S. L., Yee , H. K. C., Carlberg , R. G., & Ellingson , E. 1999, , 527, 54, 10.1086/308056

    doi:10.1086/308056 1999

  8. [8]

    2023, , 525, 5388, 10.1093/mnras/stad2617

    Bassini , L., Feldmann , R., Gensior , J., et al. 2023, , 525, 5388, 10.1093/mnras/stad2617

    doi:10.1093/mnras/stad2617 2023

Show all 136 references
  1. [9]

    E., et al

    Bezanson , R., Labbe , I., Whitaker , K. E., et al. 2024, , 974, 92, 10.3847/1538-4357/ad66cf

    2024 doi

  2. [10]

    2021, The Journal of Open Source Software, 6, 3001, 10.21105/joss.03001

    Buchner , J. 2021, The Journal of Open Source Software, 6, 3001, 10.21105/joss.03001

    2021 doi

  3. [11]

    J., Conroy , C., & Johnson , B

    Byler , N., Dalcanton , J. J., Conroy , C., & Johnson , B. D. 2017, , 840, 44, 10.3847/1538-4357/aa6c66

    2017 doi

  4. [12]

    C., et al

    Calzetti , D., Armus , L., Bohlin , R. C., et al. 2000, , 533, 682, 10.1086/308692

    2000 doi

  5. [13]

    2019, , 487, 3845, 10.1093/mnras/stz1449

    Caplar , N., & Tacchella , S. 2019, , 487, 3845, 10.1093/mnras/stz1449

    2019 doi

  6. [14]

    M., Kartaltepe , J

    Casey , C. M., Kartaltepe , J. S., Drakos , N. E., et al. 2023, , 954, 31, 10.3847/1538-4357/acc2bc

    2023 doi

  7. [15]

    2024, , 527, 7871, 10.1093/mnras/stad3709

    Cenci , E., Feldmann , R., Gensior , J., et al. 2024, , 527, 7871, 10.1093/mnras/stad3709

    2024 doi

  8. [16]

    2003, , 115, 763, 10.1086/376392

    Chabrier , G. 2003, , 115, 763, 10.1086/376392

    2003 doi

  9. [17]

    Charlot , S., & Fall , S. M. 2000, , 539, 718, 10.1086/309250

    2000 doi

  10. [18]

    2016, , 462, 1415, 10.1093/mnras/stw1756

    Chevallard , J., & Charlot , S. 2016, , 462, 1415, 10.1093/mnras/stw1756

    2016 doi

  11. [19]

    2017, , 838, 159, 10.3847/1538-4357/aa679f

    Choi , J., Conroy , C., & Byler , N. 2017, , 838, 159, 10.3847/1538-4357/aa679f

    2017 doi

  12. [20]

    2016, , 823, 102, 10.3847/0004-637X/823/2/102

    Choi , J., Dotter , A., Conroy , C., et al. 2016, , 823, 102, 10.3847/0004-637X/823/2/102

    2016 doi

  13. [21]

    2024, , 686, A128, 10.1051/0004-6361/202348091

    Ciesla , L., Elbaz , D., Ilbert , O., et al. 2024, , 686, A128, 10.1051/0004-6361/202348091

    2024 doi

  14. [22]

    2013, , 51, 393, 10.1146/annurev-astro-082812-141017

    Conroy , C. 2013, , 51, 393, 10.1146/annurev-astro-082812-141017

    2013 doi

  15. [23]

    Conroy , C., & Gunn , J. E. 2010, , 712, 833, 10.1088/0004-637X/712/2/833

    2010 doi

  16. [24]

    R., Hutter , A., Dayal , P., et al

    Cueto , E. R., Hutter , A., Dayal , P., et al. 2024, , 686, A138, 10.1051/0004-6361/202349017

    2024 doi

  17. [25]

    C., Birnboim , Y., Mandelker , N., & Li , Z

    Dekel , A., Sarkar , K. C., Birnboim , Y., Mandelker , N., & Li , Z. 2023, , 523, 3201, 10.1093/mnras/stad1557

    2023 doi

  18. [26]

    2024, , 527, 2139, 10.1093/mnras/stad3239

    Dome , T., Tacchella , S., Fialkov , A., et al. 2024, , 527, 2139, 10.1093/mnras/stad3239

    2024 doi

  19. [27]

    2016, , 222, 8, 10.3847/0067-0049/222/1/8

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

    2016 doi

  20. [28]

    T., & Li , A

    Draine , B. T., & Li , A. 2007, , 657, 810, 10.1086/511055

    2007 doi

  21. [29]

    J., Willott , C., Alberts , S., et al

    Eisenstein , D. J., Willott , C., Alberts , S., et al. 2023, arXiv e-prints, arXiv:2306.02465, 10.48550/arXiv.2306.02465

    2023 doi arXiv

  22. [30]

    R., et al

    Emami , N., Siana , B., Weisz , D. R., et al. 2019, , 881, 71, 10.3847/1538-4357/ab211a

    2019 doi

  23. [31]

    P., Topping , M

    Endsley , R., Chisholm , J., Stark , D. P., Topping , M. W., & Whitler , L. 2024, arXiv e-prints, arXiv:2410.01905, 10.48550/arXiv.2410.01905

    2024 doi arXiv

  24. [32]

    L., Capak , P

    Faisst , A. L., Capak , P. L., Emami , N., Tacchella , S., & Larson , K. L. 2019, , 884, 133, 10.3847/1538-4357/ab425b

    2019 doi

  25. [33]

    L., & Morishita , T

    Faisst , A. L., & Morishita , T. 2024, , 971, 47, 10.3847/1538-4357/ad58e2

    2024 doi

  26. [34]

    2018, , 473, 3717, 10.1093/mnras/stx2595

    Faucher-Gigu \`e re , C.-A. 2018, , 473, 3717, 10.1093/mnras/stx2595

    2018 doi

  27. [35]

    F., Faucher-Gigu \`e re , C.-A., & Kere s , D

    Feldmann , R., Quataert , E., Hopkins , P. F., Faucher-Gigu \`e re , C.-A., & Kere s , D. 2017, , 470, 1050, 10.1093/mnras/stx1120

    2017 doi

  28. [36]

    2023, , 522, 3831, 10.1093/mnras/stad1205

    Feldmann , R., Quataert , E., Faucher-Gigu \`e re , C.-A., et al. 2023, , 522, 3831, 10.1093/mnras/stad1205

    2023 doi

  29. [37]

    J., Chatzikos , M., Guzm \'a n , F., et al

    Ferland , G. J., Chatzikos , M., Guzm \'a n , F., et al. 2017, , 53, 385, 10.48550/arXiv.1705.10877

    2017 doi arXiv

  30. [38]

    P., & Bridges , M

    Feroz , F., Hobson , M. P., & Bridges , M. 2009, , 398, 1601, 10.1111/j.1365-2966.2009.14548.x

    2009

  31. [39]

    2024, , 684, A207, 10.1051/0004-6361/202348321

    Ferrara , A. 2024, , 684, A207, 10.1051/0004-6361/202348321

    2024 doi

  32. [40]

    L., Bagley , M

    Finkelstein , S. L., Bagley , M. B., Ferguson , H. C., et al. 2023, , 946, L13, 10.3847/2041-8213/acade4

    2023 doi

  33. [41]

    A., Gurvich , A

    Flores Vel \'a zquez , J. A., Gurvich , A. B., Faucher-Gigu \`e re , C.-A., et al. 2021, , 501, 4812, 10.1093/mnras/staa3893

    2021 doi

  34. [42]

    W., Lang , D., & Goodman , J

    Foreman-Mackey , D., Hogg , D. W., Lang , D., & Goodman , J. 2013, , 125, 306, 10.1086/670067

    2013 doi

  35. [43]

    2025, , 979, 66, 10.3847/1538-4357/ad9a5c

    Gao , Z., Peng , Y., Wang , K., et al. 2025, , 979, 66, 10.3847/1538-4357/ad9a5c

    2025 doi

  36. [44]

    1999, , 306, 843, 10.1046/j.1365-8711.1999.02576.x

    Glazebrook , K., Blake , C., Economou , F., Lilly , S., & Colless , M. 1999, , 306, 843, 10.1046/j.1365-8711.1999.02576.x

    1999

  37. [45]

    2010, Communications in Applied Mathematics and Computational Science, 5, 65, 10.2140/camcos.2010.5.65

    Goodman , J., & Weare , J. 2010, Communications in Applied Mathematics and Computational Science, 5, 65, 10.2140/camcos.2010.5.65

    2010 doi

  38. [46]

    2012, , 422, 1231, 10.1111/j.1365-2966.2012.20696.x

    Governato , F., Zolotov , A., Pontzen , A., et al. 2012, , 422, 1231, 10.1111/j.1365-2966.2012.20696.x

    2012

  39. [47]

    M., et al

    Guo , Y., Rafelski , M., Faber , S. M., et al. 2016, , 833, 37, 10.3847/1538-4357/833/1/37

    2016 doi

  40. [48]

    2022, , 938, 11, 10.3847/1538-4357/ac7b84

    Hahn , C., & Melchior , P. 2022, , 938, 11, 10.3847/1538-4357/ac7b84

    2022 doi

  41. [49]

    J., Hobson , M

    Handley , W. J., Hobson , M. P., & Lasenby , A. N. 2015, , 453, 4384, 10.1093/mnras/stv1911

    2015 doi

  42. [50]

    2023, , 265, 5, 10.3847/1538-4365/acaaa9

    Harikane , Y., Ouchi , M., Oguri , M., et al. 2023, , 265, 5, 10.3847/1538-4365/acaaa9

    2023 doi

  43. [51]

    R., Millman , K

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

    2020 doi

  44. [52]

    2013, , 208, 19, 10.1088/0067-0049/208/2/19

    Hinshaw , G., Larson , D., Komatsu , E., et al. 2013, , 208, 19, 10.1088/0067-0049/208/2/19

    2013 doi

  45. [53]

    J., Chartier , N., et al

    Ho , M., Bartlett , D. J., Chartier , N., et al. 2024, The Open Journal of Astrophysics, 7, 54, 10.33232/001c.120559

    2024 doi

  46. [54]

    D., & Gelman , A

    Hoffman , M. D., & Gelman , A. 2011, arXiv e-prints, arXiv:1111.4246, 10.48550/arXiv.1111.4246

    2011 doi arXiv

  47. [55]

    F., Kere s , D., O \ n orbe , J., et al

    Hopkins , P. F., Kere s , D., O \ n orbe , J., et al. 2014, , 445, 581, 10.1093/mnras/stu1738

    2014 doi

  48. [56]

    F., Gurvich , A

    Hopkins , P. F., Gurvich , A. B., Shen , X., et al. 2023 a , , 525, 2241, 10.1093/mnras/stad1902

    2023 doi

  49. [57]

    F., Wetzel , A., Wheeler , C., et al

    Hopkins , P. F., Wetzel , A., Wheeler , C., et al. 2023 b , , 519, 3154, 10.1093/mnras/stac3489

    2023 doi

  50. [58]

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

    2007 doi

  51. [59]

    G., Gawiser , E., Faber , S

    Iyer , K. G., Gawiser , E., Faber , S. M., et al. 2019, , 879, 116, 10.3847/1538-4357/ab2052

    2019 doi

  52. [60]

    G., Speagle , J

    Iyer , K. G., Speagle , J. S., Caplar , N., et al. 2024, , 961, 53, 10.3847/1538-4357/acff64

    2024 doi

  53. [61]

    G., Tacchella , S., Genel , S., et al

    Iyer , K. G., Tacchella , S., Genel , S., et al. 2020, , 498, 430, 10.1093/mnras/staa2150

    2020 doi

  54. [62]

    D., Leja , J., Conroy , C., & Speagle , J

    Johnson , B. D., Leja , J., Conroy , C., & Speagle , J. S. 2021, , 254, 22, 10.3847/1538-4365/abef67

    2021 doi

  55. [63]

    D., Weisz , D

    Johnson , B. D., Weisz , D. R., Dalcanton , J. J., et al. 2013, , 772, 8, 10.1088/0004-637X/772/1/8

    2013 doi

  56. [64]

    A., Nabergoj , D., & Seljak , U

    Karamanis , M., Beutler , F., Peacock , J. A., Nabergoj , D., & Seljak , U. 2022, , 516, 1644, 10.1093/mnras/stac2272

    2022 doi

  57. [65]

    1998, , 36, 189, 10.1146/annurev.astro.36.1.189

    Kennicutt , Robert C., J. 1998, , 36, 189, 10.1146/annurev.astro.36.1.189

    1998 doi

  58. [66]

    2018, , 473, 776, 10.1093/mnras/stx2379

    Kettlety , T., Hesling , J., Phillipps , S., et al. 2018, , 473, 776, 10.1093/mnras/stx2379

    2018 doi

  59. [67]

    2022, Machine Learning: Science and Technology, 3, 04LT04, 10.1088/2632-2153/ac98f4

    Khullar , G., Nord , B., \'C iprijanovi \'c , A., Poh , J., & Xu , F. 2022, Machine Learning: Science and Technology, 3, 04LT04, 10.1088/2632-2153/ac98f4

    2022 doi

  60. [68]

    Lange , J. U. 2023, , 525, 3181, 10.1093/mnras/stad2441

    2023 doi

  61. [69]

    C., Gil de Paz , A., Tremonti , C., et al

    Lee , J. C., Gil de Paz , A., Tremonti , C., et al. 2009, , 706, 599, 10.1088/0004-637X/706/1/599

    2009 doi

  62. [70]

    2022, , 509, 595, 10.1093/mnras/stab3034

    Legrand , L., Hutter , A., Dayal , P., et al. 2022, , 509, 595, 10.1093/mnras/stab3034

    2022 doi

  63. [71]

    C., Johnson , B

    Leja , J., Carnall , A. C., Johnson , B. D., Conroy , C., & Speagle , J. S. 2019, , 876, 3, 10.3847/1538-4357/ab133c

    2019 doi

  64. [72]

    D., Conroy , C., & van Dokkum , P

    Leja , J., Johnson , B. D., Conroy , C., & van Dokkum , P. 2018, , 854, 62, 10.3847/1538-4357/aaa8db

    2018 doi

  65. [73]

    D., Conroy , C., van Dokkum , P

    Leja , J., Johnson , B. D., Conroy , C., van Dokkum , P. G., & Byler , N. 2017, , 837, 170, 10.3847/1538-4357/aa5ffe

    2017 doi

  66. [74]

    S., Ting , Y.-S., et al

    Leja , J., Speagle , J. S., Ting , Y.-S., et al. 2022, , 936, 165, 10.3847/1538-4357/ac887d

    2022 doi

  67. [75]

    2024, , 167, 16, 10.3847/1538-3881/ad0be4

    Li , J., Melchior , P., Hahn , C., & Huang , S. 2024, , 167, 16, 10.3847/1538-3881/ad0be4

    2024 doi

  68. [76]

    J., D'Eugenio , F., Maiolino , R., et al

    Looser , T. J., D'Eugenio , F., Maiolino , R., et al. 2023, arXiv e-prints, arXiv:2306.02470, 10.48550/arXiv.2306.02470

    2023 doi arXiv

  69. [77]

    2024, , 629, 53, 10.1038/s41586-024-07227-0

    ---. 2024, , 629, 53, 10.1038/s41586-024-07227-0

    2024 doi

  70. [78]

    1995, , 441, 18, 10.1086/175332

    Madau , P. 1995, , 441, 18, 10.1086/175332

    1995 doi

  71. [79]

    V., van der Wel , A., Rix , H.-W., et al

    Maseda , M. V., van der Wel , A., Rix , H.-W., et al. 2014, , 791, 17, 10.1088/0004-637X/791/1/17

    2014 doi

  72. [80]

    2018, , 854, 29, 10.3847/1538-4357/aaa76e

    ---. 2018, , 854, 29, 10.3847/1538-4357/aaa76e

    2018 doi

  73. [81]

    A., Trenti , M., & Treu , T

    Mason , C. A., Trenti , M., & Treu , T. 2023, , 521, 497, 10.1093/mnras/stad035

    2023 doi

  74. [82]

    2023, , 526, 2196, 10.1093/mnras/stad2734

    Mauerhofer , V., & Dayal , P. 2023, , 526, 2196, 10.1093/mnras/stad2734

    2023 doi

  75. [83]

    F., & Ostriker , E

    McKee , C. F., & Ostriker , E. C. 2007, , 45, 565, 10.1146/annurev.astro.45.051806.110602

    2007 arXiv

  76. [84]

    I., Scarlata , C., et al

    Mehta , V., Teplitz , H. I., Scarlata , C., et al. 2023, , 952, 133, 10.3847/1538-4357/acd9cf

    2023 doi

  77. [85]

    Naab , T., & Ostriker , J. P. 2017, , 55, 59, 10.1146/annurev-astro-081913-040019

    2017 doi

  78. [86]

    P., Oesch , P

    Naidu , R. P., Oesch , P. A., van Dokkum , P., et al. 2022, , 940, L14, 10.3847/2041-8213/ac9b22

    2022 doi

  79. [87]

    2020, , 889, 180, 10.3847/1538-4357/ab65eb

    Nanayakkara , T., Brinchmann , J., Glazebrook , K., et al. 2020, , 889, 180, 10.3847/1538-4357/ab65eb

    2020 doi

  80. [88]

    2009, , 507, 1793, 10.1051/0004-6361/200912497

    Noll , S., Burgarella , D., Giovannoli , E., et al. 2009, , 507, 1793, 10.1051/0004-6361/200912497

    2009 doi

  81. [89]

    2018, , 70, S10, 10.1093/pasj/psx103

    Ono , Y., Ouchi , M., Harikane , Y., et al. 2018, , 70, S10, 10.1093/pasj/psx103

    2018 doi

  82. [90]

    G., Mobasher , B., et al

    Pacifici , C., Iyer , K. G., Mobasher , B., et al. 2023, , 944, 141, 10.3847/1538-4357/acacff

    2023 doi

  83. [91]

    2022, , 509, 1885, 10.1093/mnras/stab3071

    Pacucci , F., & Loeb , A. 2022, , 509, 1885, 10.1093/mnras/stab3071

    2022 doi

  84. [92]

    2023, , 677, L4, 10.1051/0004-6361/202347384

    Pallottini , A., & Ferrara , A. 2023, , 677, L4, 10.1051/0004-6361/202347384

    2023 doi

  85. [93]

    M., & Zemel , Y

    Panaretos , V. M., & Zemel , Y. 2019, Annual Review of Statistics and Its Application, 6, 405, 10.1146/annurev-statistics-030718-104938

    2019 doi

  86. [94]

    Papovich , C., Dickinson , M., & Ferguson , H. C. 2001, , 559, 620, 10.1086/322412

    2001 doi

  87. [95]

    2024, , 976, 72, 10.3847/1538-4357/ad7e15

    Park , M., Belli , S., Conroy , C., et al. 2024, , 976, 72, 10.3847/1538-4357/ad7e15

    2024 doi

  88. [96]

    2018, , 473, 4077, 10.1093/mnras/stx2656

    Pillepich , A., Springel , V., Nelson , D., et al. 2018, , 473, 4077, 10.1093/mnras/stx2656

    2018 doi

  89. [97]

    H., Bezanson , R., Labbe , I., et al

    Price , S. H., Bezanson , R., Labbe , I., et al. 2025, , 982, 51, 10.3847/1538-4357/adaec1

    2025 doi

  90. [98]

    2023, , 525, L117, 10.1093/mnrasl/slad091

    Renzini , A. 2023, , 525, L117, 10.1093/mnrasl/slad091

    2023 doi

  91. [99]

    E., Tacchella , S., Johnson , B

    Robertson , B. E., Tacchella , S., Johnson , B. D., et al. 2023, Nature Astronomy, 7, 611, 10.1038/s41550-023-01921-1

    2023 doi

  92. [100]

    F., Jim \'e nez-Vicente , J., et al

    S \'a nchez-Bl \'a zquez , P., Peletier , R. F., Jim \'e nez-Vicente , J., et al. 2006, , 371, 703, 10.1111/j.1365-2966.2006.10699.x

    2006

  93. [101]

    2023, , 525, 3254, 10.1093/mnras/stad2508

    Shen , X., Vogelsberger , M., Boylan-Kolchin , M., Tacchella , S., & Kannan , R. 2023, , 525, 3254, 10.1093/mnras/stad2508

    2023 doi

  94. [102]

    2024, , 961, 50, 10.3847/1538-4357/ad06a7

    Sipple , J., & Lidz , A. 2024, , 961, 50, 10.3847/1538-4357/ad06a7

    2024 doi

  95. [103]

    2004, in American Institute of Physics Conference Series, Vol

    Skilling , J. 2004, in American Institute of Physics Conference Series, Vol. 735, Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, ed. R. Fischer...

    2004 doi

  96. [104]

    S., & Dav \'e , R

    Somerville , R. S., & Dav \'e , R. 2015, , 53, 51, 10.1146/annurev-astro-082812-140951

    2015 doi

  97. [105]

    C., Feldmann , R., et al

    Sparre , M., Hayward , C. C., Feldmann , R., et al. 2017, , 466, 88, 10.1093/mnras/stw3011

    2017 doi

  98. [106]

    Speagle , J. S. 2020, , 493, 3132, 10.1093/mnras/staa278

    2020 doi

  99. [107]

    S., Steinhardt , C

    Speagle , J. S., Steinhardt , C. L., Capak , P. L., & Silverman , J. D. 2014, , 214, 15, 10.1088/0067-0049/214/2/15

    2014 doi

  100. [108]

    S., Dalcanton , J

    Stinson , G. S., Dalcanton , J. J., Quinn , T., Kaufmann , T., & Wadsley , J. 2007, , 667, 170, 10.1086/520504

    2007 doi

  101. [109]

    2023, , 949, L23, 10.3847/2041-8213/acd457

    Strait , V., Brammer , G., Muzzin , A., et al. 2023, , 949, L23, 10.3847/2041-8213/acd457

    2023 doi

  102. [110]

    A., Leja , J., Johnson , B

    Suess , K. A., Leja , J., Johnson , B. D., et al. 2022, , 935, 146, 10.3847/1538-4357/ac82b0

    2022 doi

  103. [111]

    A., Weaver , J

    Suess , K. A., Weaver , J. R., Price , S. H., et al. 2024, , 976, 101, 10.3847/1538-4357/ad75fe

    2024 doi

  104. [112]

    C., & Shen , X

    Sun , G., Faucher-Gigu \`e re , C.-A., Hayward , C. C., & Shen , X. 2023 a , , 526, 2665, 10.1093/mnras/stad2902

    2023 doi

  105. [113]

    C., et al

    Sun , G., Faucher-Gigu \`e re , C.-A., Hayward , C. C., et al. 2023 b , , 955, L35, 10.3847/2041-8213/acf85a

    2023 doi

  106. [114]

    B., Mirocha , J., & Faucher-Gigu \`e re , C.-A

    Sun , G., Mu \ n oz , J. B., Mirocha , J., & Faucher-Gigu \`e re , C.-A. 2024, arXiv e-prints, arXiv:2410.21409, 10.48550/arXiv.2410.21409

    2024 doi arXiv

  107. [115]

    M., et al

    Tacchella , S., Dekel , A., Carollo , C. M., et al. 2016, , 458, 242, 10.1093/mnras/stw303

    2016 doi

  108. [116]

    M., et al

    Tacchella , S., Conroy , C., Faber , S. M., et al. 2022, , 926, 134, 10.3847/1538-4357/ac449b

    2022 doi

  109. [117]

    2008, ConPh, 49, 71, 10.1080/00107510802066753

    Trotta , R. 2008, ConPh, 49, 71, 10.1080/00107510802066753

    2008 doi

  110. [118]

    Trussler , J. A. A., Conselice , C. J., Adams , N., et al. 2025, , 537, 3662, 10.1093/mnras/staf213

    2025 doi

  111. [119]

    2011, , 730, 38, 10.1088/0004-637X/730/1/38

    van der Wel , A., Rix , H.-W., Wuyts , S., et al. 2011, , 730, 38, 10.1088/0004-637X/730/1/38

    2011 doi

  112. [120]

    2024, , 973, L32, 10.3847/2041-8213/ad77b8

    van Dokkum , P., & Conroy , C. 2024, , 973, L32, 10.3847/2041-8213/ad77b8

    2024 doi

  113. [121]

    E., et al

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

    2020 doi

  114. [122]

    T., Tacchella , S., Johnson , B

    Wan , J. T., Tacchella , S., Johnson , B. D., et al. 2024, , 532, 4002, 10.1093/mnras/stae1734

    2024 doi

  115. [123]

    A., & Speagle , J

    Wang , B., Leja , J., Villar , V. A., & Speagle , J. S. 2023 a , , 952, L10, 10.3847/2041-8213/ace361

    2023 doi

  116. [124]

    2023 b , , 944, L58, 10.3847/2041-8213/acba99

    Wang , B., Leja , J., Bezanson , R., et al. 2023 b , , 944, L58, 10.3847/2041-8213/acba99

    2023 doi

  117. [125]

    2024 a , , 963, 74, 10.3847/1538-4357/ad187c

    Wang , B., Leja , J., Atek , H., et al. 2024 a , , 963, 74, 10.3847/1538-4357/ad187c

    2024 doi

  118. [126]

    2024 b , , 270, 12, 10.3847/1538-4365/ad0846

    Wang , B., Leja , J., Labb \'e , I., et al. 2024 b , , 270, 12, 10.3847/1538-4365/ad0846

    2024 doi

  119. [127]

    2024 c , , 969, L13, 10.3847/2041-8213/ad55f7

    Wang , B., Leja , J., de Graaff , A., et al. 2024 c , , 969, L13, 10.3847/2041-8213/ad55f7

    2024 doi

  120. [128]

    J., et al

    Weibel , A., de Graaff , A., Setton , D. J., et al. 2024, arXiv e-prints, arXiv:2409.03829, 10.48550/arXiv.2409.03829

    2024 doi arXiv

  121. [129]

    2017, , 465, 3291, 10.1093/mnras/stw2944

    Weinberger , R., Springel , V., Hernquist , L., et al. 2017, , 465, 3291, 10.1093/mnras/stw2944

    2017 doi

  122. [130]

    R., Johnson , B

    Weisz , D. R., Johnson , B. D., Johnson , L. C., et al. 2012, , 744, 44, 10.1088/0004-637X/744/1/44

    2012 doi

  123. [131]

    E., van Dokkum , P

    Whitaker , K. E., van Dokkum , P. G., Brammer , G., & Franx , M. 2012, , 754, L29, 10.1088/2041-8205/754/2/L29

    2012 doi

  124. [132]

    P., Endsley , R., et al

    Whitler , L., Stark , D. P., Endsley , R., et al. 2023, , 519, 5859, 10.1093/mnras/stad004

    2023 doi

  125. [133]

    J., Doyon , R., Albert , L., et al

    Willott , C. J., Doyon , R., Albert , L., et al. 2022, , 134, 025002, 10.1088/1538-3873/ac5158

    2022 doi

  126. [134]

    2025, , 537, 112, 10.1093/mnras/staf001

    Witten , C., McClymont , W., Laporte , N., et al. 2025, , 537, 112, 10.1093/mnras/staf001

    2025 doi

  127. [135]

    Yung , L. Y. A., Somerville , R. S., Finkelstein , S. L., Wilkins , S. M., & Gardner , J. P. 2024, , 527, 5929, 10.1093/mnras/stad3484

    2024 doi

  128. [136]

    2023, in Machine Learning for Astrophysics, 38, 10.48550/arXiv.2312.03824

    Zhang , K., Bloom , J., & Hernitschek , N. 2023, in Machine Learning for Astrophysics, 38, 10.48550/arXiv.2312.03824

    2023 doi arXiv

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.