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Identifying and Distinguishing Quenching Galaxies with Spatially Resolved Star Formation in Mock CASTOR and NGRST Observations

T0 review · 3 major / 4 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read Using synthetic images of galaxies as future ultraviolet and near-infrared space surveys will see them, this paper shows that annular star-formation profiles can reveal not only whether a galaxy is quenching but whether star formation is sh

desk verdict Solid feasibility test of Paper I quenching metrics through mock CASTOR/NGRST observations; the recovery numbers are best-case because the same SPS library is used for injection and fitting, but the paper is transparent about it. read the letter →

arxiv 2607.15638 v1 pith:UEFJQR6X submitted 2026-07-17 astro-ph.GA

Cameron Lawlor-Forsyth , Michael L. Balogh , Sean L. McGee , Gregory H. Rudnick This is my paper
classification astro-ph.GA
keywords galaxyquenchingspatiallyresolvedstarformationmorphologicalmetricsmockobservationsultravioletsurveysnear-infraredspectralenergydistributionfittingmachinelearningclassification
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 asks whether upcoming space telescopes that image galaxies in ultraviolet and near-infrared light can show how a galaxy is shutting down star formation. Using a cosmological simulation that produces realistic galaxy populations, the authors generate synthetic images as the proposed surveys would see them, fit the light in concentric rings, and recover radial profiles of stellar mass and star formation rate. They then apply four simple shape metrics—concentration of star formation, relative size of the star-forming disk, and inner/outer truncation radii—and show these metrics separate quenched galaxies from normally star-forming ones, and separate galaxies quenching from the inside out from those quenching from the outside in. Machine-learning classification of the metrics also recovers the stage of the quenching episode, and survey-abundance calculations suggest thousands of such galaxies will be observable, making morphology alone a viable diagnostic of quenching physics.

What carries the argument

The analysis rests on fitting the spectral energy distribution in 20 concentric circular annuli using a flexible star-formation history—a delayed-tau model with a multiplicative suppression factor in the last 100 Myr—to turn multi-band images into radial profiles of stellar mass and star formation rate. Four morphological metrics carry the diagnosis: the concentration of star formation within 1 kpc, the ratio of the star-forming disk size to the stellar disk size, and two radii where the specific star-formation profile drops sharply, indicating truncation from inside or outside. These are computed from the fitted profiles and fed to a k-nearest-neighbors classifier.

What would settle it

Create mock observations with an independent stellar-population synthesis library and a radiative-transfer dust model, then run the same annular fitting and metric pipeline; if the recovered star-formation-rate scatter substantially exceeds the reported 0.46 dex, or if the machine-learning separation of inside-out vs outside-in falls below random-chance at late stages, the central claim would be weakened.

Watch

Extended reading notes

Core claim

The central claim is that the spatial distribution of young stars, as measured from annular photometry in deep ultraviolet plus near-infrared imaging, carries enough information to diagnose both the mode and the progress of star-formation quenching. In mock observations designed to match two planned space surveys, the authors recover radial star-formation and mass profiles with median offsets of about -0.13 dex (scatter 0.46 dex) in star formation rate, and use them to reconstruct four morphological metrics. These metrics separate quenching from star-forming galaxies, separate inside-out from outside-in quenching signatures, and allow a machine-learning classifier to estimate where a galaxy

Load-bearing premise

The mock images are generated with the same stellar-population and dust models that the fitting code assumes, so the reported accuracy is a best-case estimate; real galaxies with more complex star-formation histories, metallicities, and dust geometries may not yield radial profiles as cleanly recoverable.

Editorial extensions

If this is right

  • If the recovery holds in real data, surveys combining ultraviolet and near-infrared imaging can identify quenching galaxies without spectroscopy.
  • The distinction between inside-out and outside-in quenching becomes measurable from imaging alone, enabling statistical studies of quenching physics.
  • The estimated stage within the quenching episode can be recovered for early and late phases, allowing reconstruction of quenching timelines from a snapshot sample.
  • Survey forecasts indicate thousands of quenching galaxies in deep fields out to redshift one, offering a large sample for environmental comparisons.
  • The method's dependence on high signal-to-noise annuli means low-mass or high-redshift galaxies will have less reliable profiles, limiting application to more massive or intermediate-redshift galaxies.

Reading between the lines

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

  • A natural next test is to generate mock images with independent stellar-population synthesis and dust treatments, then fit with the same pipeline; if recovery degrades significantly, real-galaxy application will need more cautious priors.
  • The same metrics might be applied to existing deep imaging from current space telescopes as an analog for the proposed surveys, providing an early empirical check on the method.
  • Because the classifier was trained and tested on the same simulation, its reported accuracy is a ceiling; applying it to a different simulation or to real galaxies would likely lower accuracies, especially at early quenching times.
  • The dust model used here is a simple foreground screen; future work with radiative-transfer-based attenuation could test whether the metric recovery is robust to realistic dust geometry.
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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

3 major / 4 minor

Summary. The paper builds mock CASTOR and NGRST images of IllustrisTNG50 galaxies from the Paper I quenched-galaxy sample, including stellar populations generated with the Bruzual & Charlot (2003) models, a radially varying foreground-screen dust toy model, a fixed Gaussian PSF, and survey-like noise. It then performs annular SED fitting with FAST++ using the same BC03 models and Calzetti dust law, reconstructs radial stellar mass and star formation rate profiles, and computes observational proxies for four morphological metrics (C_SF, R_SF, R_inner, R_outer). The authors validate recovery of these metrics, use a kNN classifier to separate star-forming, inside-out, and outside-in quenching populations and to estimate quenching-episode progress, and conclude by predicting the abundance of such galaxies in proposed CASTOR and NGRST surveys. The central claim is that these morphological metrics, applied to mock observations, can recover information about the intrinsic quenching state of galaxies from morphology alone.

Significance. If the recovery accuracy is robust, this is a useful feasibility study for the next generation of UV/optical/NIR imaging surveys: it gives concrete predictions for the number of quenching galaxies accessible to CASTOR and NGRST, tests SNR limits per annulus, and carefully reports median offsets and scatters. The paper is admirably explicit about many of its limitations, and the SED-fitting validation is thorough in its use of per-annulus comparisons and SNR thresholds. The main value would be a demonstration that radial SFR and stellar mass profiles can be recovered from future space-based photometry and that simple morphology-based metrics can separate quenching modes. However, the recovery is, as the authors acknowledge, a best-case scenario because the same stellar population library and dust law are used in both the forward model and the fitting; the quantitative accuracy claims therefore need to be interpreted as internal consistency rather than as measured performance on real galaxies.

major comments (3)
  1. [§2.4.1, §2.5, Fig. 5; acknowledged in §4.4] The recovery experiment is circular by construction. Mock images are generated with BC03 models and a Calzetti attenuation law (Sections 2.4.1-2.4.2), and FAST++ fits the same BC03 library, Chabrier IMF, and Calzetti law (Section 2.5). The reported per-annulus SFR offsets and the metric recoveries in Figure 7 therefore measure self-consistency, not accuracy on real galaxies. Section 4.4 labels the result 'a best-case scenario,' but the abstract and Section 5 conclusion state that the method 'can be used to recover information about the intrinsic state of quenching galaxies based only on morphology alone' without this caveat. Please add a cross-library or cross-dust test (e.g., inject with FSPS/BPASS/Maraston or a clumpy dust geometry and fit with the fiducial BC03/Calzetti grid), or explicitly restrict the paper's claims to idealized mocks.
  2. [§2.4.2, Eq. (1)] The dust prior is empirically tuned and directly affects the central claim. Equation (1) sets the amplitude B and floor C from the Greener et al. (2020) median radial attenuation profiles, and the same Calzetti foreground-screen assumption is then used in the SED fit. There is no test of how the choice of B, C, or the dust geometry affects the recovered SFR radial profiles. Since the outer-truncation metric has scatter of 0.58 (Section 3, Figure 7) and depends on low-SNR outer annuli, the claimed separation of inside-out versus outside-in quenching is conditional on this dust model. Please quantify the sensitivity of the recovered metrics to the dust parameters, or add a robustness test with a different dust geometry/radiative transfer treatment.
  3. [§3, Figs. 8-9] The kNN classifications use a 70%/30% split, but the sample contains multiple snapshots along each galaxy's quenching episode (361 unique quenched galaxies expanded to 5,365 quenching snapshots; Section 2.1). It is not stated whether the split is by unique galaxy ID or by snapshot. If the same galaxy contributes epochs to both training and test sets, the reported accuracies are optimistic because adjacent epochs are strongly correlated. Please split by galaxy identity, or report how such contamination is avoided. The abstract's 'reliable' accuracy claim should also acknowledge that a real survey is dominated by star-forming galaxies; Section 4.4 itself notes that false positives will be numerous in the star-forming region of color-color space.
minor comments (4)
  1. [Eq. (1)] The expression B = max(0.2, log M* - 9.5) mixes a dimensionless mass logarithm with extinction magnitudes. Please state units and explicitly define that log M* is log10(M*/Msun). Also, using the same V-band screen for all stellar populations ignores age-dependent dust attenuation, which is relevant for spatially resolved UV-derived SFRs.
  2. [§2.4.3] A single Gaussian PSF with FWHM 0.15'' is used for all 12 bands, while NGRST PSFs are wavelength dependent. This is a reasonable first approximation, but it should be listed as an approximation that can affect color gradients in the inner annuli, especially where the PSF is comparable to the annulus width.
  3. [§2.5, Fig. 5] The text says fitted metallicity and dust values are 'perturbed slightly for visualization purposes' in Figure 5, but the perturbation is not quantified. Please state the perturbation size so the density plots are not misread as exact distributions.
  4. [§4.3] The survey abundance prediction uses the TNG50 quenched-galaxy stellar mass function from Paper I, which the authors note is shifted low relative to observed red galaxies. The text should state more explicitly that the predicted counts are lower limits if the TNG quenching definition is conservative.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the shared SPS/dust setup is an acknowledged best-case self-consistency test, not a derivation that reduces to its inputs.

full rationale

After walking the derivation chain, I find no circular step. The paper creates mock CASTOR/NGRST images from TNG stellar particles using Bruzual & Charlot (2003) SPS models and a Calzetti et al. (2000) dust screen (Sec 2.4), then fits those images with FAST++ using the same SPS library and dust law (Sec 2.5). The recovered SFR/stellar mass are compared against the TNG values, not against the fitter's own outputs; the forward model (star-particle ages/metallicities to fluxes) and inverse model (parametric SFH grid to fluxes) are not algebraically identical, so the nonzero scatter (e.g., -0.13 dex and 0.46 dex for SFR) is a real test of the fitting pipeline under the assumed physics. The paper explicitly labels the same-library setup 'a best-case scenario' in Sec 4.4 and recommends cross-checking with Starburst99, Maraston, FSPS, or BPASS; this is a limitation on external validity, not a circularity. The quenching labels, episode progress, and population definitions come from the TNG simulation and Paper I (a prior, independent paper by the same group); using those as training labels in the ML classification is a controlled feasibility test, not a self-fulfilling prediction. The survey abundance predictions (Sec 4.3) are a Schechter-function extrapolation of the simulated mass function, clearly derived from the simulation rather than from the quantities being predicted. No equation reduces to its own input, and no fitted parameter is relabeled as a prediction.

Assumptions & free parameters 5 free parameters · 7 assumptions · 0 invented entities

The paper does not introduce new physical entities. Its free parameters are the dust model coefficients tuned to external MaNGA measurements, the Schechter function fit to the simulated quenched mass function, and threshold choices from Paper I. The chief circularity is the use of the same SPS models and dust law for both injection and fitting.

free parameters (5)
  • Dust amplitude coefficient B = max(0.2, log(M*/M_sun) - 9.5)
    Eq (1): coefficient of the exponential dust screen; values chosen empirically to match Greener et al. (2020) median attenuation profiles.
  • Dust floor coefficient C = max(0, 0.2 + 0.1*ΔMS)
    Eq (1): constant dust offset depending on distance from the main sequence; empirically tuned, the 0.1 factor has no independent derivation.
  • Schechter function parameters for quenched galaxies = log(M*/M_sun)=11.22, α=-1.21, Φ*=0.47e-3
    Sec 4.3, Eq (7): fit to the TNG50 quenched stellar mass function from Paper I and used to predict survey counts.
  • sSFR truncation threshold = log sSFR = -10.5, |d log sSFR/dR| >= 1
    Eqs (5)-(6): thresholds defining inner/outer truncation radii, from Paper I; not derived from the mock data.
  • Quenching definition thresholds = 2.5th percentile of main-sequence SFH; ±2σ
    Sec 2.1: selection of the quenched sample and episode boundaries from Paper I; if changed, sample and metrics change.
assumptions (7)
  • standard math Bruzual & Charlot (2003) stellar population synthesis models describe real stellar populations over the fitted age/metallicity grid.
    Invoked in Sec 2.4.1 and 2.5; both the galaxev mock pipeline and the FAST++ fitting grid use the same BC03 models with Padova 1994 tracks and Chabrier IMF.
  • domain assumption Calzetti et al. (2000) attenuation law plus a radially-declining foreground screen approximates real dust geometry; full radiative transfer is unnecessary.
    Sec 2.4.2, Eq (1); SKIRT comparison is asserted but not quantified; dust model coefficients B and C are tuned to Greener et al. (2020) median profiles.
  • domain assumption The TNG50 quenched galaxy population and its division into inside-out/outside-in/ambiguous classes is representative of real quenching galaxies.
    Sec 2.1 and Paper I; Sec 4.4 cites work arguing TNG AGN feedback is too strong and environmental stripping too strong, which would bias the morphological signature.
  • domain assumption z=0 simulated galaxies placed at z=0.5 with their existing stellar particles form realistic mock images of intermediate-redshift galaxies.
    Sec 2.4.1: images are output at a single characteristic redshift z=0.5 without evolving galaxy sizes/masses; Sec 4.2 only accounts for surface brightness and angular-size effects.
  • domain assumption The hybrid delayed-tau + 100 Myr suppression SFH (Eq. 2) is flexible enough to capture the true SFR of each annulus.
    Sec 2.5; the two-component SFH imposes a 100 Myr truncation/burst shape that may not match arbitrary real SFHs.
  • ad hoc to paper The sSFR threshold of -10.5 and slope thresholds in Eqs (5)-(6) are appropriate truncation-radius definitions for observational data.
    Adopted from Paper I; chosen constant can shift the inner/outer truncation radii in noisy data.
  • ad hoc to paper SED-fitting the mocks with the same models used to create them gives a meaningful estimate of recovery performance.
    Sec 4.4 acknowledges this is a best-case scenario; the paper does not test cross-library recovery (e.g., FSPS/BPASS for one side).

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Cite this review

Pith. "Pith review of Identifying and Distinguishing Quenching Galaxies with Spatially Resolved Star Formation in Mock CASTOR and NGRST Observations." pith.science (2026). https://pith.science/paper/UEFJQR6X

@misc{pith2026260715638,
  author       = {Pith},
  title        = {Pith review of: Identifying and Distinguishing Quenching Galaxies with Spatially Resolved Star Formation in Mock CASTOR and NGRST Observations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UEFJQR6X}},
  note         = {Machine review of arXiv:2607.15638}
}
read the original abstract

We present synthetic images of galaxies that are in the stages of star formation quenching for the Cosmological Advanced Survey Telescope for Optical and UV Research (CASTOR) and Nancy Grace Roman Space Telescope (NGRST), based on simulations coming from the IllustrisTNG suite, as processed using the stellar population synthesis library \textsc{galaxev}. We account for the effects of dust and various sources of noise to produce mock observations that should mirror real observations. Using these synthetic images, we fit photometric observations in binned circular annuli using \texttt{FAST++} and a flexible star formation history, and recover well the spatially resolved stellar mass and star formation rate. We thereby measure various indicators (morphological metrics) of spatially resolved star formation activity in the context of galaxy quenching. We find that we are able to distinguish quenching galaxies from a mass-matched control sample of normal star forming galaxies. We additionally find that we can distinguish various quenching mechanisms, where galaxies consistent with an inside-out quenching signature can be separated from galaxies that display an outside-in signature. Using machine learning techniques the accuracy of this classification is reliable, and the progress through the quenching episode can be estimated for the different populations of quenching galaxies. We make predictions for the abundance of the various quenching populations in proposed surveys for CASTOR and NGRST, and find that these surveys will enable the classifications of thousands of quenching galaxies out to intermediate redshifts, and more when considering higher redshifts.

Figures

Figures reproduced from arXiv: 2607.15638 by the authors.

Figure 1
Figure 1. Filter transmission curves for the baseline CASTOR bands (UV, u, g), the additional CASTOR bands after using the proposed broadband filter (UVL , u S ), and the bands as part of the NGRST HLWAS Deep tier and Ultradeep component (Z, Y, J, H, F, K, and wide W). a point source depth of ∼27.4 mag (Cotˆ e et al. ´ 2025; Marshall et al. 2025). The Deep Survey will image 83 deg2 over six con￾tiguous regions (Cotˆ e et al. … view at source ↗
Figure 2
Figure 2. Example dust extinction profiles using our toy model from Equa￾tion (1), shown with different colors. At our chosen characteristic redshift of 𝑧 = 0.5, one pixel corresponds with ∼0.31 kpc. tion of radius, binned by galaxy stellar mass and offset from the star forming main sequence (e.g., Brinchmann et al. 2004). They find that galaxies at all stellar masses show more attenua￾tion in the central regions (≲0.5 𝑅e), c… view at source ↗
Figure 3
Figure 3. The mock observation image creation process for example star forming (left), inside-out (center), and outside-in (right) quenching galaxies, where the quenching galaxies are selected to be in the later stages of quenching. The top row displays a two dimensional projection of star formation as measured within the simulation (100 Myr timescale), with contours shown in white that trace the distribution of stellar mass.… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Left: example star formation histories for galaxies at 𝑧 = 0.5 showing different combinations of the 𝑒-folding time 𝜏 and the suppression/burst coefficient 𝑅, using the star formation history as described in Equation (2). The inset shows a zoom into the last ∼200 Myr, …
Figure 5
Figure 5. Figure 5: Comparisons of the fitted results from FAST++ as a function of the simulated properties from TNG. Where relevant we show the logarithm for the given quantities, as noted in the axes labels. In all panels, we show a line of equality (gray). For the fitted metallicity an…
Figure 6
Figure 6. Figure 6: Example radial profiles showing recovered properties (dashed lines) when compared to the simulation (solid lines). Moving from top to bottom, we show the stellar mass radial profile (red, units of 𝑀⊙ kpc−2 ), the star formation rate radial profile (blue, units of 𝑀⊙ yr…
Figure 7
Figure 7. Figure 7: Comparisons of the observational proxies of the morphological metrics using the fitted results from FAST++ as a function of the morphological metrics as computed using TNG. In all panels, we show 2D histograms where the color scale describes the density of points, and …
Figure 8
Figure 8. Figure 8: The accuracy of the machine learning-based nearest neighbor classification as a function of progress (time) through the quenching episode. For each population (star forming, black; inside-out quenching, magenta; outside-in quenching, red), we show median values across …
Figure 9
Figure 9. Figure 9: True (top) and predicted (bottom) quenching episode progress values for inside-out quenching galaxies (left) and outside-in quenching galaxies (right) using a machine learning-based k-nearest neighbors classifier after 1000 iterations. For each panel, the histograms ha…
Figure 10
Figure 10. Figure 10: Expected number of galaxies per stellar mass bin for different redshift shells, determined using the fitted Schechter function as shown in Equation (7) and the total volume of space within each redshift shell. Each redshift shell is shown with distinct colors. lation …
Figure 11
Figure 11. Figure 11: Color–color diagrams showing the median progress through the quenching episode per bin for inside-out (left) and outside-in (right) quenching galaxies, color-coded by progress through the quenching episode. Earlier times in the quenching episode are colored more light…
Figure 12
Figure 12. Figure 12: Similar to the top panels of [PITH_FULL_IMAGE:figures/full_fig_p021_12.png]

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Forward citations

Cited by 1 Pith paper

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

  1. Identifying and Distinguishing Quenching Galaxies with Spatially Resolved Star Formation in the Hubble Frontier Fields

    astro-ph.GA 2026-07 conditional novelty 5.0 of 10

    Using a TNG50-trained kNN classifier on resolved SED maps, the authors identify 129 inside-out and 70 outside-in quenching-pathway candidates in the Hubble Frontier Fields; inside-out candidates are more massive and c...

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