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REVIEW 2 major objections 6 minor 84 references

A machine learning approach to estimating HI deficiency in galaxies

T0 review · 2 major / 6 minor · reviewed 2026-07-09 · glm-5.2

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Pith's one-line read Random forest predicts galaxy gas loss from starlight alone

desk verdict Solid incremental ML approach to HI deficiency; needs cross-validation and a domain-shift check, but the core result is defensible. read the letter →

arxiv 2607.07441 v1 pith:6PJWGSVL submitted 2026-07-08 astro-ph.GA

Filip Janák , Boris Deshev , Roman Nagy , Rhys Taylor This is my paper · ORCID
classification astro-ph.GA PACS 98.62.-g98.62.Ai98.62.Gq98.58.-j
keywords deficiencygalaxiesmodelapproxcontentalfalfaeffectsgalaxy
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 trains a random forest algorithm on 6,982 isolated galaxies — systems chosen because they should have had no environmental gas stripping — to predict each galaxy's neutral hydrogen (HI) mass from 17 optical properties (magnitudes, radii, colors, concentration indices in SDSS g/r/i bands). The model learns what the 'normal' HI content of a galaxy looks like given its optical appearance, achieving a prediction scatter of 0.22 dex versus 0.26 dex for the traditional linear relation between HI mass and optical diameter. When the model is applied to 8,232 non-isolated galaxies (those living in groups and clusters), the difference between the predicted 'expected' HI mass and the actually observed HI mass — the HI deficiency — increases by 0.15 dex in binned median from sparse to dense environments. This confirms that environment removes gas from galaxies, and that a machine-learned mapping from optical properties to gas content can detect that removal more precisely than the classical linear method. The paper also simulates how the predicted HI deficiency evolves after a gas removal event: because optical light is dominated by young stars that persist long after the gas is gone, the optical properties of a stripped galaxy still resemble a gas-rich galaxy for 1-4 billion years, causing the model to underestimate the true deficiency by 0.1-0.28 dex depending on how rapidly the gas was removed.

What carries the argument

The central object is a random forest regressor mapping 17 SDSS optical features (absolute Petrosian magnitudes, Petrosian radii at 50% and 90% flux, concentration indices, and colors in g, r, i bands) to log HI mass. The model is trained on galaxies flagged as isolated (group membership N_gal=1, no AGN) from the ALFALFA HI survey cross-matched with SDSS photometry. The most important predictors are the g-band absolute magnitude, the r-band absolute magnitude, and the g-band 90%-flux Petrosian radius. HI deficiency is then computed as the logarithmic difference between this model-predicted 'expected' HI mass and the ALFALFA-observed HI mass for non-isolated galaxies.

What would settle it

Apply the RF model to a sample of galaxies with independently measured HI deficiency from resolved HI imaging (e.g., VLA or MeerKAT maps showing truncated gas disks) and check whether the model-predicted deficiency correlates with the spatially measured gas truncation. A systematic underestimation of deficiency for galaxies with known truncated disks — especially those recently infalling into clusters — would confirm the time-lag bias the paper itself identifies.

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Extended reading notes

Core claim

A random forest model trained on isolated galaxies predicts expected HI mass from optical properties with 0.22 dex scatter (R²≈0.80), outperforming the classical linear size-mass relation (0.26 dex, R²≈0.70). Applied to non-isolated galaxies, the model recovers a 0.15 dex environmental signal in HI deficiency and reveals that the signal is time-dependent: optical properties lag behind gas loss by up to several billion years, meaning HI deficiency is systematically underestimated for recently stripped galaxies.

Load-bearing premise

The model assumes that the optical-to-HI mapping learned on isolated galaxies represents the unaltered gas content that non-isolated galaxies would have had in the absence of environmental effects. But optical properties evolve on stellar-evolution timescales (billions of years) while gas can be removed much faster, so a galaxy that was stripped recently still looks optically like a gas-rich galaxy, causing the model to overpredict its expected HI mass and underestimate itsHI

Editorial extensions

If this is right

  • The 0.15 dex environmental signal is a lower bound: the time-lag analysis shows that recently stripped galaxies have their deficiency underestimated because their optical properties still reflect a gas-rich past, so the true environmental gas loss is likely larger than measured.
  • The model provides a publicly catalogued expected-HI-mass estimate for 8,232 non-isolated ALFALFA galaxies, enabling per-galaxy HI deficiency estimates with ~0.22 dex scatter — tighter than the ~0.3-0.4 dex intrinsic scatter typical of classical methods.
  • Because the RF cannot extrapolate beyond the training-set feature range, the model's applicability is limited to galaxies with optical properties similar to the isolated-galaxy training sample, which is skewed toward gas-rich late-type systems.
  • The time-evolution toy model suggests that HI deficiency becomes undetectable for galaxies observed more than ~4 Gyr after gas removal (for rapid stripping), implying that census of environmentally stripped galaxies is incomplete unless the time since infall is accounted for.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 6 minor

Summary. This manuscript develops a random forest (RF) regression model to predict the expected HI mass of galaxies from 17 SDSS optical photometric features, trained on 6,982 isolated (N_gal=1) ALFALFA galaxies. The RF model achieves RMSE≈0.22 dex and R²≈0.80, outperforming the traditional linear HI size-mass relation (RMSE≈0.26 dex, R²≈0.70). The model is then applied to 8,232 non-isolated galaxies to compute HI deficiency, revealing a ~0.15 dex increase in binned median deficiency from sparse to dense environments. A toy model in Sect. 4.1 explores how post-gas-removal stellar population aging causes the predicted deficiency to evolve by 0.1–0.28 dex over 1–4 Gyr, establishing that the measured signal is a time-diluted lower bound. A catalog of predicted HI masses is publicly released on Zenodo.

Significance. The paper makes a useful methodological contribution by applying RF regression to the HI deficiency problem on a substantially larger and more homogeneous sample than prior work. The improvement over the linear size-mass relation is modest but real (0.04 dex in RMSE), and the feature importance analysis (g-band magnitude and R90,g dominating) is physically interpretable. The public release of the predicted HI mass catalog and the supplementary Zenodo figures is a positive reproducibility step. The Sect. 4.1 toy model, while qualitative, addresses a genuine and underappreciated systematic — the lag between optical and HI evolution after gas removal — and correctly frames the environmental signal as a lower bound. The environmental trends are consistent with established literature, serving as an external sanity check rather than a novel discovery.

major comments (2)
  1. §4, Fig. 6: The only validation that RF predictions are unbiased for non-isolated galaxies is the aggregate comparison of M_HI/M* distributions between IG and nIG samples. This is necessary but insufficient: it cannot detect a systematic prediction bias that correlates with environment. The 0.15 dex environmental signal (Fig. 8) is smaller than the model's own RMSE (0.22 dex), so a domain-shift bias of comparable magnitude could dominate the signal. The authors should add a test for prediction bias as a function of environment — e.g., compare RF-predicted M_HI to observed M_HI for nIG within narrow stellar mass and color bins, stratified by N_gal or density. If no systematic trend in residuals with environment is found, this would substantially strengthen the central claim. If such a trend exists, it should be quantified and its impact on the 0.15 dex signal assessed.
  2. §3.4: Only a single 80:20 train-test split is reported. For a sample of ~7,000 galaxies, k-fold cross-validation (e.g., 5- or 10-fold) would provide a more robust estimate of model performance and its variance. The current RMSE and R² could be optimistic due to the particular split. This is load-bearing for the claim that the RF model outperforms the linear model, since the improvement (0.04 dex) is modest relative to the scatter. Reporting cross-validated metrics with error bars on RMSE and R² for both models would address this.
minor comments (6)
  1. §2.1: The Malmquist bias discussion is qualitative. A quantitative statement about the mass completeness limit at a representative distance would help readers assess the severity. Consider citing the distance at which a 10^9 M_sun galaxy would fall below the ALFALFA completeness threshold, or at minimum noting that the choice not to apply a distance cut is a deliberate trade-off.
  2. Table 2: The 0.1th percentile for r-i color is listed as -0.77, which seems unphysically low for typical galaxies. Please verify this value.
  3. §3.3, Eq. 9: The D25 = 1.4 × R90,g conversion is adopted from Deshev et al. (2022), calibrated for gas-rich late-types. The manuscript notes this but does not quantify the uncertainty introduced. A brief estimate of how scatter in this conversion affects the linear model comparison would be useful.
  4. Fig. 8: The y-axis range (±0.2 dex) is narrow relative to the quartile scatter (~0.3 dex). Consider widening the axis or adding a note clarifying that the plotted medians are well within the per-galaxy scatter, so readers do not overinterpret the visual trend.
  5. §4.1: The toy model fixes the optical radius and only evolves luminosity/color. The text acknowledges this is a lower limit, but does not discuss how size evolution (which the RF model weights via R90,g at ~31% permutation importance) might interact with the luminosity evolution. A sentence noting whether size evolution would amplify or partially cancel the luminosity-driven effect would help.
  6. The paper would benefit from a brief comparison table or paragraph placing the RF model's performance in context with Teimoorinia et al. (2017) and Wu (2020), noting differences in target variable (M_HI vs. gas fraction), feature set, and sample selection that complicate direct comparison.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for a careful and constructive report. Both major comments are well-taken and address genuine methodological gaps. We agree to implement both requested analyses: (1) a test for prediction bias as a function of environment, stratified by stellar mass and color, and (2) k-fold cross-validation with error bars for both the RF and linear models. We outline our planned revisions below.

read point-by-point responses

  1. Referee: §4, Fig. 6: The only validation that RF predictions are unbiased for non-isolated galaxies is the aggregate comparison of M_HI/M* distributions between IG and nIG samples. This is necessary but insufficient: it cannot detect a systematic prediction bias that correlates with environment. The 0.15 dex environmental signal (Fig. 8) is smaller than the model's own RMSE (0.22 dex), so a domain-shift bias of comparable magnitude could dominate the signal. The authors should add a test for prediction bias as a function of environment — e.g., compare RF-predicted M_HI to observed M_HI for nIG within narrow stellar mass and color bins, stratified by N_gal or density. If no systematic trend in residuals with environment is found, this would substantially strengthen the central claim. If such a trend exists, it should be quantified and its impact on the 0.15 dex signal assessed.

    Authors: The referee is correct that the aggregate M_HI/M* comparison in Fig. 6 cannot detect a prediction bias that correlates with environment. This is a genuine gap in our validation, and we agree it must be addressed given that the 0.15 dex environmental signal is indeed smaller than the model's RMSE. We will implement the following test in the revised manuscript. For the nIG sample, we will compute residuals (observed M_HI minus RF-predicted M_HI) within narrow bins of stellar mass and g−r color, stratified by N_gal and by the 3 Mpc environmental density. If no systematic trend in residuals with environment is found within these narrow bins, this will directly demonstrate that domain-shift bias is not driving the signal. If a trend is present, we will quantify its magnitude and subtract it from the measured 0.15 dex environmental signal, reporting the corrected value. We note that the nIG sample does have ALFALFA HI detections (it is not a sample without observed M_HI), so this residual analysis is feasible. We will add the results as a new figure and accompanying discussion in Section 4. We fully agree that this test is essential for the central claim and will frame it as such. revision: yes

  2. Referee: §3.4: Only a single 80:20 train-test split is reported. For a sample of ~7,000 galaxies, k-fold cross-validation (e.g., 5- or 10-fold) would provide a more robust estimate of model performance and its variance. The current RMSE and R² could be optimistic due to the particular split. This is load-bearing for the claim that the RF model outperforms the linear model, since the improvement (0.04 dex) is modest relative to the scatter. Reporting cross-validated metrics with error bars on RMSE and R² for both models would address this.

    Authors: The referee is correct that a single train-test split does not provide robust error estimates, and the 0.04 dex improvement over the linear model is modest enough that its statistical significance needs to be established. We will implement 10-fold cross-validation for both the RF and linear models on the full IG sample (6,982 galaxies), reporting the mean and standard deviation of RMSE and R² across folds for each model. This will allow a direct assessment of whether the RF improvement is statistically significant relative to the fold-to-fold variance. We will update Section 3.4, Table 4, and Figures 4–5 accordingly, and revise the abstract to report cross-validated metrics. If the improvement is not statistically significant at a level the referee would consider adequate, we will adjust our claims accordingly — for instance, by stating that the RF model performs comparably or modestly better, rather than 'noticeably better.' We agree this is load-bearing for the paper's methodological contribution. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the RF model is trained on isolated galaxies' observed M_HI, applied to a distinct non-isolated population, and the environmental signal emerges without being fitted.

full rationale

The paper's derivation chain is self-contained and non-circular. The RF model (Sect. 3.4) is trained on 6,982 isolated galaxies (N_gal=1) with observed ALFALFA M_HI as the target and 17 optical SDSS features as predictors. The training target is the observed HI mass of isolated galaxies; the application target is the HI deficiency of non-isolated galaxies. These are distinct populations, and the environmental tracers (N_gal, M_200, density) are not among the 17 model features. The 0.15 dex environmental trend (Sect. 4, Fig. 8) is an emergent property of applying the trained model to a different population, not a fitted quantity. The linear model comparison (Eq. 9) is a standard regression on the same IG sample, evaluated on a held-out test set. The time-evolution toy model (Sect. 4.1) uses external stellar population synthesis (DSPS) to qualitatively estimate how optical evolution affects predictions; it is explicitly labeled a lower-bound demonstration, not a prediction. Self-citations exist (Deshev et al. 2022 for the D25 conversion factor; Taylor et al. 2020 for optical/HI equilibrium discussion; Taylor 2025 for ALFALFA completeness), but none are load-bearing for the central derivation: the D25 conversion is a minor calibration step, and the other citations are supporting context. The transferability assumption (that the IG-trained model applies unbiasedly to nIG) is a validity risk, not a circularity — the paper does not define its prediction in terms of the quantity it claims to derive.

Assumptions & free parameters 9 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities, particles, forces, or dimensions. It uses standard astronomical catalogs (ALFALFA, SDSS, Tempel et al. 2017) and standard ML methods (random forest, scikit-learn). The DSPS stellar population synthesis package is an external tool. All free parameters are either fitted from data (linear model coefficients), chosen by hyperparameter optimization (RF parameters), or adopted from prior literature (D25 conversion). The toy model parameters (tau, initial SFR duration) are explicitly stated as illustrative rather than fitted.

free parameters (9)
  • RF n_estimators = 300
    Number of decision trees, chosen by hyperparameter optimization (Sect. 3.4)
  • RF max_samples (bootstrap fraction) = 0.8
    Fraction of training set used in bootstrapping, chosen by hyperparameter optimization (Sect. 3.4)
  • RF max_features = 0.8
    Fraction of features considered per split, chosen by hyperparameter optimization (Sect. 3.4)
  • Linear model a (intercept) = 7.64±0.02
    Fitted intercept for log(M_HI) vs log(D25) relation, Eq. 9
  • Linear model b (slope) = 1.55±0.01
    Fitted slope for log(M_HI) vs log(D25) relation, Eq. 9
  • D25 conversion factor = 1.4
    Multiplicative factor converting R90,g to D25, adopted from Deshev et al. 2022 (Sect. 3.3)
  • Stellar mass split threshold = 10.5 (log M*/M_sun)
    Threshold separating low-mass and high-mass subsamples for environmental analysis (Sect. 4)
  • Toy model tau values = {0.25, 0.5, 0.75, 1.0} Gyr
    Exponential SFR decline timescales chosen to sample different gas removal mechanisms (Sect. 4.1)
  • Toy model initial SFR duration = 5 Gyr
    Duration of constant SFR before exponential decline in the DSPS simulation (Sect. 4.1)
assumptions (5)
  • domain assumption Galaxies with N_gal=1 in the Tempel et al. (2017) catalog are truly isolated and their HI content represents the unaltered baseline.
    This is the foundational assumption for the entire approach: the RF model trained on these galaxies predicts the 'expected' HI mass. Invoked in Sect. 3.2 where isolated galaxies are defined.
  • domain assumption The optical-to-HI mapping learned on isolated galaxies transfers to non-isolated galaxies without systematic bias.
    The model is applied to nIG galaxies (Sect. 4) assuming the learned relationship holds. The paper itself shows this mapping is time-dependent (Sect. 4.1), creating tension with this assumption.
  • domain assumption ALFALFA HI detections are sufficiently complete for the analysis without requiring upper limits for non-detections.
    Sect. 2.1 states the analysis is restricted to confirmed detections. This biases the sample toward gas-rich galaxies, acknowledged but not corrected.
  • domain assumption The D25=1.4×R90,g conversion is valid for the full IG sample despite being calibrated for gas-rich LTGs.
    Sect. 3.3 adopts this conversion from Deshev et al. 2022. The paper notes it is 'expected to be most reliable for systems with similar T-type' but applies it broadly.
  • ad hoc to paper Standard random forest regression without measurement error treatment is adequate for the current analysis.
    Sect. 3.4 acknowledges the RF cannot account for measurement errors but proceeds without error propagation, deferring this to future work.

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Cite this review

Pith. "Pith review of A machine learning approach to estimating HI deficiency in galaxies." pith.science (2026). https://pith.science/paper/6PJWGSVL

@misc{pith2026260707441,
  author       = {Pith},
  title        = {Pith review of: A machine learning approach to estimating HI deficiency in galaxies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6PJWGSVL}},
  note         = {Machine review of arXiv:2607.07441}
}
abstract

Measurements of the HI content of galaxies serve as an important tracer for probing the impact of environment on galaxy evolution. More specifically, the HI deficiency (defined as the difference between expected unaltered and observed HI content of a galaxy) is closely related with environmental effects, which are most significant in large groups and clusters. In this work, we aim to estimate the HI deficiency of ALFALFA galaxies and investigate its relation with galactic environment. Using a random forest machine learning algorithm, we developed a predictive model capable of estimating the original HI content of a galaxy based solely on its optical properties. The model was trained on a subsample of 6 982 isolated ALFALFA galaxies with optical photometric data from the Sloan Digital Sky Survey (SDSS). Our predictive model outperforms the traditional approach, in which HI mass is linearly related to optical size (both on a logarithmic scale). The model achieves RMSE $\approx$ 0.22 dex and $R^2 \approx$ 0.80, compared with RMSE $\approx$ 0.26 dex and $R^2 \approx$ 0.70 for the traditional method. We applied this model to predict the expected HI content for non-isolated ALFALFA galaxies, enabling the calculation of HI deficiency. Controlling for the effects of internal factors, like stellar mass and presence of AGN, we find an increase in binned median HI deficiency of 0.15 dex attributable to environmental effects. In addition, we evaluate the temporal evolution of the predicted HI mass, and associated HI deficiency, due to the evolving stellar populations, following a gas removal event.

Figures

Figures reproduced from arXiv: 2607.07441 by the authors.

Figure 1
Figure 1. The morphology distribution of 14 288 ALFALFA sources with respect to the M [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. The Pearson correlation matrix for fea￾tures and the target variable for our sample of IG. work, we adopted a correction inspired by Deshev et al. (2022) to transform the optical radius of IG into D25. Specifically, we used the SDSS Pet￾rosian radius containing 90% of the total flux in g band, adopting the conversion D25 = 1.4 × R90,g. This conversion was originally calibrated for gas￾rich, late-type galaxies and is… view at source ↗
Figure 3
Figure 3. H i mass from our sample of IG versus the optical diameter D25 linearly fitted with the green line, compared with Haynes & Giovanelli (1984) (grey dashed line), Solanes et al. (1996) (blue dashdotted line), Toribio et al. (2011) (red dotted line), D´enes et al. (2014) (black loosely dashed line) and Jones et al. (2018) (cyan loosely dotted line). MH i and the optical diameter for different mor￾phologies in the Appen… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: The scatter plot of the predicted and ALFALFA MH i evaluated on the test set for the linear model (upper panel) and the RF model (lower panel). 1.5 1.0 0.5 0.0 0.5 1.0 1.5 log10(MHI, ALFALFA) log10(MHI, predicted) 10 2 10 1 10 0 normalized counts RF model Linear model …
Figure 5
Figure 5. Figure 5: The distributions of residuals (log10MHI,ALF ALF A − log10MHI,predicted) eval￾uated on the test set for the RF model (red, hatched histogram), linear model (black line histogram) and Jones et al. (2018) linear model (blue, dashed line histogram). 11 [PITH_FULL_IMAGE:f…
Figure 6
Figure 6. Figure 6: The distributions of the H i to stellar mass ratio for the IG sample (red) and the nIG sample with observed (green) and predicted (blue) MH i , using equally populated bins. The shaded area represents the 1st and 3rd quartiles of distri￾butions. 4. logMHI e: expected l…
Figure 7
Figure 7. Figure 7: H i deficiency histograms for the low￾mass and high-mass nIG subsamples. four environmental tracers (as introduced in Sect. 2.3). Galaxies in each subsample are binned into seven equally populated bins. The x-axis posi￾tion of each bin is determined as the middle value…
Figure 8
Figure 8. Figure 8: The binned distribution of the H i deficiency with respect to different environmental tracers; orange circles represent median values for low-mass galaxies (log10 M∗ M⊙ < 10.5) and green squares denote median values for high-mass galaxies (log10 M∗ M⊙ ≥ 10.5). The shad…
Figure 9
Figure 9. Figure 9: The distribution of H i deficiencies (computed with the RF model) for the nIG sample (solid histograms) and their expected evolution driven by the aging stellar populations (empty histograms). Separate panels show the expected evolution result of exponentially declinin…
Figure 10
Figure 10. Figure 10: H i mass for isolated galaxies versus the optical diameter D25 for three subsamples ac￾cording to the T-type: T-type < 3 (851 galaxies), 3 < T-type < 5 (2956 galaxies) and T-type > 5 (1913 galaxies). The linear fit is plotted with the green line, compared with Jones e…

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