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A statistical study of lopsided galaxies using random forest

T0 review · 2 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A random forest trained only on internal galaxy properties can pre-classify lopsided versus symmetric disk galaxies with 81% balanced accuracy, supporting the view that lopsidedness is mainly a tracer of internal structure.

desk verdict A useful random-forest preselection tool for lopsided galaxies, but the accuracy numbers are inflated by snapshot leakage and the physical conclusion overreaches without an environment baseline. read the letter →

arxiv 2411.19723 v3 pith:TQEHTO5W submitted 2024-11-29 astro-ph.GA astro-ph.IM

Valentina Fontirroig , Facundo A. Gomez , Marcelo Jaque Arancibia , Arianna Dolfi , Nicolas Monsalves This is my paper · ORCID
classification astro-ph.GAastro-ph.IM
keywords lopsidedgalaxiesgalaxyclassificationrandomforestTNG50simulationFourierdecompositioninternalstructuremachinelearningphotometricsurveys
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

Lopsided galaxies, whose stellar disks are asymmetrically distributed, are common but hard to characterize at survey scale. The paper claims that a random forest trained only on internal galaxy properties can quickly pre-classify simulated disk galaxies as lopsided or symmetric, with balanced accuracy 0.813 ± 0.010 on the TNG50 test set and 0.799 ± 0.009 when only photometrically observable features are used. Because no environmental information enters the features, the authors read the high accuracy as strong support for the hypothesis that lopsidedness is mainly a tracer of a galaxy's internal structure. The practical payoff would be a fast preselection tool for large multiband photometric surveys, with Fourier decomposition reserved for the smaller pre-selected sample.

What carries the argument

The load-bearing object is A1, the radially averaged amplitude of the m=1 mode of the stellar mass surface density, computed within a cylinder of width 1.4R90 and height 2h90 and averaged over the radial interval R50–1.4R90; galaxies with A1>0.1 are labeled lopsided. This Fourier label is the ground truth. The classifier is a random forest ensemble, with SMOTE oversampling used to balance the minority symmetric class, trained on ten internal parameters; permutation importance shows that central stellar mass density μ*, tidal parameter TP, and star formation rate carry most of the predictive signal, with μ* dominant. In the observable-feature variant, M50 is replaced by r-band luminosity within R50 and only R50, Rext, c/a, and SFR remain as inputs, while the training pipeline stays the same.

What would settle it

Retrain the identical SMOTE+RF pipeline using a galaxy-grouped split that keeps all snapshots of the same galaxy in the same fold, and measure balanced accuracy on held-out galaxies. If the grouped-split accuracy drops well below 0.81 (toward 0.5–0.6), the reported generalization is inflated by snapshot overlap. A complementary check is to run the trained classifier on an independent observed sample of disk galaxies whose A1 values are measured from imaging and to compare predicted versus actual labels.

Watch

Extended reading notes

Core claim

The paper's central claim is that the m=1 Fourier asymmetry A1, averaged over the radial interval R50–1.4R90 and thresholded at 0.1, can be predicted from a small set of internal galaxy parameters. On a held-out 30% of the TNG50 sample, the SMOTE+RF classifier reaches balanced accuracy 0.813±0.010, and substituting r-band luminosity and other photometric proxies for the mass-based features leaves the performance almost unchanged at 0.799±0.009. The authors interpret this as evidence that lopsidedness is primarily an internal-structure phenomenon, not a direct map of present-day environment. They also find that the model's errors cluster near the A1=0.1 threshold and are physically intelligible: some misclassifications have symmetric interiors with a recently tidally disturbed outer disk, while others have lopsided-prone interiors that are merely unperturbed at the current snapshot.

Load-bearing premise

The key load-bearing assumption is that randomly splitting the simulated galaxy snapshots into training and test sets gives independent samples, even though each galaxy reappears at multiple snapshots between z=0 and z=0.5; if later snapshots of a galaxy resemble the earlier ones used for training, the ~81% balanced accuracy could partly reflect memorization of individual systems rather than generalization to unseen galaxies.

Editorial extensions

If this is right

  • Large multiband surveys can use the classifier as a fast preselection step, flagging lopsided candidates and limiting expensive Fourier decomposition or visual inspection to a smaller subset.
  • The high accuracy with no environment features supports treating lopsidedness as a structural indicator, which simplifies theoretical interpretation of survey samples.
  • The observable-feature version at ~80% balanced accuracy suggests the method transfers to photometric catalogs without stellar population or dynamical modeling of each galaxy.
  • The misclassification structure implies the pre-selected sample will still be clean enough for statistical studies as long as borderline A1 cases are treated as a separate, low-confidence category.

Reading between the lines

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

  • Because the paper's split is over snapshots, not over unique galaxies, and each TNG50 galaxy appears at several redshifts, a galaxy-grouped cross-validation is the natural next test; I would expect the true generalization accuracy for never-seen galaxies to be lower than the reported 0.813.
  • The authors' own misclassification analysis suggests lopsidedness is partly a transient state: a galaxy can be structurally prone to lopsidedness but currently symmetric, or internally symmetric but temporarily lopsided after an interaction. Pushing this further, a single snapshot label mixes at least three physically distinct populations, so evolutionary conclusions drawn from pre-selected sample
  • A testable extension would be to train the same pipeline separately on the three subsets defined by the misclassification analysis (recently perturbed, structurally prone, borderline) and check whether their feature distributions are separable; if they are, a three-class or regression formulation would be more informative than a binary preselector.
  • For real photometric surveys, the simulation-trained model will face redshift-dependent surface brightness dimming, PSF dilution, and differences between simulated and observed SFR proxies; re-calibrating on a labeled observed subsample would be necessary before the reported ~80% accuracy is trusted on survey data.
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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

2 major / 5 minor

Summary. The paper trains random forest classifiers on 7,919 late-type galaxy snapshots from TNG50, labeling each snapshot as lopsided or symmetric using the radial average of the m=1 Fourier amplitude A1 over the interval R50–1.4R90 with a threshold of 0.1. The authors compare two imbalance-handling approaches, SMOTE+RF and Balanced Random Forest, and report a balanced accuracy of 0.813±0.010 (Table 3), with similar performance (~0.799±0.009, Table 5) when using photometrically observable features (R50, Rext, c/a, SFR, L50). They interpret the high accuracy as evidence that lopsidedness is mainly a tracer of internal galaxy structure, and propose the classifier as a fast preselection tool for large multiband surveys. The paper also includes a feature-importance analysis, a study of misclassified galaxies with illustrative snapshots and interaction histories, and a discussion of the physical meaning of borderline A1 classifications.

Significance. If the reported accuracy reflects generalization to unseen galaxies, this would be a useful practical contribution: a fast, automated preselector for lopsided galaxies, with a clearly described pipeline and careful treatment of class imbalance (SMOTE and BRF, balanced accuracy as primary metric). The case studies of misclassified galaxies in Section 4.3 are informative and provide physical insight into the borderline nature of the A1-based label. However, the two concerns below—train/test leakage from repeated snapshots of the same galaxies and the self-predictive relationship between features and label—mean that the headline accuracies cannot currently be taken at face value, and the physical interpretation in Section 5 is stronger than the evidence supports. The analysis is reproducible in principle, though no code is provided.

major comments (2)
  1. [Sections 2.2 and 3.2; Tables 3 and 5] The sample consists of galaxy snapshots over z=0–0.5; Section 2.2 explicitly states that a given galaxy 'will be present at different snapshots of the simulation' and 'will serve as input for the training process.' The train/test split in Section 3.2 uses StratifiedShuffleSplit on the 7,919 rows without grouping by subhalo ID. Because the structural features (µ*, TP, R50, Rext, M50, SFR) and the A1 label are strongly autocorrelated across lookback times up to ~5 Gyr, later snapshots of a galaxy used in training can appear in the test set, allowing the random forest to exploit per-galaxy identity rather than a general rule. The balanced accuracies in Tables 3 and 5 are therefore estimates of performance on partially seen galaxies, not on independent galaxies, and do not yet support the survey preselection claim. The authors should redo the split at the galaxy level (e.g., GroupShuffleSplit or GroupKFold on subhalo ID) and report metrics for galaxies whose entire evolutionary tracks were excluded from training.
  2. [Sections 3.1, 2.2 (Table 1), and 5] The label A1 is the radially averaged m=1 Fourier amplitude of the stellar mass distribution over R50–1.4R90, while the top-ranked features in Table 4—µ*, TP, M50, and also R50 and Rext—are integrals or moments of the same simulated stellar mass distribution. The classifier is thus mapping one set of summary statistics of a density field onto another summary statistic of the same field. The high accuracy therefore does not by itself test whether lopsidedness is 'mainly a tracer of galaxies internal structures' rather than of environment, since no environment-based features are included for comparison. The authors should either temper this interpretation (noting that the result is partly a self-consistency check of the mass distribution), or add a control experiment that includes environment features (e.g., host halo mass, local density, distance to nearest neighbor) and compares accuracies or feature importances, to directly address the environment hypothesis.
minor comments (5)
  1. [Abstract and Section 2.2] The abstract states '≈ 8000 late-type galaxies' while Section 2.2 gives 7,919; please harmonize the numbers.
  2. [Table 3] The BRF TNR row reports the uncertainty as 0.00; if this is a rounding artifact, please give at least two significant digits.
  3. [Section 4.4] The text states that the observational-feature model produces 535 misclassifications, 'a 15% increase' relative to the 455 misclassifications of the full-feature model; the increase is actually 17.6%, and the wording should clarify that this is an increase in errors.
  4. [Section 4.2] Permutation importance is computed on the test set only; this is nonstandard because the test set is meant to be used once. Consider computing permutation importance on a validation split or on the training set to avoid optimistic feature-importance estimates.
  5. [Throughout] Minor typographical issues include 'stellar participles' in Section 3.1 and 'Richter & Sacisi 1994' in the references (should be 'Sancisi'); also, the numbers quoted for correct/incorrect classifications in Fig. 5 are slightly inconsistent with the reported TPR/TNR values, so the rounding should be checked.

Circularity Check

1 steps flagged · score 6.0 of 10

Repeated snapshots of the same TNG50 galaxies enter both the training and test sets, so the reported balanced accuracies (0.813 and 0.799) reflect partly within-galaxy memorization rather than generalization to unseen galaxies.

  1. fitted input called prediction [Section 2.2 (selection criteria) and Section 3.2 (train/test split); reported in Tables 3 and 5]
    "Note that, even though a given galaxy will be present at different snapshots of the simulation, their detailed structure will evolve (see e.g., Varela-Lavin et al. 2023) and, thus, it will serve as input for the training process. ... we partitioned the dataset into a training set and a testing set comprising 70% and 30% of the total sample, respectively. To do so, we employed stratifiedshufflesplit from scikit-learn."

    The sample consists of 7,919 rows that are snapshots of the same TNG50 subhalos across z = 0 to z = 0.5, not 7,919 independent galaxies. The row-level StratifiedShuffleSplit does not group by subhalo ID, so early snapshots of a galaxy can appear in the training set while later snapshots of the same galaxy are placed in the test set. Because structural features and the A1 label are strongly autocorrelated over the roughly 5 Gyr spanned, the classifier can effectively recognize the individual galaxy rather than learn a general internal-property rule. The headline balanced accuracies (0.813 +/- 0.010 and 0.799 +/- 0.009) are therefore not estimates of performance on unseen galaxies, which is exactly the quantity required for the survey preselection claim.

full rationale

The paper's derivation chain is otherwise self-contained: A1 is computed by Fourier decomposition, the features are tabulated in Table 1, and the classifier is a standard SMOTE+RF/BRF pipeline with no imported uniqueness theorem and no load-bearing self-citation chain. The central flaw is statistical rather than algebraic: the train/test split is performed on snapshot rows rather than on unique galaxies, and Section 2.2 explicitly notes that the same galaxy appears at multiple snapshots and will serve as input for the training process. Because no grouping by subhalo ID is described, the same galaxy can appear in both training and test, so the reported accuracies partly reflect memorization of individual systems. This falls under fitted-input-called-prediction: the test prediction is not genuinely out-of-sample. The further interpretive claim that high classifier accuracy strongly supports an internal origin of lopsidedness is an overreach, since lopsidedness is itself an internal structural property and the model never tests the environmental alternative; however, that is an inference issue rather than a circular derivation. No other circular steps were found.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

Everything in the test is internal to TNG50: the labels come from a Fourier decomposition of the simulated stellar mass distribution, and the top features (mu*, TP, Rext) are derived from the same simulated mass distribution. In addition, galaxy snapshots are not grouped by galaxy identity, so training and test sets may share the same galaxies at different redshifts. No observed galaxy sample is used for validation, and no code or data are released.

free parameters (3)
  • A1 lopsidedness threshold = 0.1
    Adopted threshold for labeling lopsided versus symmetric; standard in the literature but a free choice that sets the class balance and thus the classifier's target.
  • Radial interval for A1 = R50 to 1.4R90
    The authors state that 'this radial interval best represent the non-axisymmetry of the sample' (Section 3.1), i.e., chosen by hand to emphasize the m=1 signal, affecting the label definition.
  • SMOTE+RF hyperparameters = n_trees=1500, max_depth=75, etc.
    Selected by randomized search on the same data; they affect reported scores, though results are stable across metrics.
assumptions (4)
  • domain assumption TNG50 simulation faithfully represents real galaxy structure and lopsidedness
    All training and test labels come from TNG50, so the classifier's accuracy is only as good as the simulation's realism.
  • domain assumption Fourier m=1 amplitude A1 averaged over R50-1.4R90 correctly measures lopsidedness
    Used as ground truth for labels; a different radial weighting or threshold would change the target and the measured accuracy.
  • domain assumption Galaxy snapshots at different redshifts can be treated as independent samples
    The same galaxy appears at multiple snapshots; the paper does not group by galaxy identity, so this independence may not hold (Section 2.2).
  • ad hoc to paper Selected structural features can be measured without reference to the same A1 label
    Features and label are computed from the same simulated stellar mass distribution, so the classifier may exploit a structural correlation that is partly by construction.

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Cite this review

Pith. "Pith review of A statistical study of lopsided galaxies using random forest." pith.science (2026). https://pith.science/paper/TQEHTO5W

@misc{pith2026241119723,
  author       = {Pith},
  title        = {Pith review of: A statistical study of lopsided galaxies using random forest},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TQEHTO5W}},
  note         = {Machine review of arXiv:2411.19723}
}
read the original abstract

Lopsided galaxies are late-type galaxies with a non-axisymmetric disk due to an uneven distribution of their stellar mass. Despite being a relatively common perturbation, several questions regarding its origin and the information that can be extracted from them about the evolutionary history of late-type galaxies. The advent of several large multi-band photometric surveys will allow us to statistically analyze this perturbation, with information that was not previously available. Given the strong correlation between lopsidedness and the structural properties of the galaxies, this paper aims to develop a method to automatically classify late-type galaxies between lopsided and symmetric. We seek to explore if an accurate classification can be obtain by only considering their internal properties, without additional information about the environment. We select 8000 late-type galaxies from TNG50. A Fourier decomposition of their stellar mass surface density is used to label galaxies as lopsided and symmetric. We trained a Random Forest classifier to rapidly and automatically identify this type of perturbations, exclusively using galaxies internal properties. We test different algorithms to deal with the imbalance of our data and select the most suitable approach based on the considered metrics. We show that our trained algorithm can provide a very accurate and rapid classification of lopsided galaxies. The excellent results obtained by our classifier strongly supports the hypothesis that lopsidedness is mainly a tracer of galaxies internal structures. We show that similar results can be obtained using observable quantities, readily obtainable from multi-bad photometric surveys. Our results show it allows a rapid and accurate classification of lopsided galaxies, allowing us to explore whether lopsidedness in present-day disk galaxies is connected to galaxies specific evolutionary histories.

Figures

Figures reproduced from arXiv: 2411.19723 by the authors.

Figure 1
Figure 1. Heatmap of the Pearson Coefficient Correlation of the features listed in [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. V-band face-on projected surface brightness distribution of a symmetric (left) and lopsided (right) galaxy, considered as examples of the classification made by A1. Their respective A1 value, ID (as in TNG50-1), and redshift snapshot are plotted on the upper side. On the lower left, the box size considered for each galaxy is also plotted. For both images, the dashed cyan line represents the radius R50 and the solid … view at source ↗
Figure 3
Figure 3. A1 distribution of our total sample. A1 is defined as the aver￾aged strength of the m = 1 mode of the Fourier decomposition for each stellar particle within the radial range R50 − 1.4R90. The black line rep￾resents the threshold used to distinguish between lopsided (orange) and symmetric galaxies (blue). The dashed distributions represent the in￾correct classifications of the galaxies made by SMOTE+RF for testing se… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Distribution of parameters selected to characterize our galaxy sample. These parameters are used as features by the random forest classifier. The orange and blue distributions represent lopsided and symmetric galaxies, respectively. The dashed colored lines represent t…
Figure 5
Figure 5. Figure 5: Confusion matrix for the testing set of the best model, SMOTE+RF. The x axis is the predicted class or predicted label, and the y axis is the actual class or actual label. The percentage with respect each type of galaxy set is on parenthesis. value are labeled as the n…
Figure 6
Figure 6. Figure 6: Box plot of each feature from the testing set, ranked by their importance as determined by the feature permutation attribute from SMOTE+RF. Each box represents the range of the different scores ob￾tained from a cross-validation with niter = 5. The inner dashed line rep…
Figure 7
Figure 7. Figure 7: Radial profiles of A1 for our four classification cases, calculated as the median of A1 for each bin with respect to R90. The fuchsia and blue distributions represent the correctly classified lopsided galaxies (LGA1 − LGm) and symmetric galaxies (SGA1 − SGm), respectiv…
Figure 8
Figure 8. Figure 8: Normalized distribution of µ∗ (left), TP (middle), and Rext (right), considering the correct (upper) and incorrect (bottom) classification made by SMOTE+RF. Each distribution has been normalized by their corresponding number of galaxies of each subsample. Their respect…
Figure 9
Figure 9. Figure 9: These localized asymmetries, captured by the global [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 9
Figure 9. Figure 9: (top panels) V-band face-on projected surface brightness distribution of a (left) symmetric galaxy pre-classified as lopsided (SGA1 − LGm) and a (right) lopsided galaxy pre-classified as symmetric (LGA1 −SGm), considered as examples of the misclassification made by SMO…
Figure 10
Figure 10. Figure 10: (left) Confusion matrix of the testing set using SMOTE+RF with only observational parameters. The x axis is the predicted class or predicted label, and the y axis is the actual class or actual label. The percentage with respect each class is on parenthesis. (right) Bo…

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

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