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arxiv: 2606.11313 · v1 · pith:KYSKAH5Bnew · submitted 2026-06-09 · 🌌 astro-ph.GA · astro-ph.IM

Comparison and verification methods to trace interaction-driven disturbances in galaxies

Haotian Lyu , Sarah Brough , Aman Khalid , Alice Desmons , Elizaveta Sazonova This is my paper

Pith reviewed 2026-06-27 12:29 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.IM
keywords galaxy interactionstidal debrisself-supervised learningCAS parametersmerger signaturesvisual classificationstellar mass
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The pith

Self-supervised learning model detects galaxy disturbances with 0.86 recall and low contamination using only a small labeled dataset.

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

This paper evaluates a self-supervised learning model and the CAS parameter method for identifying interaction-driven disturbances in galaxies, using visual classification as the reference. The SSL model, by retraining only its linear classifier, reaches high recall of 0.86 with contamination of 0.2, capturing faint tidal debris better than the CAS method's recall of 0.20 despite the latter's higher precision of 0.77. Both the visual method and SSL show a positive correlation between stellar mass and the fraction of disturbed galaxies. The approach is positioned as useful for large surveys like LSST to get a more complete picture of merger signatures.

Core claim

The SSL model achieves high recall (0.86 +/- 0.04) and low contamination (0.2) by retraining only its linear classifier on a small labelled dataset, making it suitable for identifying a broad set of disturbed systems, including faint tidal debris and other interaction-driven morphological disturbances, thereby providing a more complete census of merger-related features. The CAS method, using the traditional threshold A > 0.35, shows higher precision (0.77) but lower recall (0.20). Visual classification and the SSL model show a significant positive correlation between stellar mass and disturbance fraction, while the CAS method exhibits a much weaker trend.

What carries the argument

Self-supervised learning model with retrained linear classifier on small labelled dataset, benchmarked against visual classification and compared to CAS parameters with A > 0.35 threshold.

Load-bearing premise

Visual classification provides an accurate reference standard for the true disturbance fraction despite being limited by galaxy distance and image resolution in detecting faint low surface brightness structures.

What would settle it

Deeper higher-resolution imaging of the same sample revealing that the SSL model misses a substantial fraction of newly visible disturbances or produces many unconfirmed positives.

Figures

Figures reproduced from arXiv: 2606.11313 by Alice Desmons, Aman Khalid, Elizaveta Sazonova, Haotian Lyu, Sarah Brough.

Figure 1
Figure 1. Figure 1: Example galaxies in the original and re-made mock image sample. Panels (a) and (b) show the same galaxy, and Panels (c) and (d) show another galaxy. Panel (a) and Panel (c) are from Khalid et al. (2024)’s original mock images, with a size of 2400 × 2400 pixels and placed at a distance of 𝑧 ∼ 0.025. Panel (b) and Panel (d) are the re-made mock images, with a size of 128 × 128 pixels and placed at a distance… view at source ↗
Figure 2
Figure 2. Figure 2: Example galaxies in the remade mock-image sample. The four images from left to right in each row show g-band grayscale, 5-band grayscale, 5-band inverse grayscale, and gri-colour, respectively. Row (a) shows a galaxy classified as containing a stream/tail and an asymmetric halo. Row (b) is classified as showing a shell, and row (c) is classified as showing a double nucleus and an asymmetric halo (confidenc… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of segmentation maps for the same image generated without and with image smoothing. Panel (a) shows the segmentation map produced without smoothing, while panel (b) displays the segmentation map generated after applying smoothing. Following Conselice (2003), we adopt the standard merger-selection criteria 𝐴 > 0.35 and 𝐴 > 𝑆, and therefore focus here on the asymme￾try (𝐴) and smoothness (𝑆) param… view at source ↗
Figure 4
Figure 4. Figure 4: Example of the complete mask generation process. From left to right: the original mock image, the segmentation map before deblending, the segmentation map after deblending, and the image after applying the mask to isolate the central galaxy. isolate the central galaxies for CAS parameter calculations. However, detect_sources identifies only regions above the threshold with￾out deblending overlapping source… view at source ↗
Figure 6
Figure 6. Figure 6: Example of generating a residual image for calculating the smooth￾ness parameter, 𝑆. The first image shows the masked galaxy, the sec￾ond image presents the smoothed version with a kernel size of 16 pixels (𝑟𝑝 (𝜂 = 0.2) = 44.8 pixels), and the third image shows the residual ob￾tained by subtracting the smoothed image from the masked one. This galaxy has a smoothness value of 𝑆 = 0.34 ± 0.0003. local surfac… view at source ↗
Figure 8
Figure 8. Figure 8: The ROC curve for ten re-trained linear classifiers, trained on 1440 mock images and tested on 206 galaxies. The model with the highest ROC AUC is shown in red, the lowest is shown in blue, and the other nine models are shown in grey [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: ROC AUC on the test set when reducing the training set size compared to reducing only the number of unique galaxies while maintaining the overall training set size. For each training set size, we trained 10 models and took the mean value as the final result, and the standard deviation around this was used as the uncertainty. classifier’s ability to adapt to the new dataset. To do this, we reduced the train… view at source ↗
Figure 11
Figure 11. Figure 11: presents the confusion matrix for the re-trained SSL model tested on the prediction dataset. We use true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) in the confusion matrix to compute key evaluation metrics, including [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: The distribution of the asymmetry and smoothness parameters for the full dataset. recall, precision, and the F1-score. The model correctly classifies 184 galaxies as disturbed, consistent with the visual classifications, resulting in a high recall. However, it also misclassifies 147 galaxies as disturbed, leading to a lower precision. 3.3 CAS parameters As described in Section 2.4, we calculate the asymme… view at source ↗
Figure 14
Figure 14. Figure 14: Confusion matrix of the CAS parameter results. True Positive and True Negative cases, representing correctly classified samples, are shown in blue, while False Negative and False Positive cases, representing misclassified samples, are shown in red. The metrics of recall, precision, and F1-score are derived from these four classification outcomes [PITH_FULL_IMAGE:figures/full_fig_p011_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Asymmetry parameter as a function of linear classifier scores from the best model. Classifier Score = 0.47 is the threshold for this best model at FPR = 0.2. 𝐴 > 0.35 is the typical value used to classify galaxy mergers in the CAS method, while 𝐴 > 0.14 is the asymmetry threshold driven from the ROC Curve. The labels are based on the visual disturbance classifications. a substantially broader disturbed po… view at source ↗
Figure 17
Figure 17. Figure 17: The results obtained from visual classification (top panel), our re-trained SSL model (middle panel) and the Asymmetry parameter (bottom panel) as a function of stellar mass. Stellar mass is binned in intervals of 0.375 dex in log10 (𝑀/𝑀⊙ ); each point represents the mean value within a bin. Horizontal error bars denote the bin width (0.375 dex), while vertical error bars represent the standard deviation … view at source ↗
Figure 18
Figure 18. Figure 18: ROC curve for the asymmetry parameter on the prediction set of 960 galaxies with ROC AUC = 0.758. The red point minimizes the distance to the ideal classifier (FPR = 0, TPR = 1), corresponding to an ROC-derived optimal threshold of 𝐴 = 0.12. The vertical dashed line indicates the point where FPR = 0.2, which corresponds to a threshold of 𝐴 = 0.14 [PITH_FULL_IMAGE:figures/full_fig_p015_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: ROC curves for the re-trained SSL model (red) and the CAS parameter (blue) evaluated on the prediction set of 960 galaxies. The area under the curve (AUC) is 0.895 for the SSL model and 0.758 for the CAS method. The green dashed line marks the point where the false positive rate (FPR) equals 0.2, which is used as the classification threshold for comparison. of 0.75, precision of 0.48, recall of 0.59, and … view at source ↗
read the original abstract

Low surface brightness tidal debris around galaxies, such as tails, streams, and shells, together with other interaction-driven morphological disturbances, serve as valuable indicators of past or ongoing galaxy mergers. With the growing data volume from surveys like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), automated detection methods are essential. This paper evaluates the performance of two automated methods, a Self-Supervised Learning (SSL) model and the Concentration-Asymmetry-Smoothness (CAS) parameter method, in tracing interaction-driven disturbances and merger signatures, with visual classification used as the benchmark. Visual classification yields a high-confidence disturbance fraction of 25.1 +/- 1.5% in our sample and serves as the reference standard for assessing the completeness and precision of the automated approaches. Visual classification is affected by galaxy distance and image resolution, which limit the detectability of faint low surface brightness structures. The SSL model achieves high recall (0.86 +/- 0.04) and low contamination (0.2) by retraining only its linear classifier on a small labelled dataset, making it suitable for identifying a broad set of disturbed systems, including faint tidal debris and other interaction-driven morphological disturbances, thereby providing a more complete census of merger-related features. The CAS method, using the traditional threshold A > 0.35, shows higher precision (0.77) but lower recall (0.20), indicating a conservative approach that captures cleaner but less complete samples. Visual classification and the SSL model show a significant positive correlation between stellar mass and disturbance fraction, while the CAS method exhibits a much weaker trend.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper evaluates two automated methods—a Self-Supervised Learning (SSL) model and the Concentration-Asymmetry-Smoothness (CAS) parameters—for detecting interaction-driven morphological disturbances and merger signatures in galaxies. Visual classification serves as the benchmark, yielding a disturbance fraction of 25.1 ± 1.5%. The SSL model, after retraining only its linear classifier on a small labeled set, achieves recall 0.86 ± 0.04 and contamination 0.2, while CAS with A > 0.35 yields precision 0.77 but recall 0.20. The authors conclude that SSL is suitable for identifying a broad set of disturbed systems including faint tidal debris, providing a more complete census than CAS, and report a positive stellar-mass correlation for both visual and SSL results but a weaker trend for CAS.

Significance. If validated, the work offers a practical, data-efficient approach (SSL with minimal retraining) for scaling disturbance detection to LSST volumes, addressing the need for automated merger tracers. The reporting of concrete metrics with uncertainties is a positive feature of the empirical comparison.

major comments (1)
  1. [Abstract] Abstract: The claim that the SSL model is suitable for identifying a broad set of disturbed systems including faint tidal debris (and thereby provides a more complete census) is not supported by the reported metrics. The abstract states that visual classification (the reference standard) is affected by galaxy distance and image resolution, limiting detectability of faint low surface brightness structures. Because the reference labels systematically under-count the very class of features asserted to be better recovered by SSL, the recall of 0.86 ± 0.04 measures agreement with an incomplete benchmark rather than true completeness; the positive stellar-mass correlation and superiority claim over CAS inherit the same incompleteness. No independent verification (e.g., simulation labels or deeper imaging) is indicated to break this dependency.
minor comments (1)
  1. The manuscript would benefit from explicit definitions of 'contamination' versus precision and from a dedicated discussion of how sample selection and image quality cuts affect the visual labels.

Simulated Author's Rebuttal

1 responses · 1 unresolved

We thank the referee for their constructive feedback on our manuscript. We address the single major comment below, providing an honest assessment of our claims relative to the visual benchmark.

read point-by-point responses

  1. Referee: The claim that the SSL model is suitable for identifying a broad set of disturbed systems including faint tidal debris (and thereby provides a more complete census) is not supported by the reported metrics. The abstract states that visual classification (the reference standard) is affected by galaxy distance and image resolution, limiting detectability of faint low surface brightness structures. Because the reference labels systematically under-count the very class of features asserted to be better recovered by SSL, the recall of 0.86 ± 0.04 measures agreement with an incomplete benchmark rather than true completeness; the positive stellar-mass correlation and superiority claim over CAS inherit the same incompleteness. No independent verification (e.g., simulation labels or deeper imaging) is indicated to break this dependency.

    Authors: We agree that visual classification is an imperfect benchmark, as explicitly stated in the manuscript, due to its sensitivity to distance and resolution for faint low-surface-brightness features. Our reported metrics (recall 0.86 for SSL vs. 0.20 for CAS) are therefore measures of agreement with this benchmark rather than absolute completeness. The claim of suitability for a broad set of disturbed systems, including faint tidal debris, is made comparatively: SSL recovers a substantially larger fraction of the visually identified disturbed galaxies than CAS does, while both methods are evaluated on identical labels. This supports a more complete census relative to CAS within the benchmark's limitations. We do not claim the SSL detects features entirely missed by visual inspection, as no such independent verification (simulations or deeper imaging) is presented. We will revise the abstract to qualify the wording on faint tidal debris and the completeness claim to avoid overstatement. revision: partial

standing simulated objections not resolved
  • Absence of independent verification (e.g., simulation labels or deeper imaging) to confirm whether the SSL model recovers interaction features missed by visual classification.

Circularity Check

0 steps flagged

No circularity: purely empirical comparison against external visual benchmark

full rationale

The paper reports recall, contamination, and precision of the SSL and CAS methods computed directly against visual classification labels used as an independent reference standard. No equations, derivations, or predictions are presented that reduce by construction to fitted inputs or self-citations. The SSL retraining step uses a small labelled dataset to adjust only the linear classifier, but the resulting metrics are external evaluations, not self-referential. Visual classification limitations are explicitly acknowledged but do not create a definitional loop or fitted-input prediction. This is a standard empirical benchmark comparison with no load-bearing self-citation chains or ansatz smuggling.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Only abstract available; ledger populated from stated assumptions in the provided text.

axioms (1)
  • domain assumption Visual classification is treated as the ground-truth reference standard for disturbance fraction.
    Explicitly used to benchmark both automated methods and to report the 25.1% fraction.

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