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Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods

T0 review · 2 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims a two-stage Swin Transformer pipeline finds 8,052 new ring galaxies in DESI Legacy Imaging Surveys with 64.87 percent precision.

desk verdict A useful new ring-galaxy catalog with measured precision, but the central labels rest on an undocumented single-observer visual inspection and a subjective training cut. read the letter →

arxiv 2506.16090 v1 pith:VU4SVXFF submitted 2025-06-19 astro-ph.GA

Aina Zhang , Xiaoming Kong , Bowen Liu , Nan Li , Yude Bu , Zhenping Yi , Meng Liu This is my paper · ORCID
classification astro-ph.GA
keywords ringgalaxiesSwinTransformerDESILegacyImagingSurveysgalaxymorphologyclassificationmachinelearningcatalogdataaugmentationinteractions
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 claims that a two-stage binary classifier built on the Swin Transformer can find ring galaxies in the DESI Legacy Imaging Surveys more reliably than earlier single-pass machine-learning searches. Applied to 573,668 galaxy images with spectroscopic redshifts 0.01–0.20 and r-band magnitude below 17.5, the pipeline produced candidates that, after visual inspection, reached an overall precision of 64.87 percent. The result is a catalog of 8,052 newly discovered ring galaxies with positions, redshifts, and fluxes. If the claim holds, the catalog substantially expands the known ring-galaxy sample available for studying dark matter, galaxy interactions, and galaxy evolution.

What carries the argument

The load-bearing mechanism is the two-stage classification design built on the Swin Transformer, a vision model whose shifted-window self-attention captures both local and long-range image structure. Stage one (Swin T1) is a binary classifier trained on 4,113 verified ring-galaxy images versus 35,000 non-ring images, with data augmentation; stage two (Swin T2) is a second binary classifier trained to distinguish rings from spirals and barred spirals, which are the main contaminants. The second stage is what converts a high-recall first pass into a usable candidate list: it raises application precision from about 9 percent on the extra candidates to roughly 65 percent on the retained set. The paper also compares the Swin Transformer against ResNet18 and VGG16 on the same data and selects the Swin architecture for its higher F1 score.

What would settle it

Visually classify a random sample of DR9 galaxies with spectroscopic redshift between 0.01 and 0.20 and r-band magnitude below 17.5 that were not used in training, including faint and ambiguous ring cases, and compare with the model's predictions; a recall much lower on faint rings than on prominent rings would show the catalog misses a substantial population.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a two-stage Swin Transformer classifier can identify ring galaxies in a wide-area imaging survey at a precision competitive with or better than previous machine-learning efforts, without relying on simulated training data. The first stage separates ring galaxies from all other galaxies; the second stage removes spiral and barred spiral galaxies that dominate the false positives. Combining three second-stage models and removing duplicates yielded 18,802 unique candidates, of which 12,196 were visually confirmed as true rings, an overall precision of 64.87 percent. After removing 4,144 objects overlapping with earlier catalogs, 8,052 objects remain as new discoveries. The paper also reports that ring galaxies show smaller color changes with redshift than non-ring galaxies, consistent with a more homogeneous population.

Load-bearing premise

The load-bearing premise is that the 4,113 visually verified ring-galaxy images used for training represent all ring galaxies in the survey; if faint or ambiguous rings are common, the model will systematically miss them and the 8,052-object catalog will be incomplete and biased.

Editorial extensions

If this is right

  • The published catalog gives astronomers 8,052 new ring galaxies with positions, spectroscopic redshifts, and g/r/z fluxes, a substantial expansion of the known sample.
  • The two-stage scheme shows a practical way to hunt rare morphologies in large imaging surveys when positive examples are scarce, since the second stage is explicitly built to remove the dominant false-positive class.
  • The reported 64.87 percent precision implies that roughly 6,606 of the 18,802 unique candidates are still non-rings, so statistical studies using the full candidate union should account for contamination.
  • At 64.87 percent, the visual-inspection precision exceeds the 58.9 percent reported for a prior machine-learning ring search that relied on simulated training data, and the method avoids simulated data altogether.

Reading between the lines

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

  • Because the positive training set dropped 4,774 images whose rings were not prominent or were hard to identify, the catalog is likely biased toward prominent rings; faint rings in DR9 are probably underrepresented even if the reported precision is accurate.
  • The redshift and color distributions presented in the paper therefore describe the detectable prominent-ring population rather than the intrinsic ring-galaxy population, since the training selection and the survey magnitude cut shape them.
  • The same two-stage architecture could transfer to other rare morphological classes, such as polar-ring galaxies or tidal dwarf candidates, by keeping the first stage broad and retraining the second stage on the dominant contaminant class.
  • A testable extension is to run the trained models on a deeper or bluer survey and measure whether the precision remains stable outside the spectroscopic redshift and magnitude cuts used here.
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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 presents a two-stage machine-learning pipeline, based on a Swin Transformer, to identify ring galaxies in DESI Legacy Imaging Surveys DR9. Stage 1 separates ring galaxies from all other galaxies; Stage 2 removes spiral and barred-spiral contaminants. The authors train on 4,113 visually vetted ring galaxy images and apply the model to 573,668 galaxies with z_spec=0.01-0.20 and mag_r<17.5. Stage 1 yields 49,264 candidates; Stage 2, using three balanced classifiers, yields 18,802 unique candidates. Visual inspection of all 18,802 candidates confirms 12,196 true ring galaxies (overall precision 64.87%), and after removing 4,144 overlaps with training samples and prior catalogs, the paper reports 8,052 newly discovered ring galaxies. A machine-readable catalog is provided at DOI 10.5281/zenodo.15545272.

Significance. If the catalog is reliable, it is a useful addition to the relatively small set of confirmed ring galaxies and extends the search to the DESI Legacy Surveys footprint. The paper has several strengths: it visually inspects every final candidate rather than a sample, it compares three architectures and several class-imbalance configurations, and it makes the resulting catalog publicly available. The two-stage design is sensible for reducing contamination from spiral and barred-spiral galaxies. However, the central quantitative claims—the 64.87% precision and the 8,052-object count—depend entirely on a visual inspection procedure that is described only as 'systematic visual inspection', with no stated criteria, no number of inspectors, and no inter-rater agreement measure. For a catalog paper, this is a load-bearing reproducibility gap that must be addressed before the central claims can be fully accepted.

major comments (2)
  1. [Section 5.1, Table 3] The reported precision of 64.87% (12,196/18,802) and the final catalog membership of 8,052 objects rest entirely on visual inspection of 18,802 candidate images, yet the manuscript provides no inspection protocol: it does not state how many inspectors were involved, whether they were blinded to the model prediction, what explicit criteria defined a 'true ring galaxy', or how disagreements were resolved. Because the training positives were also selected by subjective visual judgement (Section 2.2), the ground-truth labels for both training and final validation are single-observer judgements with no demonstrated reproducibility. Please provide a detailed protocol and, ideally, an independent re-inspection of a random subset with an inter-rater agreement statistic (e.g., Cohen's kappa), so that both the precision and the final count are anchored by reproducible measurements.
  2. [Section 2.2] The positive training set was constructed by visually removing 4,774 of 8,887 images whose rings were 'not prominent or difficult to clearly identify'. No quantitative or operational criteria are given for this removal. This biases the classifier toward prominent, cleanly resolved rings and means that the resulting catalog is likely incomplete for faint, edge-on, or poorly resolved ring galaxies. Consequently, the redshift and color distributions in Figures 7 and 8 inherit this selection bias, and the paper should explicitly state that the 8,052 objects are a subset selected by these criteria rather than a complete census. At minimum, the criteria for excluding training images should be specified and the expected impact on completeness discussed.
minor comments (5)
  1. [Section 2.2, Table 1] The cross-matched counts in the text (2,598 Nair & Abraham; 185 Timmis & Shamir; 443 Shamir; 1,151 Krishnakumar & Kalmbach; 3,657 Galaxy Zoo 2; 853 GALAXY CRUISE) differ from the 'used ring galaxies' counts in Table 1 (1,087; 71; 252; 774; 1,726; 203). I assume Table 1 lists post-filter counts, but this should be stated explicitly, and the treatment of galaxies appearing in multiple input catalogs should be clarified.
  2. [Figures 4 and 5] The captions state that 'AUC values are identical across all three models', while the text in Section 4.2.1 says the AUC values 'differ by only 0.001'. Please make the reported values consistent and give the actual AUC numbers.
  3. [Section 2.3] The list of augmentation operations implies ten or more transformed versions per image, but the text and Figure 2 mention '8 samples'. Please clarify how many augmented images are generated per original image and which operations are randomly applied versus always applied.
  4. [Section 4.2.2] The construction of the Stage 2 negative samples is not fully specified: the text says spiral and barred-spiral galaxies were balanced using 'a random sampling method', but it does not state how these morphological types were identified, from which catalog, or what the resulting class balance was after sampling. Please provide this information for reproducibility.
  5. [Section 5.1] When reporting the 9% precision on the 1,000 randomly selected images from the difference set between Swin T1 8-8 and Swin T1 3-8, the sample size and the implied binomial uncertainty should be given, since 9% is based on 1,000 samples and the uncertainty is nontrivial.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the catalog and precision are produced by an independently trained model plus external visual verification, with no fitted target or self-citation chain.

full rationale

The derivation chain is self-contained against the paper's claimed inputs. The positive training set (4113 images) is assembled from six independent external catalogs (Nair & Abraham 2010; Hart et al. 2016; Timmis & Shamir 2017; Shamir 2020; Krishnakumar & Bryce Kalmbach 2022; Tanaka et al. 2023) after cross-matching to DR9, and the negative set is drawn from Galaxy Zoo 2 non-ring classifications (Section 2.2). The two-stage Swin models are trained on these labels and then applied to 573,668 previously unseen DR9 galaxy images (Section 5.1); no parameter is fitted to the final '8052 new ring galaxies' count. The claimed precision 64.87% = 12,196/18,802 is a directly measured quantity obtained by systematic visual inspection of the model's candidate set, and the final catalog is defined by removing overlaps with the training samples and existing catalogs (Hart et al. 2016; Shimakawa et al. 2024; Krishnakumar & Kalmbach 2024; Abraham et al. 2024). The paper does not rely on a self-citation chain or uniqueness theorem; the external catalogs are independent prior results, not outputs of this paper. The absence of a documented inter-rater agreement protocol for the visual inspection is a reproducibility and validation limitation, but it does not make the derivation circular: the human labels are not obtained by solving the model's equations and are not equivalent to the model's outputs by construction. Therefore no circular step can be exhibited.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The catalog claim rests on machine learning training labels and a visual-inspection ground truth. No physical parameters are fitted, but several subjective experimental choices enter: the positive-training sample is defined by eyeballing an already heterogeneous set of catalog labels; the negative class is defined by Galaxy Zoo 2 votes; and the final precision is defined by the authors' own visual review. These choices are domain assumptions rather than fitted constants.

free parameters (4)
  • stage-1 training augmentation factors = 3x and 8x
    The positive samples were augmented to threefold and eightfold the original size to test imbalance effects; the choice is experimental and not derived.
  • stage-2 positive-to-negative ratios = 1:2, 1:3, 1:5
    The number of negative spiral and barred spiral samples was set to 2, 3, or 5 times the positive count to study class balance.
  • classification threshold = 0.5
    All test-set metrics are reported at a default probability threshold of 0.5 with no threshold-optimization analysis.
  • visually excluded positive images = 4774
    Almost half of the initial 8,887 positive images were removed by subjective visual criteria, a hand-selected data division that shapes the training distribution.
assumptions (5)
  • domain assumption A ring galaxy can be reliably recognized from a single 256x256 three-band Legacy Surveys cutout.
    The entire pipeline treats cutout morphology as sufficient for ring classification (Sections 2.2 and 3).
  • domain assumption Galaxy Zoo 2 galaxies with ring count<1 are a reliable non-ring population.
    The negative training set is defined by this criterion (Section 2.2).
  • domain assumption The different positive-label sources correspond to the same morphological class of ring galaxies.
    Positive samples are pooled from Nair & Abraham, Hart et al., Timmis & Shamir, Shamir, Krishnakumar & Kalmbach, and Tanaka et al. with different selection methods (Section 2.2 and Table 1).
  • domain assumption The authors' visual classification is an acceptable ground truth for the final catalog.
    The reported final precision and the 8,052-object catalog are based on manual inspection of 18,802 candidates (Section 5.1).
  • domain assumption The parent sample defined by z_spec=0.01-0.20 and mag_r<17.5 is a meaningful target population.
    The search and all statistics are restricted to this DR9 subset (Section 5.1).

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Pith. "Pith review of Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods." pith.science (2026). https://pith.science/paper/VU4SVXFF

@misc{pith2026250616090,
  author       = {Pith},
  title        = {Pith review of: Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VU4SVXFF}},
  note         = {Machine review of arXiv:2506.16090}
}
read the original abstract

The formation and evolution of ring structures in galaxies are crucial for understanding the nature and distribution of dark matter, galactic interactions, and the internal secular evolution of galaxies. However, the limited number of existing ring galaxy catalogs has constrained deeper exploration in this field. To address this gap, we introduce a two-stage binary classification model based on the Swin Transformer architecture to identify ring galaxies from the DESI Legacy Imaging Surveys. This model first selects potential candidates and then refines them in a second stage to improve classification accuracy. During model training, we investigated the impact of imbalanced datasets on the performance of the two-stage model. We experimented with various model combinations applied to the datasets of the DESI Legacy Imaging Surveys DR9, processing a total of 573,668 images with redshifts ranging from z_spec = 0.01-0.20 and magr <17.5. After applying the two-stage filtering and conducting visual inspections, the overall Precision of the models exceeded 64.87%, successfully identifying a total of 8052 newly discovered ring galaxies. With our catalog, the forthcoming spectroscopic data from DESI will facilitate a more comprehensive investigation into the formation and evolution of ring galaxies.

Figures

Figures reproduced from arXiv: 2506.16090 by the authors.

Figure 1
Figure 1. Examples of samples screened based on visual inspection. filtered out. After constructing the catalog of non-ring galaxies, we extracted image data for these galaxies from DR9 with a uniform size of 256 × 256 pixels. 2.3. Data Augmentation Data augmentation aims to create a more diverse and enriched sample set by transforming and expanding the existing data, thereby improving the training effectiveness of machine le… view at source ↗
Figure 2
Figure 2. The original image and the results of 8 samples after applying data augmentation used in this work. 3. METHODS 3.1. Swin Transformer The Swin Transformer (Liu et al. 2021), proposed by Microsoft Research Asia, is a deep learning model based on the Transformer architecture, specifically designed for image processing tasks. It has demonstrated outstanding performance in the field of computer vision, particularly in im… view at source ↗
Figure 3
Figure 3. (a) Swin Transformer architecture; (b) Swin Transformer Blocks. This shift improves the model’s ability to capture long-range dependencies, and helps to deal with the effective transfer of global information. 4. EXPERIMENT 4.1. Performance Metric The objective of our task is to accurately classify ring galaxies and non-ring galaxies. In evaluating the performance of the classification model, we employed three metric… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: PR curve for each model. The AUC values are identical across all three models, and the black, purple, and red points in the graph indicate the maximum F1 score achieved by Swin T11-8, Swin T13-8, and Swin T18-8, which are 96.57%, 96.78%, and 97.35%, respectively [PITH…
Figure 5
Figure 5. Figure 5: PR curve for each model. The AUC values are identical across all three models, and the black, purple, and red points in the graph indicate the maximum F1 score achieved by ResNet18, VGG16, and Swin Transformer respectively. When trained with a positive-to-negative samp…
Figure 6
Figure 6. Figure 6: 36 galaxies from the catalog of 8052 rings. & Shamir (2017), Shamir (2020), Tanaka et al. (2023), Shimakawa et al. (2024), Krishnakumar & Kalmbach (2024), and Abraham et al. (2024). In [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: The number and frequency of newly discovered ring galaxies in this study, as well as those from other known catalogs in DR9 that meet the criteria of zspec = 0.01–0.20 and magr < 17.5. The light blue bars represent the frequency of newly discovered ring galaxies in thi…
Figure 8
Figure 8. Figure 8: The distribution of color differences between the newly discovered ring galaxies and other galaxies in DR9 that meet the criteria of zspec = 0.01–0.20 and magr < 17.5. The blue line represents the ring galaxies identified in this study, while the orange line denotes th…

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