REVIEW 4 major objections 5 minor 7 references
Classification of HI Galaxy Profiles Using Unsupervised Learning and Convolutional Neural Networks: A Comparative Analysis and Methodological Cases of Studies
T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash
Pith's one-line read Converting 1D HI spectral profiles into 2D images improves classification accuracy by 13% compared with traditional 1D methods.
desk verdict A useful but loosely reported ML pipeline for HI profiles; its headline 13% improvement claim is not backed by a reported 1D baseline. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing mechanism is a set of three 2D transforms of each 1D profile: Model 1 rotates the original spectrum into an image; Model 2 splits the profile at its centre, reflects one side, subtracts it from the other, and rotates the difference; Model 3 is a normalized version of Model 2 with pixel intensities scaled. Feeding these images to a CNN is what produces the 13% improvement. The rest of the pipeline, including Busy-function fitting, iterative polynomial, Gaussian, and double-Lorentzian models, and clustering plus KNN, SVM, and Random Forest bootstrapping, supports the comparison but does not by itself carry the asymmetry gain.
What would settle it
Re-run the released pipeline on the same 318 profiles with the analytical labels replaced by blind, independent expert classifications or by labels derived from resolved HI kinematics; if the 2D CNN no longer beats the 1D CNN by roughly 13%, the claimed advantage is an artifact of the reference labels rather than a property of the 2D representation.
Extended reading notes
Core claim
The paper's central claim is that representing each one-dimensional HI spectrum as a two-dimensional image exposes asymmetries that a CNN can exploit, and that this representation raises classification accuracy by 13% over the same task done on 1D profiles. On the 318-profile sample the best 2D pipeline reaches 63% agreement with the analytical classification used as reference. The claim is therefore not about physical truth of asymmetry in an absolute sense; it is that the 2D transformation makes machine classification track that reference scheme better than 1D input does.
Load-bearing premise
The evaluation assumes the earlier analytical classification of the 318 profiles is the correct answer; if those labels are biased or contain errors, the 63% accuracy and the 13% advantage of 2D over 1D are only measures of agreement with that scheme.
Editorial extensions
If this is right
- The 2D image representation can be applied to any future HI survey without waiting for new instruments, since it only transforms existing spectra.
- The reported 63% accuracy sets a concrete baseline for classifying isolated-galaxy HI profiles against the analytical reference scheme.
- The framework, including code and models, is openly documented so the 13% gain can be reproduced and stress-tested by other groups.
- The same workflow is positioned to scale to millions of profiles expected from next-generation radio surveys, where manual or analytical classification will be impractical.
Reading between the lines
- The 13% gain is measured against one analytical labelling scheme; whether the 2D models capture physically real asymmetries could be tested by comparing their output with metrics from resolved HI velocity fields or simulated galaxies.
- Because the transforms are generic, rotation and mirrored subtraction, the same recipe could be tried on other symmetric one-dimensional spectra, such as optical emission lines or molecular line profiles.
- The per-iteration cost of about 1.13 hours on modest hardware suggests that scaling to surveys with millions of profiles will need lighter CNN architectures or pre-selection, a practical step the paper leaves open.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper presents a machine-learning framework for classifying integrated HI galaxy spectral profiles, applied to 318 CIG profiles and 30,780 ALFALFA profiles. The methodology combines unsupervised clustering (K-means, spectral clustering, DBSCAN, agglomerative) for feature extraction with supervised classifiers (KNN, SVM, Random Forest) and CNN optimization. The paper's central methodological novelty is the transformation of 1D spectral profiles into three 2D representations (rotated spectrum, subtracted asymmetry, normalized asymmetry) to improve asymmetry classification. The authors report a 63% classification success rate with the 2D images and claim a 13% accuracy improvement over traditional 1D methods. All code and models are publicly available in a GitHub repository following FAIR principles.
Significance. If substantiated, the claimed 13% improvement over 1D classification would be a useful practical contribution for classifying large numbers of HI profiles from upcoming surveys such as SKA. The paper's strengths are its open-source repository, reproducible code, and the attempt to compare multiple unsupervised and supervised ML methods on a well-defined astronomical dataset. However, the headline quantitative claim is currently unverifiable because the paper does not report the 1D baseline accuracy or the experimental protocol behind the comparison. The evaluation also relies entirely on an analytical classification from a prior study by the same group, whose reliability is not discussed. The significance of the contribution therefore depends on strengthening the empirical reporting.
major comments (4)
- [Section 4] The claim 'improving classification accuracy by 13% compared to traditional 1D methods' is not supported by the manuscript because no 1D baseline accuracy is reported anywhere. Section 3 reports a 63% success rate for the 2D models, but without the 1D accuracy (and a clear definition of what 'traditional 1D methods' means, including classifier, features, train/test split, and architecture), the 13% improvement cannot be computed or checked. This is a load-bearing number for the paper's main conclusion and must be provided, along with the exact comparison protocol.
- [Section 2.3 and Section 3] The evaluation uses the analytical profile classification of Espada et al. (2011) as the target labels, and this prior work has overlapping authorship with the AMIGA group. The paper does not discuss the authority or limitations of this analytical scheme, nor does it report any measure of inter-rater agreement or independent validation. The 63% accuracy should be explicitly framed as agreement with the Espada et al. classification rather than an absolute ground truth; otherwise, the accuracy claim is vulnerable to any biases in that reference scheme. Please provide justification for using this benchmark or add independent validation.
- [Section 3] The manuscript provides no details of the CNN architecture, hyperparameters, training/validation split, number of runs, or error bars. The sentence 'the results suggest that the methodology is robust and scalable' is not backed by any statistical evidence, such as standard deviations over random seeds or the 54 configurations mentioned. Report the mean and standard deviation of the classification accuracy, and specify the configuration search space, to allow the robustness claim to be assessed.
- [Section 2.2] The description of the ALFALFA classification pipeline is too incomplete for reproducibility. For example, 'the temporal shapelet transform' is mentioned but not defined or referenced, and the number of clusters, feature extraction steps, and how the 18 classifications were generated are not specified. The GitHub repository is helpful, but the paper itself should contain the essential methodological parameters so that the results can be reproduced without reverse-engineering the code.
minor comments (5)
- [Abstract] There is a typographical error: 'in a efficient way' should be 'in an efficient way'.
- [Section 1] The text reads 'improve the e fficiency' with a stray space; it should be 'improve the efficiency'.
- [Section 2.3] Figure 3 is referenced in the text only by a caption; please add a sentence in the main text describing what the three panels show and how they relate to the models.
- [Section 3] The number '30.780' uses a period as a thousands separator; for international consistency, use '30,780' or '30780'.
- [Section 3] The definition of W50 (width at 50 percent of peak) is standard, but it would help to state explicitly that it refers to HI line width at half the peak flux.
Circularity Check
No significant circularity: the 2D-CNN classification is evaluated against an external analytical benchmark, and no claimed result reduces to a fitted parameter or self-citation.
full rationale
The paper's central claim is that transforming 1D HI profiles into 2D images and classifying them with CNNs improves classification accuracy relative to traditional 1D methods. No step in the manuscript defines a quantity in terms of the result it is supposed to predict, and no fitted parameter is renamed as a prediction. The ground-truth labels come from Espada et al. (2011), an earlier analytical classification by the AMIGA group, and the current paper measures the CNN's agreement with those labels. This is a standard supervised-learning benchmark: the model is trained and evaluated on the same external label set, and the reported 63% success rate is a test-set agreement rate, not a derivation of those labels. The authors' overlap with the earlier analytical study is a legitimate concern about benchmark authority, but it is not internal circularity because the Espada et al. labels are not produced by the model or by equations in this paper. The 2D representations (rotation, subtraction, normalization) are feature-engineering choices that encode asymmetry explicitly, which may make the classification task easier, but this does not make the accuracy claim tautological. The one notable weakness is that the 13% improvement over 1D methods is asserted without presenting the 1D baseline accuracy, error bars, or the precise meaning of 'traditional 1D methods'; this is a serious reproducibility and correctness gap, not a circular-reasoning gap. There is no self-citation invoked as a uniqueness theorem, no ansatz smuggled in via citation, and no renaming of a known result as a new organization. Therefore, the derivation chain is self-contained with respect to circularity, and the appropriate score is 0.
Assumptions & free parameters
free parameters (4)
- Basis function fit coefficients
- Shapelet transform parameters
- CNN hyperparameters
- Clustering hyperparameters
assumptions (4)
- domain assumption Espada et al. (2011) analytical labels are correct
- domain assumption HI profile shape reflects galaxy physical state
- domain assumption Busyfit and iterative fits represent the true profile
- ad hoc to paper The 2D transformations preserve or enhance relevant information
invented entities (3)
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Model 1 (rotated spectrum image)
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Model 2 (subtracted asymmetry image)
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Model 3 (normalized asymmetry image)
Cite this review
Pith. "Pith review of Classification of HI Galaxy Profiles Using Unsupervised Learning and Convolutional Neural Networks: A Comparative Analysis and Methodological Cases of Studies." pith.science (2026). https://pith.science/paper/CVJZN5O2
@misc{pith2026250111657,
author = {Pith},
title = {Pith review of: Classification of HI Galaxy Profiles Using Unsupervised Learning and Convolutional Neural Networks: A Comparative Analysis and Methodological Cases of Studies},
year = {2026},
howpublished = {\url{https://pith.science/paper/CVJZN5O2}},
note = {Machine review of arXiv:2501.11657}
}
read the original abstract
Hydrogen, the most abundant element in the universe, is crucial for understanding galaxy formation and evolution. The 21 cm neutral atomic hydrogen - HI spectral line maps the gas kinematics within galaxies, providing key insights into interactions, galactic structure, and star formation processes. With new radio instruments, the volume and complexity of data is increasing. To analyze and classify integrated HI spectral profiles in a efficient way, this work presents a framework that integrates Machine Learning techniques, combining unsupervised methods and CNNs. To this end, we apply our framework to a selected subsample of 318 spectral HI profiles of the CIG and 30.780 profiles from the Arecibo Legacy Fast ALFA Survey catalogue. Data pre-processing involved the Busyfit package and iterative fitting with polynomial, Gaussian, and double-Lorentzian models. Clustering methods, including K-means, spectral clustering, DBSCAN, and agglomerative clustering, were used for feature extraction and to bootstrap classification we applied K-NN, SVM, and Random Forest classifiers, optimizing accuracy with CNN. Additionally, we introduced a 2D model of the profiles to enhance classification by adding dimensionality to the data. Three 2D models were generated based on transformations and normalised versions to quantify the level of asymmetry. These methods were tested in a previous analytical classification study conducted by the Analysis of the Interstellar Medium in Isolated Galaxies group. This approach enhances classification accuracy and aims to establish a methodology that could be applied to data analysis in future surveys conducted with the Square Kilometre Array (SKA), currently under construction. All materials, code, and models have been made publicly available in an open-access repository, adhering to FAIR principles.
Figures
Reference graph
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Reviewed August 10, 2026 · model on record in the stance chip above.
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