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REVIEW 4 major objections 4 minor 35 references

Application of Deep Learning to the Classification of Stokes Profiles: From the Quiet Sun to Sunspots

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A supervised network classifies solar Stokes V profiles with accuracy close to or above 90%, enabling the first quiet-Sun DKIST/ViSP analysis and exposing a sunspot-simulation contradiction over which line detects reverse-polarity fields.

desk verdict First supervised Stokes-V classifier plus first DKIST/ViSP quiet-Sun statistics; useful and honest, but cluster-core training pools leave generalization claims unverified. read the letter →

arxiv 2505.14275 v1 pith:NPCGRKFO submitted 2025-05-20 astro-ph.SR

Ryan James Campbell , Mihalis Mathioudakis , Carlos Quintero Noda , Peter Keys , David Orozco Suárez This is my paper · ORCID
classification astro-ph.SR
keywords StokesVprofilesspectropolarimetrymulti-layerperceptronk-meansclusteringquietSunsunspotpenumbrareversepolaritymagneticfieldssupervisedmachinelearning
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 sets out to establish that supervised machine learning—a multi-layer perceptron trained on manually labelled Stokes V profiles—can classify the shapes of solar circular-polarisation signals into morphological classes with validation and test metrics close to or above 90%, and that this is reliable enough to support statistical comparisons between telescopes and against simulations. It applies the classifier to quiet-Sun data from DKIST/ViSP, Hinode/SP, and GREGOR/GRIS-IFU, as well as synthetic observations from MANCHA granulation and MURaM sunspot simulations, and in doing so presents the first statistical analysis of quiet-Sun DKIST/ViSP data based on inversions and profile classification. The methodological core is the demonstration that k-means clustering, when used to label profiles by their centroid shape, introduces systematic errors—most notably too many 'symmetric' labels—that would compromise cross-dataset statistics, whereas the supervised approach keeps errors bounded and class-balanced. The notable results are that DKIST and Hinode quiet-Sun morphologies agree despite modelling that says spatial resolution should separate them; that the 1564.85 nm line produces more symmetric and far fewer single-lobed profiles, consistent with its narrower response functions; and that in the MURaM sunspot simulation the 1564.85 nm line detects more reverse-polarity penumbral fields than the 630.25 nm line, the opposite of observations.

What carries the argument

The load-bearing object is the multi-layer perceptron classifier, a three-layer fully connected network with Swish activation, class-weighted cross-entropy loss, dropout, early stopping, and Xavier initialization, which maps each Stokes V profile to one of four quiet-Sun classes (asymmetric, symmetric, Q-like, single-lobed) or five sunspot classes (positive, negative, double positive, mixed polarity). Its training labels come from a two-stage pipeline: k-means++ clustering with 35 clusters selects the 250 profiles nearest each centroid, and a human labels those thousands of profiles using amplitude thresholds of 0.9 and 0.25 on subordinate lobes; the MLP then replaces the k-means centroids as the classifier. Two comparison devices carry the argument: Sankey diagrams that trace how profiles move between classes when the same MANCHA atmosphere is synthesised in 630.25 nm versus 1564.85 nm, and between k-means centroid labels and MLP labels; and the SIR inversion code, which supplies both the synthetic profiles and the physical parameters (field strength, inclination, velocity) that the paper uses to show the classes carry physical meaning.

What would settle it

Apply the paper's own labelling thresholds (subordinate lobe at least 0.9 of the main lobe for symmetric, at most 0.25 for single-lobed) automatically to every profile in each dataset, not just to cluster centroids, and compare the resulting class fractions with the MLP's output; if the disagreement approaches the size of the k-means-versus-MLP disagreement the paper documents, the claimed robustness of the supervised statistics is not established. A second check: retrain the classifier on pools that deliberately include the furthest profiles from each cluster and confirm that the reported cross-dataset differences (DKIST versus Hinode versus GREGOR) survive.

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Extended reading notes

Core claim

The central discovery, stated on the paper's own terms, is that supervised machine learning provides a robust, reproducible way to classify the morphology of solar circular-polarisation profiles where earlier work relied on manual inspection or on k-means clustering. A multi-layer perceptron with three fully connected layers, Swish activation, and class-weighted cross-entropy loss, trained on a few thousand manually labelled profiles drawn from the cores of k-means clusters, reaches validation and test accuracies and f1-scores typically close to or above 90% on quiet-Sun data from DKIST/ViSP, Hinode/SP, and GREGOR/GRIS-IFU, and on synthetic observations from MANCHA granulation and MURaM sunspot simulations. Deployed across these datasets, the classifier produces four principal findings: the two visible-line quiet-Sun datasets (DKIST and Hinode) classify similarly even though degraded MANCHA syntheses predict that spatial resolution should change profile shapes; the near-infrared 1564.85 nm line produces more symmetric and far fewer single-lobed profiles than the visible 630.25 nm line, in line with its narrower response functions; nearly a fifth of the simulated penumbra shows mixed-polarity profiles, of which 67–75% coincide with genuine line-of-sight polarity reversals while the 'double' profiles common in the 630.25 nm penumbra are mostly magneto-optical effects in strongly inclined fields; and the 1564.85 nm line detects more than twice as many reverse-polarity profiles as the 630.25 nm line in the penumbra, in direct contradiction to observations. The paper also shows that labelling profiles by k-means centroids alone systematically overestimates the symmetric class, an error that is not cured by increasing the number of clusters.

Load-bearing premise

The whole comparison rests on the assumption that the profiles nearest the cluster centres—the only ones used to train the classifier—represent the full population of profile shapes, even though the paper's own figures show that the most unusual profiles in each cluster look very different from the ones the classifier ever sees.

Editorial extensions

If this is right

  • The classifier is a reusable public tool: trained once and deployed to millions of pixels, it lets future DKIST observations be statistically compared with this quiet-Sun baseline without manual profile inspection.
  • The MURaM sunspot simulation fails a specific observational test: it predicts the 1564.85 nm line detects over twice as many reverse-polarity penumbral profiles as the 630.25 nm line, whereas observations show visible lines are far better at revealing three-lobed and reverse-polarity signatures; correcting this mismatch is a concrete target for simulation work.
  • Mixed-polarity profiles, about 18% of the simulated penumbra, can be read as tracers of genuine polarity reversals along the line of sight, while 'double' profiles in the 630.25 nm line trace magneto-optical effects in nearly horizontal fields; the paper shows inclination alone can create or erase the double shape.
  • For inversion work, the near-infrared line is better described by atmospheres with no gradients in optical depth, since its classification statistics and inferred parameters vary little with optical depth compared with the visible line.
  • Amplitude-asymmetry statistics become cleaner when computed only on the MLP-isolated symmetric and asymmetric classes, excluding Q-like and single-lobed profiles for which the asymmetry measure is not physically meaningful.

Reading between the lines

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

  • My inference: the DKIST/Hinode agreement is usable as a diagnostic — if the analysed DKIST scan was seeing-limited, a future good-seeing ViSP dataset run through the same classifier should drift toward the high-resolution MANCHA statistics (more single-lobed, fewer symmetric profiles), giving a morphology-based measure of DKIST's effective resolution.
  • My inference: because the cited response-function work gives the 525.02 nm line the strongest inclination sensitivity, synthesising the full MURaM snapshot in that line and classifying it with the same tool should maximise the double-profile fraction; if future high-resolution observations of that line do not show the predicted abundance, the magneto-optical interpretation would need revision.
  • My inference: the four quiet-Sun classes could be retrained on Stokes Q and U profiles as DKIST's polarimetric sensitivity grows, turning linear-polarisation morphology into a statistical map of horizontal fields — an extension the paper notes is not yet feasible at disk centre.
  • My inference: the Sankey-transfer analysis suggests a direct test of the line-difference interpretation — synthesising the same MANCHA cube at intermediate spectral lines such as 525.02 nm would show whether single-lobed and symmetric fractions vary monotonically with line-formation height, which would confirm that the response function, not the atmosphere, drives the differences.
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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

4 major / 4 minor

Summary. The paper trains a multi-layer perceptron (MLP) to classify the morphology of Stokes V profiles, using four quiet-Sun classes (symmetric, asymmetric, single-lobed, Q-like) and five sunspot classes (positive, negative, double positive, mixed polarity, and a collapsed negative/double-negative class). The classifier is applied to quiet-Sun observations from DKIST/ViSP, Hinode/SP, and GREGOR/GRIS-IFU, as well as to synthetic observations from MANCHA and MURaM simulations. The authors report validation and test metrics for every model (Table 3), present the first statistical analysis of quiet-Sun DKIST/ViSP data using inversions and a supervised classifier, compare supervised and unsupervised (k-means) classification, and examine the occurrence of reverse-polarity magnetic fields in a simulated sunspot. The central claims are that supervised ML robustly classifies solar spectropolarimetric data, that k-means centroid labeling introduces systematic errors that can compromise statistical comparisons, and that in the MURaM sunspot simulation the 1564.85 nm line detects more reverse-polarity fields in the penumbra than the 630.25 nm line, in contradiction to observations.

Significance. If the central claims hold, the trained classifier is a reusable, open-source tool for large-scale Stokes-profile classification, and the sunspot line-dependent discrepancy gives modelers a concrete target. Strengths of the paper include explicit validation and test metrics for every model, controlled re-synthesis that isolates spectral-line effects from spatial-resolution effects, a transparent k-means versus MLP comparison, and use of the MURaM simulation's ground-truth magnetic field to test the physical interpretation of the classifier output. The main weakness is that the training, validation, and test pools are constructed only from profiles closest to k-means centroids, so the reported metrics do not establish generalization to the full data population. In addition, some validation checks are logically circular because they use the same amplitude thresholds that defined the labels. The paper is honest about several of these limitations, but they are not quantified or resolved.

major comments (4)
  1. [§3.3, Fig. 1] The labelled pool for each quiet Sun dataset is built from the 250 profiles closest to each of the 35 k-means++ centroids, and the training, validation, and test splits in Table 3 are drawn exclusively from this pool. The test metrics therefore measure performance only on cluster-core profiles. Figure 1 shows that the furthest 50 profiles in each cluster often have additional or missing lobes, and these peripheral profiles are never seen by the classifier. If the fraction of such peripheral profiles differs between DKIST, Hinode, and GREGOR, or between quiet Sun and sunspot data, the class fractions in Figures 2 and 11 and the cross-dataset comparisons would be biased in a way that the reported accuracies cannot detect. The sentence in Section 5 acknowledging "lack of proper generalisation to unseen data" identifies this issue but does not quantify it. Please add a held-out test set drawn from the full population (e.g., random profiles outside the cluster-core pool) and report metrics on that set, or explicitly restrict the paper's robustness claims to the cluster-core population.
  2. [§4.6, Eq. (2)] The amplitude-asymmetry analysis is presented as "empirical evidence that the MLP classifier has successfully distinguished between these morphological types based on asymmetry." However, the class definitions in §4.3 already impose that a symmetric profile has a subordinate lobe reaching at least 0.9 of the dominant lobe, while an asymmetric profile has a subordinate lobe below that threshold. Consequently, δa is near zero for the symmetric class and non-zero for the asymmetric class essentially by construction, so Figure 7 does not provide independent validation. To make this point load-bearing, the authors should use an asymmetry measure not directly tied to the labelling thresholds (e.g., area asymmetry or lobe-separation asymmetry) or compare distributions on a held-out sample with human labels that were not used in setting the 0.9 and 0.25 thresholds.
  3. [§4.7, Fig. 11] The sunspot classifier's "negative" class is defined to include both simple negative and negative double profiles because the MLP could not be trained to separate them. The central sunspot claim—that the 1564.85 nm line detects more reverse-polarity fields in the penumbra than the 630.25 nm line—rests on the relative counts of this class. If negative double profiles are more common in one line than the other, their inclusion in the same class biases the ratio, and because double profiles are associated at least in part with magneto-optical effects rather than genuine polarity reversals, the comparison with observed RPMF detection rates becomes ambiguous. Please report the proportion of manually labelled negative profiles that are actually double profiles for each spectral line, and discuss how this class impurity affects the comparison with Franz et al. (2016) and other observations.
  4. [§4.4, Fig. 6] The text states "We find no statistically significant differences in magnetic flux density across classes; the distributions are broadly similar," but no statistical test is described or reported. Given that the later discussion in Section 5 contrasts the lack of differences in magnetic flux density with clear trends in inclination angle, the absence of any significance testing weakens the claim. Please either provide appropriate statistical tests (e.g., Kruskal-Wallis, bootstrap confidence intervals) or rephrase the statement to report the observed distributional overlap without asserting statistical significance.
minor comments (4)
  1. [Abstract and title] There are minor typographical issues in the title and abstract: the title contains "F rom" with an extra space, and the abstract uses an unmatched quotation mark in "double’ profiles."
  2. [§3.3, Table 3] The hyperparameter table lists a batch size of 125 for DKIST; if this is not a typo for 128, please clarify, since all other batch sizes are powers of two.
  3. [§4.1] For the GREGOR dataset, the 60% and 23% fractions of circular and linear polarization are cited from a previous paper (Campbell et al. 2023a) rather than measured here; this should be stated more explicitly in the text so that the reader does not think all fractions come from the current analysis.
  4. [§5] The phrase "lack of proper generalisation to unseen data" in the Discussion is important, but it is not connected to any quantitative estimate of the generalization gap. This would be a natural place to mention the extent to which the held-out cluster-core test set overestimates performance on peripheral profiles.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the supervised classifier is trained on manual per-profile labels and the main physics claims are checked against independent simulation ground truth.

full rationale

The paper's central derivation chain is not circular. The MLP is trained on manually assigned per-profile labels defined by explicit lobe-amplitude thresholds (Section 3.3 and 4.3), not on the target statistics (class fractions, cross-dataset comparisons, or penumbral polarity-reversal rates). The reported validation and test metrics are measured on held-out profiles drawn from the same labelled pool, which is a standard supervised-learning evaluation rather than a fitted-input-called-prediction pattern. The comparison in Section 4.5 between k-means centroid labels and MLP labels is also not circular: the MLP's training targets are per-profile labels, not the centroid assignments, so the comparison directly measures whether the centroid-representative assumption introduces discrepancies. The authors acknowledge the sampling limitation that training pools were drawn from the 250 profiles closest to each k-means centroid (Section 3.3) and explicitly note 'lack of proper generalisation to unseen data' in Section 5; this is a robustness concern, not a definitional equivalence. The main physical conclusions are validated against independent information: the sunspot polarity-reversal statistics are checked directly against the simulation's inclination and Bz stratifications (Section 4.8), and the line-formation interpretations rest on external response-function calculations (Quintero Noda et al. 2021) and prior observational studies. Citations to the authors' own prior work (Campbell et al. 2023a,b) are used for dataset descriptions and noise properties, not as load-bearing justification for the novel claims. No equation or quantity in the paper is defined in terms of the output it is used to predict, and no fitted parameter is renamed as a prediction. The paper is therefore self-contained against external benchmarks and the appropriate finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claims depend on modeling assumptions (SIR forward synthesis and inversion, MANCHA and MURaM snapshots), on arbitrary classification thresholds, and on the assumption that cluster-core training profiles represent the full dataset. No new physical entities are introduced; the MLP classes are descriptive labels.

free parameters (4)
  • Subordinate lobe amplitude thresholds for class labels = 0.9 (symmetric) and 0.25 (single-lobed)
    Hand-chosen in Section 4.3 to define the boundary between symmetric, asymmetric, and single-lobed profiles. They determine every class label and thus all reported statistics.
  • k-means++ cluster count K = 35
    Chosen in Section 3.2 to provide a representative sampling pool; not optimized, but controls which profiles enter the training set.
  • Signal detection thresholds = 4 sigma_n (observations), 3 sigma_n of DKIST noise (degraded MANCHA), 0.02 I_c (sunspot)
    Set in Sections 4.1 and 4.7 to decide which pixels are counted as having signal; different thresholds across datasets affect the reported percentages.
  • MLP hyperparameters (hidden sizes, learning rate, batch size, dropout, epochs) = Per dataset, see Table 3
    Selected by hyperparameter search on validation sets; standard model selection. They do not enter the physics claims but affect the reported accuracies.
assumptions (6)
  • domain assumption SIR synthesis and inversion correctly model Stokes profile formation in the 630.25 nm and 1564.85 nm lines.
    Used in Sections 2.3 and 3.4 for forward modeling and inversion; standard in the field but assumed, not verified in this paper.
  • domain assumption The MANCHA and MURaM snapshots are representative of the quiet Sun and sunspot conditions being studied.
    Single snapshots (plus eight MANCHA timesteps) generate the synthetic classification statistics in Sections 4.3 and 4.7; the paper itself notes MURaM may misplace reverse polarity fields.
  • ad hoc to paper The four quiet Sun and five sunspot classes adequately capture the diversity of Stokes V profiles.
    Labels are defined by the authors in Sections 4.3 and 4.7; Section 4.7 concedes negative double profiles could not be represented as a separate class.
  • domain assumption Pre-processing steps (Doppler shift compensation, polarity sign flip, maximum normalization) do not distort the morphological classes.
    Applied in Section 3.1 to all quiet Sun profiles; the polarity sign flip is not applied to sunspot data, which the authors state is intentional.
  • ad hoc to paper The amplitude thresholds of 0.9 and 0.25 produce physically meaningful class boundaries.
    Stated to be arbitrary in Section 4.3 and used only for labeling consistency; the paper does not test sensitivity to these values.
  • domain assumption PCA noise removal preserves the shapes relevant to classification.
    Applied to observed quiet Sun data in Section 3.1; no shape-preservation check is reported.

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Pith. "Pith review of Application of Deep Learning to the Classification of Stokes Profiles: From the Quiet Sun to Sunspots." pith.science (2026). https://pith.science/paper/NPCGRKFO

@misc{pith2026250514275,
  author       = {Pith},
  title        = {Pith review of: Application of Deep Learning to the Classification of Stokes Profiles: From the Quiet Sun to Sunspots},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NPCGRKFO}},
  note         = {Machine review of arXiv:2505.14275}
}
abstract

The morphology of circular polarisation profiles from solar spectropolarimetric observations encode information about the magnetic field strength, inclination, and line-of-sight velocity gradients. Previous studies used manual methods or unsupervised machine learning (ML) to classify the shapes of circular polarisation profiles. We trained a multi-layer perceptron (MLP) comparing classifications with unsupervised ML. The method was tested on quiet Sun datasets from DKIST, Hinode, and GREGOR, as well as simulations of granulation and a sunspot. We achieve validation metrics typically close to or above $90\%$. We also present the first statistical analysis of quiet Sun DKIST/ViSP data using inversions and our supervised classifier. We demonstrate that classifications with unsupervised ML alone can introduce systemic errors that could compromise statistical comparisons. DKIST and Hinode classifications in the quiet Sun are similar, despite our modelling indicating spatial resolution differences should alter the shapes of circular polarization signals. Asymmetrical (symmetrical) profiles are less (more) common in GREGOR than DKIST or Hinode data, consistent with narrower response functions in the $1564.85$ nm line. Single-lobed profiles are extremely rare in GREGOR data. In the sunspot simulation, the $630.25$ nm line produces ``double' profiles in the penumbra, likely a manifestation of magneto-optical effects in horizontal fields; these are rarer in the $1564.85$ nm line. We find the $1564.85$ nm line detects more reverse polarity magnetic fields in the penumbra in contradiction to observations. We detect mixed-polarity profiles in nearly one fifth of the penumbra. Supervised ML robustly classifies solar spectropolarimetric data, enabling detailed statistical analyses of magnetic fields.

Figures

Figures reproduced from arXiv: 2505.14275 by the authors.

Figure 1
Figure 1. Classes of Stokes V profiles from DKIST/ViSP data for the 6302.5 ˚A line from kmeans++. In each panel the centroid is shown in red (solid lines), the closest 250 profiles are shown in gray (dashed lines), and the furthest 50 profiles are shown in blue (dotted lines). The percentage of profiles belonging to each class is shown above each panel. model’s performance on unseen data. Validation and testing scores, along … view at source ↗
Figure 2
Figure 2. Comparison of classification statistics for circular polarisation profiles from a collection of synthetic (MANCHA simulations) and real (DKIST, Hinode, and GREGOR) quiet Sun observations. Shown are the percentages of Stokes V signals belonging to the four classes (Symmetric, Q-like, Single-lobed, and Asymmetric) as a percentage of the total number of pixels (upper panel) and excluding pixels with no signal (lower pa… view at source ↗
Figure 3
Figure 3. Sankey diagram showing the statistical transfer in the classification statistics produced from the MLP when the original MANCHA quiet Sun data is synthesised in Fe I 630.25 nm (left) and 1564.85 nm (right). Numbers for each population are available in the upper panel of [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Synthetic Stokes V at a wavelength of 630.24 nm produced from a MANCHA simulation snapshot (upper row) and the associated labels (lower row) as determined by the MLP. Stokes V profiles are classified as one (E-Asymm), two (Asymm or Sym), or three or more (Q-like) lobes…
Figure 5
Figure 5. Figure 5: Probability density distributions of γ (top row), αmB (second row), and vLOS (third row) returned from the second cycle of the inversions (as described in [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Violin plots showing the distribution αmB, γ, and vLOS from DKIST/ViSP inversions at log(τ5000˚A) = −1.0, grouped by Stokes V morphological class as determined by the MLP. Each violin shows the kernel density estimate for pixels in a given class, with the white dot mar…
Figure 7
Figure 7. Figure 7: Violin plots of amplitude asymmetry (δa) for cir￾cular polarisation profiles classified as asymmetric and sym￾metric, shown separately for the DKIST, Hinode, and GRE￾GOR datasets. Each violin represents the distribution of δa within a given class and instrument. Horizo…
Figure 8
Figure 8. Figure 8: Sankey diagram showing the statistical difference in the classification statistics produced from k-means (lef t) and the MLP (right) for Stokes V profiles from DKIST/ViSP quiet Sun data. ing datasets (as we have done in [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: Sample double profile synthesised from the asso￾ciated original model atmosphere (solid, blue lines), shown with the synthesised profiles with the inclination adjusted to be constant in optical depth at values of 40◦ (orange, dot￾ted lines), 20◦ (dashed, green lines), …
Figure 10
Figure 10. Figure 10: Classification statistics shown for the MURaM sunspot simulation. Shown is the synthetic Stokes I (upper left) at a continuum wavelength, the magnetic inclination angle, γ, at an optical depth of log(τ5000˚A) = −0.5 (upper right), and the classification labels for the…
Figure 11
Figure 11. Figure 11: Comparison of classification statistics for cir￾cular polarisation profiles synthesised from the MURaM sunspot simulation snapshot in Fe I 630.25 nm (lower row) and 1564.85 nm (upper row). Shown are the percentages of Stokes V signals belonging to the five classes (Po…

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