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A convolutional autoencoder on TESS light curves identifies 24 candidate cool dwarfs with potential solar-like oscillations.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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A convolutional autoencoder applied to TESS data flags 24 candidate cool dwarf stars as potential solar-like oscillators after achieving 99.8% test accuracy on known examples.

T0 review reviewed 2026-06-30 challenge →

load-bearing objection The paper trains a conv autoencoder on known TESS oscillators and lists 24 new cool-dwarf candidates, but supplies no quantitative checks that the final list is mostly real detections rather than false positives. the 2 major comments →

arxiv 2605.24269 v1 pith:67GPZ2DS submitted 2026-05-22 astro-ph.SR

Pushing the Limit of Asteroseismic Detection for Cool Dwarfs using TESS and Deep Learning

Waly M Z Karim , Rocio Kiman , Derek Buzasi , Cecilia Garraffo , Joshua D. Wing , Jim Fuller , Benjamin J. Ricketts , Viktor Khalack
show 1 more author
Sajia Shahrin Neha
This is my paper
classification astro-ph.SR
keywords asteroseismologyTESSsolar-like oscillationscool dwarfsconvolutional autoencoderdeep learningmain-sequence starsstellar interiors
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

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 trains a convolutional autoencoder on TESS two-minute light curves of confirmed solar-like oscillators to automatically flag similar signals in cool main-sequence and sub-giant stars. These signals have low amplitudes that have so far limited confirmed detections to only a small number of such dwarfs. The trained network reaches 99.8 percent accuracy on held-out known cases and returns 24 high-probability candidates from the Asteroseismic Target List. A sympathetic reader would care because several candidates sit in color-magnitude regions previously inaccessible to TESS photometry alone, offering a route to enlarge the sample of stars whose interiors can be measured directly.

Core claim

The paper claims that a convolutional autoencoder trained and validated on catalogs of confirmed solar-like oscillators can identify potential new oscillators among cool dwarfs from TESS data, yielding a list of 24 candidates that extend the detection frontier of TESS-based asteroseismology into regions of the color-magnitude diagram accessible only through more resource-intensive radial velocity observations.

What carries the argument

Convolutional autoencoder that classifies solar-like oscillation features in TESS two-minute light curves of cool dwarf stars.

Load-bearing premise

High accuracy on known oscillators will translate into reliable identification of true new oscillators rather than false positives when the model is applied to unconfirmed targets.

What would settle it

Radial velocity or additional high-precision photometry that either detects or fails to detect the expected solar-like oscillation frequencies in any of the 24 candidate stars.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Several candidates occupy color-magnitude regions previously out of reach for TESS photometry, demonstrating extension of the detection limit.
  • The candidate list supplies a concrete starting point for follow-up observations aimed at confirming oscillations.
  • Confirmed detections would increase the number of cool dwarfs with asteroseismic constraints on interior structure and evolution.
  • The approach shows that deep learning can be used to mine large photometric datasets for weak oscillation signals.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same model architecture could be retrained on data from other photometric surveys to search for additional cool-dwarf oscillators.
  • Confirmed candidates would allow direct tests of theoretical predictions for how oscillation amplitudes scale with stellar mass and age on the lower main sequence.
  • Prioritizing these targets for multi-epoch observations could also constrain the interplay between oscillations and magnetic activity in cool dwarfs.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 0 minor

Summary. The manuscript trains a convolutional autoencoder on TESS 2-minute light curves of confirmed cool-dwarf solar-like oscillators, reports test-set metrics of 99.8% accuracy, 0.945 precision, 0.998 recall and 0.971 F1, applies the model to the Asteroseismic Target List to obtain 3463 objects with probability >0.5, and after unspecified further analysis selects a final list of 24 candidate stars whose detection would extend the TESS asteroseismic frontier to cooler main-sequence and sub-giant regimes.

Significance. A reliably validated list of 24 new cool-dwarf oscillators would be a meaningful addition to the sparse sample of main-sequence solar-like oscillators and would support the use of deep-learning classifiers on TESS data. The current manuscript, however, provides no quantitative validation that the 24 candidates are predominantly true oscillators rather than false positives arising from domain shift between the training distribution and the unlabeled ATL stars.

major comments (2)
  1. [Abstract] Abstract: the reduction from 3463 high-probability objects to the final list of 24 candidates is described only as 'further analysis' with no quantitative criteria, no reported false-positive rate, and no error bars or cross-validation supplied for the new detections. This step is load-bearing for the central claim that the model extends the detection frontier.
  2. [Abstract] Abstract: the reported test-set metrics (99.8% accuracy, 0.945 precision) are obtained exclusively on catalogs of already-confirmed oscillators; no injection-recovery tests, no comparison with an independent detection pipeline, and no assessment of how differences in SNR, T_eff or light-curve systematics between the training set and the ATL affect the probability threshold (>0.5) are presented. Consequently the generalization to unlabeled ATL stars remains untested.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We are grateful to the referee for their thorough review and valuable suggestions. Below we respond to each major comment. We agree that enhancements to the validation and description of the candidate selection are required and will implement these changes in the revised manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the reduction from 3463 high-probability objects to the final list of 24 candidates is described only as 'further analysis' with no quantitative criteria, no reported false-positive rate, and no error bars or cross-validation supplied for the new detections. This step is load-bearing for the central claim that the model extends the detection frontier.

    Authors: We concur that the abstract does not adequately detail the process by which the 3463 objects were reduced to 24 candidates. This step indeed requires more transparency to substantiate the central claim. In the revised manuscript, we will elaborate on the further analysis by specifying the quantitative criteria employed, such as additional probability thresholds, quality cuts on the light curves, and any cross-checks performed. We will also include an assessment of the false-positive rate and associated uncertainties for the final list of candidates. revision: yes

  2. Referee: [Abstract] Abstract: the reported test-set metrics (99.8% accuracy, 0.945 precision) are obtained exclusively on catalogs of already-confirmed oscillators; no injection-recovery tests, no comparison with an independent detection pipeline, and no assessment of how differences in SNR, T_eff or light-curve systematics between the training set and the ATL affect the probability threshold (>0.5) are presented. Consequently the generalization to unlabeled ATL stars remains untested.

    Authors: We acknowledge that the performance metrics are derived from the test set consisting of confirmed oscillators and that no explicit tests for generalization, such as injection-recovery experiments or comparisons to independent pipelines, are included. Differences in stellar parameters and data characteristics between the training set and the ATL could affect the results. In the revision, we will incorporate injection-recovery tests tailored to the ATL sample, evaluate the effects of SNR and T_eff variations on the probability threshold, and provide a comparison with other detection methods where possible to better demonstrate the model's applicability to unlabeled data. revision: yes

Circularity Check

0 steps flagged

No circularity: model trained on confirmed set applied to separate ATL targets

full rationale

The paper trains a convolutional autoencoder exclusively on catalogs of already-confirmed solar-like oscillators, reports standard classification metrics on a held-out test subset of that same distribution, then applies the fixed model to the distinct, unlabeled Asteroseismic Target List to produce candidate probabilities. No equation or step reduces a claimed prediction to a fitted parameter of the same data by construction, no self-citation supplies a load-bearing uniqueness theorem, and no ansatz is smuggled in. The 24-candidate list is the output of an independent forward pass on new stars rather than a renaming or re-derivation of the training inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated.

reviewed 2026-06-30 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Pushing the Limit of Asteroseismic Detection for Cool Dwarfs using TESS and Deep Learning." pith.science (2026). https://pith.science/paper/67GPZ2DS

@misc{pith2026260524269,
  author       = {Pith},
  title        = {Pith review of: Pushing the Limit of Asteroseismic Detection for Cool Dwarfs using TESS and Deep Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/67GPZ2DS}},
  note         = {Machine review of arXiv:2605.24269}
}
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read the original abstract

Asteroseismology provides a powerful probe of stellar interiors by detecting stellar oscillations, including solar-like oscillations, which are stochastically excited by near-surface convection. While thousands of solar-like oscillators have been identified in evolved stars, only a limited number of main-sequence cool dwarfs have confirmed oscillations due to the low amplitudes of their signals. In this work, we train a convolutional autoencoder on TESS two-minute light curves to automatically identify solar-like oscillation features in cool dwarf main sequence and sub-giant stars. Using catalogs of confirmed oscillators for training and validation, our network achieves a classification accuracy of 99.8% on the test set, along with Precision of 0.945, Recall of 0.998, and F1 Score of 0.971. From the Asteroseismic Target List, our model identifies 3463 potential solar-like oscillators (probability greater than 0.5). After further analysis, we find a list of 24 candidate stars that have the potential to exhibit solar-like oscillations. Notably, several of these candidates occupy regions of the color-magnitude diagram that are accessible only through more resource-intensive radial velocity observations, thereby has the potential of extending the detection frontier of TESS-based asteroseismology. Our candidate catalog provides a valuable foundation for follow-up efforts aimed at expanding the sample of cool-dwarf solar-like oscillators. This will ultimately improve our understanding of stellar structure and evolution across the lower main sequence and strengthen the evidence for using deep learning techniques to study stellar light curves.

Figures

Figures reproduced from arXiv: 2605.24269 by Benjamin J. Ricketts, Cecilia Garraffo, Derek Buzasi, Jim Fuller, Joshua D. Wing, Rocio Kiman, Sajia Shahrin Neha, Viktor Khalack, Waly M Z Karim.

Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Power Spectrum of a solar-like oscillator of an evolved star with TIC 141201954. Granulation background fit is in red and oscillation power excess frequency is marked in a blue vertical dashed line. The figure is just for schematic illustration, and does not serve any quan￾titative purpose [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Loss as a function of epochs for autoencoder (left) and classifier (right). Only training loss is shown for autoencoder while both training and validation loss is shown for classifier. We computed validation loss for all epochs only for the classifier network. minutes and the classifier training took around 2 hour 11 minutes [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Confusion Matrix for test set. Here, SOLR refers to solar-like oscillator and NonSOLR refers to stars that do not exhibit solar-like oscillation. The color bar describes the fractional percentage in each panel between 0 and 1. True positive is 99.8% and true negative is 94.2%. False positive and false negative are 5.8% and 0.2% respectively. ing their periodograms, we found that for most stars, the low-fre… view at source ↗
Figure 7
Figure 7. Figure 7: Diagnostic plots for five solar-like oscillator candidates from [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Stellar radius versus effective temperature plot for our candidates (star symbol) and previously discovered so￾lar-like oscillators using TESS (violet circles), Kepler (orange circles), and radial velocity (black triangles). Our candidates are color-coded by their signal-to-noise ratio. TESS and Ke￾pler stars were taken from E. Hatt et al. (2023) and S. Mathur et al. (2017), respectively. Radial velocity c… view at source ↗
Figure 9
Figure 9. Figure 9: Diagnostic plots for two solar-like oscillator candidates from [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Same as [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of predicted νmax from ATL catalog (D. Hey et al. 2024) and obseved νmax using pySYD. The stars are color-coded based on their effective temperature. All the candidates with ther TIC IDs are discussed in this section. νmax > 3600 µHz, the difference grows larger. This fre￾quency range lies near the Nyquist limit for TESS 2 min cadence data (4167 µHz, shown in a vertical black dashed line), sugg… view at source ↗
Figure 12
Figure 12. Figure 12: Color-Magnitude Diagram of the candidate stars from [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Diagnostic plots of TIC 240764948, TIC 406430692, TIC 36596946, TIC 83489517, and TIC 34115230. First Column: power spectrum with region near observed νmax is shaded in dark grey and predicted νmax from scaling relation in dotted red line, Second Column: background-corrected power spectrum zoomed-in on expected νmax, Third Column: auto-correlation function, Fourth Column: ´echelle diagram. The plots are i… view at source ↗
Figure 14
Figure 14. Figure 14: The same at at [PITH_FULL_IMAGE:figures/full_fig_p026_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: The same at at [PITH_FULL_IMAGE:figures/full_fig_p027_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: The same at at [PITH_FULL_IMAGE:figures/full_fig_p028_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Detailed architecture of the encoder network [PITH_FULL_IMAGE:figures/full_fig_p028_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Comparison of latent space representation between a solar-like oscillator and other types of variables. The SOLR column represents solar-like oscillation and NonSOLR column represents other types of variables. All signals are normalized between 0 and 1 [PITH_FULL_IMAGE:figures/full_fig_p029_18.png] view at source ↗

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  1. [1]

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  3. [3]

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    work page internal anchor Pith review Pith/arXiv arXiv doi:10.3847/1538-4357/ac7c74 2017

This paper was first reviewed by grok-4.3 on June 30, 2026.