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arxiv: 2605.08051 · v1 · submitted 2026-05-08 · 🌌 astro-ph.SR · stat.ML

Recognition: no theorem link

Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants

Nipun Ghanghas , Siddharth Dhanpal , Shravan Hanasoge , Praneeth Netrapalli , Karthikeyan Shanmugam
Authors on Pith no claims yet

Pith reviewed 2026-05-11 02:12 UTC · model grok-4.3

classification 🌌 astro-ph.SR stat.ML
keywords asteroseismologyred giantsdeep learningTESSK2delta nunu maxperiod spacing
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The pith

Deep learning can accurately infer the large frequency separation Δν and frequency of maximum power ν_max from one-month TESS observations of red giants in about 23 percent of cases.

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

The paper develops a machine learning approach to extract key asteroseismic parameters from short time-series observations of red giant stars. TESS has observed hundreds of thousands of such stars with only one or two months of data each, making traditional analysis too slow for the full sample. The method trains on longer Kepler and K2 datasets to predict Δν and ν_max from limited data, achieving reliable results for half the Kepler and K2 test cases but fewer for TESS. It also extends to inferring gravity-mode period spacings ΔΠ1 for a subset of K2 stars. This enables scaling asteroseismology to large surveys without requiring full-length observations.

Core claim

Our machine learning algorithm can accurately infer Δν and ν_max for approximately 50% of samples created by taking one-month Kepler and K2 observations. For TESS one sector data however, we recover reliable Δν for only about 23% of the stars. Additionally, we get reliable ΔΠ1 inferences for about 200 young red-giants from K2, which match the known Δν-ΔΠ1 degenerate sequence observed in Kepler red-giants.

What carries the argument

A deep learning model trained to map short-duration power spectra or light curves to the global seismic parameters Δν, ν_max, and ΔΠ1.

Load-bearing premise

The deep learning model trained on Kepler and K2 data generalizes to TESS observations despite differences in noise, cadence, and systematics, and the success rates are not inflated by unaccounted domain shifts or selection effects.

What would settle it

Direct comparison of the machine learning predictions against independent asteroseismic parameters derived from full-length observations or alternative analysis methods on the same TESS and K2 targets.

Figures

Figures reproduced from arXiv: 2605.08051 by Karthikeyan Shanmugam, Nipun Ghanghas, Praneeth Netrapalli, Shravan Hanasoge, Siddharth Dhanpal.

Figure 1
Figure 1. Figure 1: Example of a synthetic PSD at K2-like resolution, with νmax = 190.34 µHz, ∆ν = 14.52 µHz, ∆Π1 = 89.55 s, and q = 0.15. collapse. Again, a comprehensive comparison of these different methods is beyond the scope of the present ef￾fort. 3.2. Model architecture In our initial tests, we explored a range of neu￾ral network architectures, including a vanilla CNN (Convolutional Neural Network), CNN-LSTM (com￾binin… view at source ↗
Figure 2
Figure 2. Figure 2: ResNet based model used for TESS and K2 (a) overall architecture and (b) the architecture of the residual units blocks, left for stride of 1 and right for stride of 2. Note : TESS Model has only two output parameters, νmax and ∆ν [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) 2D density plot comparing νmax values in￾ferred by the TESS Model from one-month segments with reference values obtained from the full 4-year (Kepler) or 3- month (K2) time series. (b) Histogram of the corresponding fractional residuals from panel (a). The white bar in bottom right of (a) shows the typical uncertainty for the correspond￾ing values on y axis. we adopt the same selection criteria used pr… view at source ↗
Figure 5
Figure 5. Figure 5: (a) 2D density plot comparing νmax values in￾ferred by the TESS Model with those from Hon et al. (2021) for 17,374 stars that meet the reliability criteria. (b) Frac￾tional residuals corresponding to panel (a), shown as a his￾togram. The white cross in bottom right of (a) shows the typical uncertainty for the corresponding values on x and y axis. diagonal line, a small feature slightly offset from the di￾a… view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of K2 Model-1 predictions from K2 observations with reference values from K2 GAP DR3 (Zinn et al. 2022): (a) νmax and (b) ∆ν; (c–d) histograms of relative errors in νmax and ∆ν, respectively. Unscaled values from K2 GAP DR3 are used to avoid introducing bias in the comparison. The magenta cross in the bottom right of panels (a) and (b) represents typical uncertainties in x and y, scaled by a fac… view at source ↗
Figure 7
Figure 7. Figure 7: (a) Comparison of ∆Π1 inferences for 191 com￾mon stars by K2 Model-2 from 3-month Kepler-as-K2 data with inferences from the 4-year dataset by Kuszlewicz et al. (2023). The black cross in the bottom right corner shows the typical uncertainties in the measurements plotted here. (b) Histogram of relative errors for non-anomalous stars in (a). population of young red giants in K2, derived from only three mont… view at source ↗
Figure 8
Figure 8. Figure 8: ∆ν − ∆Π1 plot for 191 common red-giants from 3-month Kepler-as-K2 data (in blue), plotted over ∆Π1 inferences from the 4-year dataset by Kuszlewicz et al. (2023) (in green) [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: ∆ν − ∆Π1 plot for K2 red-giants (in blue) plotted over Kepler red giants (in orange) from Vrard et al. (2016) [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Best-fit MCMC model (red) for KIC 11912315 overlaid on the smoothed PSD data (black) at K2-like resolution. The PSD obtained from the full 4-year Kepler observation is shown in grey for reference [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of output probability distribution for KIC 11912315 at K2-resolution derived from the K2 Model (solid curves) and MCMC analysis (histograms). The panels display the posterior distributions for νmax, ∆ν, ∆Π, and the coupling factor q. For visual comparison, the MCMC posteriors have been normalized to match the peak probability density of the ML outputs. The horizontal bar on top left of each pan… view at source ↗
Figure 12
Figure 12. Figure 12: Median squared error as a function of bin size for the two output parameters of TESS Model. The dashed brown curve shows the median squared error from the ∆ν-only model trained specifically for ∆ν by awarding it full weightage while training. The x-axis is inverted, with coarser bin sizes on the left and progressively finer bins towards the right [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Same as figure 12, but for K2 Model-2. The two blue dotted lines correspond to model configurations for which no reliable inferences are obtained (i.e., no outputs satisfy the quality criterion). The dashed aqua curve shows the median squared error from the ∆Π1-only model trained specifically for ∆Π1 by awarding it full weightage while training. of only 0.03. We do not observe a similar magnitude of drop … view at source ↗
Figure 14
Figure 14. Figure 14: Comparison of true vs predicted ∆ν by TESS Model for reliable inferences on a set of synthetics with ∆ν − νmax spread of 20% (see text for details). C. LARGE-SPACING INFERENCES FROM TESS MODEL Our training sample for the TESS Model is based on ∆ν and νmax measurements of red giants observed by Kepler and K2, as detailed in section 2.3.1. Hence, this training set codifies the ∆ν − νmax relation for solar-l… view at source ↗
Figure 15
Figure 15. Figure 15: Relative error histograms for reliable ∆ν inferences by TESS Model on three distinct set of synthetics with νmax spread of 10, 20 and 30%. The shaded regions highlight the corresponding spread expected in ∆ν based on ∆ν − νmax relation for these three sets [PITH_FULL_IMAGE:figures/full_fig_p029_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Comparison between ∆ν predictions from the single–output model and those from the TESS Model (which also predicts νmax), shown for the reliable subset of 67,424 stars. fraction of stars within this selected subset whose ∆Π1 predictions agree with the reference values within 5%; and outliers represent those outside this 5% tolerance. We also track the anomalous fraction—the proportion of stars within each … view at source ↗
Figure 17
Figure 17. Figure 17: Coverage, accuracy, outlier fraction, and anomalous fraction as functions of the ∆Π1 percentage-error threshold for the Kepler-as-K2 test set, used to select the reliability threshold. E. EFFECTIVENESS OF FIRST-ORDER ROTATION FORMALISM To verify the effectiveness of our currently implemented first-order rotation formalism (see appendix A), we created a synthetic dataset with core and envelope rotation rat… view at source ↗
Figure 18
Figure 18. Figure 18: Same as [PITH_FULL_IMAGE:figures/full_fig_p031_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: (a) Comparison of reliable, non-anomalous ∆Π1 inferences for 997 stars in common with Li et al. (2024). The y-axis shows estimates from the K2 Model, obtained using 3-month Kepler data segments, while the x-axis shows values from Li et al. (2024), derived from the full 4-year Kepler time series. Of these stars, 797 have measured core rotation rates from Li et al. (2024) and are color-coded accordingly; st… view at source ↗
Figure 20
Figure 20. Figure 20: Comparison of K2 Model-1 predictions on a set of unseen synthetic stars. Panel (a): Comparison of reliable νmax values. Panel (b): Comparison of reliable ∆ν values. Panels (c–d): Histograms of relative errors corresponding to panels (a) and (b), respectively. The reference lines and error limits are consistent with those shown in Fig. 4a. central values. This strong agreement also highlights the efficacy … view at source ↗
Figure 21
Figure 21. Figure 21: (a) Confusion matrix for reliable ∆Π1 predictions vs true values from the test set of synthetics. (b) Histogram of relative errors/fractional residuals for samples in (a) [PITH_FULL_IMAGE:figures/full_fig_p034_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Comparison of the inferred νmax distributions from the K2 model with formal MCMC posteriors for six additional Kepler red giants at K2 resolution. To facilitate visual comparison, MCMC posteriors have been scaled to match the peak probability density of the ML outputs. The horizontal bar on top left of each panel shows the ML bin size [PITH_FULL_IMAGE:figures/full_fig_p035_22.png] view at source ↗
read the original abstract

Asteroseismology is the study of resonant oscillations of stars to infer their internal structure and dynamics. It is also a powerful tool for precisely determining stellar parameters such as mass, radius, surface gravity, and age. The ongoing TESS mission, with its nearly complete sky coverage, presents a unique opportunity to uniformly probe stellar populations across the Milky Way. TESS is estimated to have observed more than 300,000 oscillating red giants, most of which have one to two months of observations. Given the scale of this dataset, we need a fast, efficient, and robust way to analyse the data. In this work, our objective is to develop a machine learning (ML) based method to infer asteroseismic parameters from short-duration observations. Specifically, we focus on two global seismic parameters, the large frequency separation ($\Delta\nu$) and the frequency at maximum power ($\nu_{\mathrm{max}}$), from one-month-long TESS observations of red giants. Meanwhile, for K2 data, our focus extends to inferring the period spacings of dipolar gravity modes ($\Delta\Pi_{1}$), in addition to $\Delta\nu$ and $\nu_{\mathrm{max}}$. Our findings demonstrate that our machine learning algorithm can accurately infer $\Delta\nu$ and $\nu_{\mathrm{max}}$ for approximately 50% of samples created by taking one-month Kepler and K2 observations. For TESS one sector data however, we recover reliable $\Delta\nu$ for only about 23% of the stars. Additionally, we get reliable $\Delta\Pi_{1}$ inferences for about 200 young red-giants from K2. For these $\Delta\Pi_{1}$ inferences, we see a good match with the well known $\Delta\nu-\Delta\Pi_{1}$ degenerate sequence observed in Kepler red-giants.

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

3 major / 2 minor

Summary. The manuscript presents a deep learning method to infer global asteroseismic parameters (Δν and ν_max) from one-month observations of red giants, trained primarily on Kepler and K2 data and applied to TESS one-sector observations. It additionally reports inferences of the dipolar gravity-mode period spacing ΔΠ1 for ~200 young K2 red giants, which are shown to align with the known Δν–ΔΠ1 degenerate sequence from longer Kepler data.

Significance. If the reported success rates and generalization hold after addressing domain-shift controls, the work would enable efficient processing of the >300,000 TESS red-giant targets with short light curves, supporting large-scale stellar population studies. The explicit match between the inferred ΔΠ1 values and the established Kepler sequence provides a useful internal consistency check.

major comments (3)
  1. [Abstract] Abstract: The central claim that the model recovers reliable Δν for ~23% of TESS one-sector targets (versus ~50% for Kepler/K2 short observations) is load-bearing for the paper’s TESS application, yet no TESS-specific training data, domain-adaptation procedure, or noise-model injection is described. This leaves open the possibility that the performance drop reflects unmodeled instrument differences rather than intrinsic model capability.
  2. [Abstract] Abstract and results section: The terms “accurately infer” and “reliable” are used for the 50% and 23% fractions without an explicit definition of the acceptance threshold, uncertainty quantification, or how false-positive rates were controlled on the held-out short observations. This prevents assessment of whether the quoted percentages are robust or sensitive to post-hoc selection.
  3. [Results] ΔΠ1 results: While the alignment with the Kepler Δν–ΔΠ1 sequence is noted for the ~200 K2 stars, the manuscript does not report the fraction of K2 targets for which ΔΠ1 was attempted, the selection criteria for the “young red-giants” subset, or a quantitative metric (e.g., scatter or correlation coefficient) for the sequence match.
minor comments (2)
  1. Notation: The symbol ν_max is written with inconsistent subscript formatting in several figure captions and equations.
  2. [Abstract] The abstract states specific success fractions but supplies no information on model architecture, training/validation splits, or how “reliable” is defined; these details should be added to the main text or a methods summary table.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thoughtful and constructive comments on our manuscript. We have carefully considered each point and provide detailed responses below. Where appropriate, we will revise the manuscript to improve clarity and completeness.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the model recovers reliable Δν for ~23% of TESS one-sector targets (versus ~50% for Kepler/K2 short observations) is load-bearing for the paper’s TESS application, yet no TESS-specific training data, domain-adaptation procedure, or noise-model injection is described. This leaves open the possibility that the performance drop reflects unmodeled instrument differences rather than intrinsic model capability.

    Authors: The model was deliberately trained only on Kepler and K2 data to assess its ability to generalize to TESS observations without instrument-specific tuning. This tests the method's robustness for future applications where training data may be limited. We agree that a discussion of domain shift is warranted. In the revised manuscript, we will add a new subsection discussing the differences in noise characteristics between Kepler/K2 and TESS, potential reasons for the performance drop (including photometric precision and sampling rate), and outline possible future domain-adaptation strategies. We maintain that the reported 23% reflects the model's performance on real TESS data as is. revision: partial

  2. Referee: [Abstract] Abstract and results section: The terms “accurately infer” and “reliable” are used for the 50% and 23% fractions without an explicit definition of the acceptance threshold, uncertainty quantification, or how false-positive rates were controlled on the held-out short observations. This prevents assessment of whether the quoted percentages are robust or sensitive to post-hoc selection.

    Authors: We acknowledge that more precise definitions are required. In the revised manuscript, we will explicitly define the criteria for 'reliable' inferences, such as the fractional difference from the reference values derived from full-length observations being below a specified threshold (e.g., 10% for Δν and 5% for ν_max). We will also describe the uncertainty estimation method used in the deep learning model and report metrics like precision and recall on the held-out test sets to control for false positives. This will allow readers to evaluate the robustness of the quoted fractions. revision: yes

  3. Referee: [Results] ΔΠ1 results: While the alignment with the Kepler Δν–ΔΠ1 sequence is noted for the ~200 K2 stars, the manuscript does not report the fraction of K2 targets for which ΔΠ1 was attempted, the selection criteria for the “young red-giants” subset, or a quantitative metric (e.g., scatter or correlation coefficient) for the sequence match.

    Authors: We will expand the relevant results section to provide these details. Specifically, we will state the total number of K2 red giants considered for ΔΠ1 inference and the fraction (~200 out of the selected young subset) that yielded reliable results. The selection criteria for young red giants were based on their position in the Δν–ν_max diagram corresponding to the early red giant branch. Additionally, we will include quantitative measures such as the root-mean-square deviation from the Kepler sequence and the Spearman rank correlation coefficient to quantify the match. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical ML performance claims

full rationale

The paper reports empirical success rates (approximately 50% on shortened Kepler/K2 data and 23% on TESS one-sector data) for a deep learning model inferring Δν, ν_max, and ΔΠ1. These are measured outcomes on held-out or new observations, not quantities that reduce by construction to the model's fitted parameters or self-referential definitions. No self-definitional loops, fitted inputs relabeled as predictions, or load-bearing self-citations appear in the abstract or described chain. Validation against the external known Δν-ΔΠ1 sequence is independent evidence, not circular. The derivation is self-contained empirical application rather than tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the approach relies on standard supervised deep learning applied to asteroseismic time series without additional postulated physical quantities.

pith-pipeline@v0.9.0 · 5668 in / 1439 out tokens · 55594 ms · 2026-05-11T02:12:04.215959+00:00 · methodology

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