Potential of Gaia XP Spectra in Red Giant Star Asteroseismology: A Deep-Learning Approach
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| Autores principales: | , , , |
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| Formato: | Preprint |
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2026
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| _version_ | 1866917418055499776 |
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| author | Barman, Rajarshi Bhattacharya, Shatanik Hanasoge, Shravan M. Dhanpal, Siddharth |
| author_facet | Barman, Rajarshi Bhattacharya, Shatanik Hanasoge, Shravan M. Dhanpal, Siddharth |
| contents | Red giants are tracers of stellar evolution & Galactic structure & their asteroseismic properties, particularly large frequency separation, frequency of maximum oscillation power & dipole-mode period spacing, provide direct insight into their internal structure, masses & evolutionary states. Until now, seismic inferences on large stellar samples relied primarily on high-quality light curves from missions such as Kepler & TESS, or on moderate-resolution spectroscopy (LAMOST: R ~ 1800 & APOGEE: R ~ 22500) that clearly preserve information correlated with these seismic quantities. With Gaia XP spectra (R ~ 15-85), the possibility arises to extend asteroseismic measurements to orders of magnitude more stars, despite the much lower spectral res. . Our goal is to assess whether XP spectra retain enough information to enable reliable seismic inference for RGs. We develop hybrid CNN-LSTM models trained on RGs with seismic parameters measured from Kepler photometry. The networks learn the subtle spectral signatures, imprinted through global stellar properties, that correlate with Δν, ν_max & ΔΠ_1. The models recover all three global asteroseismic parameters from Gaia XP spectra with accuracies comparable to results based on moderate-res. surveys such as LAMOST, demonstrating that even low-res. spectrophotometry carries sufficient information for seismic prediction. Saliency analysis reveals wavelength regions most strongly associated with seismic sensitivity & highlights physically distinct spectral behaviour between RGB & RC stars. Applying our models to Gaia DR3 yields seismic predictions for more than 2.5 M bright RGs, enabling population-level asteroseismic studies on an unprecedented scale. We also identify a small subset of low-Δνred clump candidates showing unusual spectral-seismic correlations, offering new avenues for investigating evolved stellar populations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17045 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Potential of Gaia XP Spectra in Red Giant Star Asteroseismology: A Deep-Learning Approach Barman, Rajarshi Bhattacharya, Shatanik Hanasoge, Shravan M. Dhanpal, Siddharth Solar and Stellar Astrophysics Red giants are tracers of stellar evolution & Galactic structure & their asteroseismic properties, particularly large frequency separation, frequency of maximum oscillation power & dipole-mode period spacing, provide direct insight into their internal structure, masses & evolutionary states. Until now, seismic inferences on large stellar samples relied primarily on high-quality light curves from missions such as Kepler & TESS, or on moderate-resolution spectroscopy (LAMOST: R ~ 1800 & APOGEE: R ~ 22500) that clearly preserve information correlated with these seismic quantities. With Gaia XP spectra (R ~ 15-85), the possibility arises to extend asteroseismic measurements to orders of magnitude more stars, despite the much lower spectral res. . Our goal is to assess whether XP spectra retain enough information to enable reliable seismic inference for RGs. We develop hybrid CNN-LSTM models trained on RGs with seismic parameters measured from Kepler photometry. The networks learn the subtle spectral signatures, imprinted through global stellar properties, that correlate with Δν, ν_max & ΔΠ_1. The models recover all three global asteroseismic parameters from Gaia XP spectra with accuracies comparable to results based on moderate-res. surveys such as LAMOST, demonstrating that even low-res. spectrophotometry carries sufficient information for seismic prediction. Saliency analysis reveals wavelength regions most strongly associated with seismic sensitivity & highlights physically distinct spectral behaviour between RGB & RC stars. Applying our models to Gaia DR3 yields seismic predictions for more than 2.5 M bright RGs, enabling population-level asteroseismic studies on an unprecedented scale. We also identify a small subset of low-Δνred clump candidates showing unusual spectral-seismic correlations, offering new avenues for investigating evolved stellar populations. |
| title | Potential of Gaia XP Spectra in Red Giant Star Asteroseismology: A Deep-Learning Approach |
| topic | Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2604.17045 |