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

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 Transiting Exoplanet Survey Satellite (TESS) two-minute light curves to automatically identify solar-like oscillation features in cool dwarf main sequence and subgiant 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 a precision of 0.945, a recall of 0.998, and an F1 score of 0.971. From the Asteroseismic Target List, our model identifies 3463 potential solar-like oscillators (probability > 50%). 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, and thereby have the potential to extend 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.