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The age and mass of red giants are essential for understanding the structureand evolution of the Milky Way. Traditional isochrone methods for theseestimations are inherently limited due to overlapping isochrones in theHertzsprung-Russell diagram, while asteroseismology, though more precise,requires high-precision, long-term observations. In response to thesechallenges, we developed a novel framework, Spectral Transformer (SPT), topredict the age and mass of red giants aligned with asteroseismology from theirspectra. A key component of SPT, the Multi-head Hadamard Self-Attentionmechanism, designed specifically for spectra, can capture complex relationshipsacross different wavelength. Further, we introduced a Mahalanobisdistance-based loss function to address scale imbalance and interaction modeloss, and incorporated Monte Carlo dropout for quantitative analysis ofprediction uncertainty.Trained and tested on 3,880 red giant spectra fromLAMOST, the SPT achieved remarkable age and mass estimations with averagepercentage errors of 17.64% and 6.61%, respectively, and provided uncertaintiesfor each corresponding prediction. The results significantly outperform thoseof traditional machine learning algorithms and demonstrate a high level ofconsistency with asteroseismology methods and isochrone fitting techniques. Inthe future, our work will leverage datasets from the Chinese Space StationTelescope and the Large Synoptic Survey Telescope to enhance the precision ofthe model and broaden its applicability in the field of astronomy andastrophysics.

SPT: Spectral Transformer for Red Giant Stars Age and Mass Estimation