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We introduce Astroconformer, a Transformer-based model to analyze stellarlight curves from the Kepler mission. We demonstrate that Astrconformer canrobustly infer the stellar surface gravity as a supervised task. Importantly,as Transformer captures long-range information in the time series, itoutperforms the state-of-the-art data-driven method in the field, and thecritical role of self-attention is proved through ablation experiments.Furthermore, the attention map from Astroconformer exemplifies the long-rangecorrelation information learned by the model, leading to a more interpretabledeep learning approach for asteroseismology. Besides data from Kepler, we alsoshow that the method can generalize to sparse cadence light curves from theRubin Observatory, paving the way for the new era of asteroseismology,harnessing information from long-cadence ground-based observations.

Astroconformer: Inferring Surface Gravity of Stars from Stellar Light Curves with Transformer