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