论文标题
星形磁形式：从变压器的恒星光曲线中推断出星的表面重力
Astroconformer: Inferring Surface Gravity of Stars from Stellar Light Curves with Transformer
论文作者
论文摘要
我们介绍了基于变压器的模型Astroconformer，以分析开普勒任务的恒星光曲线。我们证明，AstronConformer可以鲁棒地推断出恒星的表面重力作为监督任务。重要的是，由于变压器在时间序列中捕获了远程信息，它的表现优于该领域的最新数据驱动方法，并且通过消融实验证明了自我注意力的关键作用。此外，来自Astroconformenter的注意力图例子说明了该模型学到的远程相关信息，从而导致了一种更容易解释的Asteroseology方法。除了开普勒的数据外，我们还表明，该方法可以概括为鲁宾天文台的稀疏节奏光曲线，为星空学的新时代铺平了道路，从而利用了长期基于地面观测的信息。
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.
