Light curves of stars encapsulate a wealth of information about stellar oscillations and granulation, thereby offering key insights into the internal structure and evolutionary state of stars. Conventional asteroseismic techniques have been largely confined to power spectral analysis, neglecting the valuable phase information contained within light curves. While recent machine learning applications in asteroseismology utilizing Convolutional Neural Networks (CNNs) have successfully inferred stellar attributes from light curves, they are often limited by the local feature extraction inherent in convolutional operations. To circumvent these constraints, we present $\textit{Astroconformer}$, a Transformer-based deep learning framework designed to capture long-range dependencies in stellar light curves. Our empirical analysis, which focuses on estimating surface gravity ($\log g$), is grounded in a carefully curated dataset derived from $\textit{Kepler}$ light curves. These light curves feature asteroseismic $\log g$ values spanning from 0.2 to 4.4. Our results underscore that, in the regime where the training data is abundant, $\textit{Astroconformer}$ attains a root-mean-square-error (RMSE) of 0.017 dex around $\log g \approx 3 $. Even in regions where training data are sparse, the RMSE can reach 0.1 dex. It outperforms not only the K-nearest neighbor-based model ($\textit{The SWAN}$) but also state-of-the-art CNNs. Ablation studies confirm that the efficacy of the models in this particular task is strongly influenced by the size of their receptive fields, with larger receptive fields correlating with enhanced performance. Moreover, we find that the attention mechanisms within $\textit{Astroconformer}$ are well-aligned with the inherent characteristics of stellar oscillations and granulation present in the light curves.

翻译：恒星光变曲线蕴含着关于恒星振荡和粒化过程的丰富信息，为揭示恒星的内部结构和演化状态提供了关键线索。传统星震学技术主要局限于功率谱分析，忽略了光变曲线中蕴含的宝贵相位信息。尽管近期利用卷积神经网络（CNN）的机器学习应用已成功从光变曲线中推断恒星属性，但卷积运算固有的局部特征提取能力常限制其性能。为突破这些局限，我们提出$\textit{Astroconformer}$——一种基于Transformer的深度学习框架，旨在捕获恒星光变曲线中的长程依赖关系。本项聚焦于估算表面重力（$\log g$）的实证分析，基于精心整理的$\textit{Kepler}$光变曲线数据集展开。这些光变曲线对应的星震学$\log g$值跨度范围为0.2至4.4。研究结果表明，在训练数据充足的条件下，$\textit{Astroconformer}$在$\log g \approx 3$附近可实现0.017 dex的均方根误差（RMSE）；即使在训练数据稀疏区域，RMSE仍可达0.1 dex。该模型不仅优于基于K近邻的模型（$\textit{The SWAN}$），还超越了最先进的CNN模型。消融研究证实，模型在该特定任务中的性能受感受野大小显著影响，感受野越大则性能越强。此外，我们发现$\textit{Astroconformer}$中的注意力机制与光变曲线中恒星振荡及粒化过程的固有特征高度契合。