标题： 从恒星光到天体物理洞察：使用机器学习自动化变星研究 显示英文标题

标题： From stellar light to astrophysical insight: automating variable star research with machine learning

摘要： 大规模的测光调查正在通过提供前所未有的数据量而彻底改变天文学。 来自如美国国家航空航天局（NASA）开普勒和TESS卫星，以及即将推出的欧洲空间局（ESA）PLATO任务等任务的丰富数据集，为恒星变异性、星震学和系外行星研究提供了宝贵的资源。 为了充分发挥这些大规模数据集的科学潜力，需要自动化的数据驱动方法。 在本篇综述中，我展示了机器学习如何将星震学带入自动化科学发现的新时代，涵盖了从数据清理到变异性分类和参数推断的整个过程，同时突出了表示学习、多模态数据集和基础模型的最新进展。 这篇特邀综述为机器学习在恒星变异性研究中带来的挑战和机遇提供了指导，并探讨了它如何帮助开拓时域天文学的新领域。 显示英文摘要

摘要： Large-scale photometric surveys are revolutionizing astronomy by delivering unprecedented amounts of data. The rich data sets from missions such as the NASA Kepler and TESS satellites, and the upcoming ESA PLATO mission, are a treasure trove for stellar variability, asteroseismology and exoplanet studies. In order to unlock the full scientific potential of these massive data sets, automated data-driven methods are needed. In this review, I illustrate how machine learning is bringing asteroseismology toward an era of automated scientific discovery, covering the full cycle from data cleaning to variability classification and parameter inference, while highlighting the recent advances in representation learning, multimodal datasets and foundation models. This invited review offers a guide to the challenges and opportunities machine learning brings for stellar variability research and how it could help unlock new frontiers in time-domain astronomy.