English(EN) Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants

深度学习从短期天文观测中推断恒星参数

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-08 17:41

研究人员开发了一种深度学习方法，可以从短期天文观测中推断星震学参数。该模型旨在高效分析TESS等任务的数据，TESS已观测了数十万颗红巨星。该研究证明了机器学习算法能够从为期一个月的TESS和K2观测数据中准确推断出关键参数，如大频率分离和最大功率频率，尽管不同数据集的成功率各不相同。 AI

影响能够更快、更高效地分析大型天文数据集，可能加速恒星的发现和表征。

排序理由详细介绍一种用于科学研究的新机器学习方法的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv stat.ML TIER_1 English(EN) · Nipun Ghanghas, Siddharth Dhanpal, Shravan Hanasoge, Praneeth Netrapalli, Karthikeyan Shanmugam · 2026-05-11 04:00

利用深度学习从短期观测推断恒星震学参数：以TESS和K2红巨星为例

arXiv:2605.08051v1 Announce Type: cross Abstract: Asteroseismology is the study of resonant oscillations of stars to infer their internal structure and dynamics. It is also a powerful tool for precisely determining stellar parameters such as mass, radius, surface gravity, and age…
arXiv stat.ML TIER_1 English(EN) · Karthikeyan Shanmugam · 2026-05-08 17:41

基于深度学习的短时观测中星震参数推断：以TESS和K2红巨星为例

Asteroseismology is the study of resonant oscillations of stars to infer their internal structure and dynamics. It is also a powerful tool for precisely determining stellar parameters such as mass, radius, surface gravity, and age. The ongoing TESS mission, with its nearly comple…