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 complete sky coverage, presents a unique opportunity to uniformly probe stellar populations across the Milky Way. TESS is estimated to have observed more than 300,000 oscillating red giants, most of which have one to two months of observations. Given the scale of this dataset, we need a fast, efficient, and robust way to analyse the data. In this work, our objective is to develop a machine learning (ML) based method to infer asteroseismic parameters from short-duration observations. Specifically, we focus on two global seismic parameters, the large frequency separation ($Δν$) and the frequency at maximum power ($ν_{\mathrm{max}}$), from one-month-long TESS observations of red giants. Meanwhile, for K2 data, our focus extends to inferring the period spacings of dipolar gravity modes ($ΔΠ_{1}$), in addition to $Δν$ and $ν_{\mathrm{max}}$. Our findings demonstrate that our machine learning algorithm can accurately infer $Δν$ and $ν_{\mathrm{max}}$ for approximately 50% of samples created by taking one-month Kepler and K2 observations. For TESS one sector data however, we recover reliable $Δν$ for only about 23% of the stars. Additionally, we get reliable $ΔΠ_{1}$ inferences for about 200 young red-giants from K2. For these $ΔΠ_{1}$ inferences, we see a good match with the well known $Δν-ΔΠ_{1}$ degenerate sequence observed in Kepler red-giants.

翻译：星震学是通过研究恒星的共振振荡来推断其内部结构和动力学的一门学科。它也是精确测定恒星参数（如质量、半径、表面重力和年龄）的有力工具。正在进行的TESS任务几乎覆盖了整个天空，为均匀探测银河系中的恒星群体提供了独特的机会。据估计，TESS已观测到超过30万颗振荡的红巨星，其中大多数观测时长为一至两个月。考虑到该数据集的规模，我们需要一种快速、高效且稳健的方法来分析这些数据。在本工作中，我们的目标是开发一种基于机器学习的方法，从短时观测中推断星震学参数。具体来说，我们重点关注从TESS对红巨星的一个月时长观测中提取的两个全局地震参数：大频率间隔（$Δν$）和最大功率频率（$ν_{\mathrm{max}}$）。同时，对于K2数据，我们的重点除了$Δν$和$ν_{\mathrm{max}}$之外，还扩展到推断偶极重力模的周期间距（$ΔΠ_{1}$）。我们的研究结果表明，我们的机器学习算法能够从取自开普勒（Kepler）和K2的一个月观测数据中，准确地推断约50%样本的$Δν$和$ν_{\mathrm{max}}$。然而，对于TESS的单扇区数据，我们仅对约23%的恒星恢复了可靠的$Δν$。此外，我们从K2数据中为约200颗年轻红巨星获得了可靠的$ΔΠ_{1}$推断结果。对于这些$ΔΠ_{1}$推断，我们观察到其与开普勒红巨星中已知的$Δν-ΔΠ_{1}$简并序列吻合良好。