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arXiv 2605.08051astro-ph.SRstat.ML
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利用深度学习从短期观测推断恒星振荡参数:应用于TESS和K2红巨星

Period spacings and global seismic parameters for K2 red giants using deep learning

Tata Institute of Fundamental Research(塔塔基础研究所);NYUAD Institute, New York University Abu Dhabi(纽约大学阿布扎比分院);Google Research India(谷歌印度研究院)

Nipun Ghanghas, Siddharth Dhanpal, Shravan Hanasoge, Praneeth Netrapalli, Karthikeyan Shanmugam

AI总结:

本文提出利用深度学习方法从短期观测数据推断红巨星的振荡参数,包括Δν和ν_max,并在K2数据中扩展至推断双极重力模的周期间隔ΔΠ₁,验证了算法在不同数据集中的有效性。

摘要:

恒星振荡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。它也是精确确定恒星参数如质量、半径、表面重力和年龄的强大工具。TESS任务以其几乎完整的天空覆盖,为均匀探测银河系中的恒星群体提供了独特机会。TESS预计已观测到超过300,000个振荡红巨星,大多数观测时间在一个月到两个月。鉴于该数据集的规模,我们需要一种快速、高效且稳健的方法来分析数据。在本工作中,我们的目标是开发一种基于机器学习(ML)的方法,从短期观测中推断恒星振荡参数。具体而言,我们专注于从一个月长的TESS观测数据中推断两个全球振荡参数,即大频率分离(Δν)和功率最大频率(ν_max)。同时,对于K2数据,我们的关注点扩展到推断双极重力模的周期间隔(ΔΠ₁),除了Δν和ν_max。我们的发现表明,我们的机器学习算法可以准确推断约50%的样本的Δν和ν_max,这些样本是由一个月的开普勒和K2观测数据生成的。然而,对于TESS单扇区数据,我们仅能可靠地推断约23%的恒星的Δν。此外,我们从K2中获得约200颗年轻红巨星的可靠ΔΠ₁推断。对于这些ΔΠ₁推断,我们看到与开普勒红巨星中已知的Δν-ΔΠ₁退化序列的良好匹配。

英文摘要:

Gravity-mode period spacings (DPi_1) of red giants probe the stellar core directly, constraining its structure, mass and evolutionary state. Their measurement requires resolving narrow, densely spaced mixed modes and has so far relied on the four-year baseline of Kepler. Recovering DPi_1 from the much shorter (~80-day) baselines typical of K2 remains largely unexplored at the ensemble scale. We develop an automated machine-learning technique to measure global asteroseismic parameters and gravity-mode period spacings for red giants from single-campaign K2 photometry. Two deep residual neural networks take the full-resolution power spectrum of an ~80-day light curve as input, without background fitting or mode identification, and return a probability distribution for each parameter, yielding a point estimate and asymmetric uncertainties. They are trained on ~8 million synthetic spectra and evaluated on held-out synthetics, on Kepler data degraded to K2-like resolution, and on K2 observations. On Kepler data at K2-like resolution, numax and Dnu are recovered for 96% and 91% of stars with robust dispersions of 3.6% and 1.2%; DPi_1 for 22%, with a dispersion of 0.9%. Applied to 18,704 K2 red giants, the inferred numax and Dnu agree with catalogue values to 5.8% and 1.9%, the additional scatter arising from known K2 artefacts. Among the 2,059 young red giants we obtain DPi_1 for 232 stars with a median fractional uncertainty of 1.3%, following the same Dnu-DPi_1 sequence as the Kepler sample although the training set carries no imprint of that relation. We show that numax, Dnu and - for a subset of young red giants - DPi_1 can be recovered from a single ~80-day K2 campaign within an automated, probabilistic machine-learning framework. This approach can also be extended to short-baseline samples from TESS and, in future, Roman and PLATO.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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