arXivDaily arXiv每日学术速递 周一至周五更新
看起来今天可以划水了
arXiv 2202.07599astro-ph.SRastro-ph.IM
收藏

使用机器学习测量红巨星的频率和周期分离

Measuring frequency and period separations in red-giant stars using machine learning

Siddharth Dhanpal, Othman Benomar, Shravan Hanasoge, Abhisek Kundu, Dattaraj Dhuri, Dipankar Das, Bharat Kaul

摘要:

天文地震学被用来推断恒星的内部物理。\textit{Kepler}和TESS空间飞行任务提供了大量红巨星光变曲线数据集,可用于星震分析。预计这些数据集将随着未来的任务(如\textit{PLATO})而显著增长,因此需要有效的方法来快速分析这些数据。在这里,我们描述了一种机器学习算法,该算法从原始振荡光谱中识别红巨星,并从红巨星功率谱中捕获\textit{p}和\textit{mixed}模式参数。我们报告的算法推断大频率分离($Δν$),频率在最大振幅($ν_{max}$),和周期分离($ΔΠ$)的恒星合奏。此外,我们还在用该方法分析的151,000个$\sim$长节奏恒星振荡光谱中发现了25个新的可能红巨星,其中4个是似乎拥有红巨星对应物的双星候选者。为了验证这种方法的结果,我们选择了$\sim$ 3,000颗\textit{Kepler}恒星,这些恒星处于从亚巨星到红色团块的不同演化阶段,并将$Δν$、$ΔΠ$和$ν_{max}$的推断与使用其他技术获得的估计进行比较。机器学习算法的强大之处在于其速度:它能够在现代计算机(Intel Xeon Platinum 8280 CPU的单核)上在$\sim$ 5秒内从1,000个光谱中准确提取地震参数。

英文摘要:

Asteroseismology is used to infer the interior physics of stars. The \textit{Kepler} and TESS space missions have provided a vast data set of red-giant light curves, which may be used for asteroseismic analysis. These data sets are expected to significantly grow with future missions such as \textit{PLATO}, and efficient methods are therefore required to analyze these data rapidly. Here, we describe a machine learning algorithm that identifies red giants from the raw oscillation spectra and captures \textit{p} and \textit{mixed} mode parameters from the red-giant power spectra. We report algorithmic inferences for large frequency separation ($Δν$), frequency at maximum amplitude ($ν_{max}$), and period separation ($ΔΠ$) for an ensemble of stars. In addition, we have discovered $\sim$25 new probable red giants among 151,000 \textit{Kepler} long-cadence stellar-oscillation spectra analyzed by the method, among which four are binary candidates which appear to possess red-giant counterparts. To validate the results of this method, we selected $\sim$ 3,000 \textit{Kepler} stars, at various evolutionary stages ranging from subgiants to red clumps, and compare inferences of $Δν$, $ΔΠ$, and $ν_{max}$ with estimates obtained using other techniques. The power of the machine-learning algorithm lies in its speed: it is able to accurately extract seismic parameters from 1,000 spectra in $\sim$5 seconds on a modern computer (single core of the Intel Xeon Platinum 8280 CPU).

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。

↑