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arXiv 1906.09609astro-ph.SR
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使用机器学习技术确定开普勒任务观测到的类太阳恒星的表面旋转周期

Determining surface rotation periods of solar-like stars observed by the Kepler mission using machine learning techniques

S. N. Breton, L. Bugnet, A. R. G. Santos, A. Le Saux, S. Mathur, P. L. Palle, R. A. Garcia

摘要:

对于类太阳恒星,表面自转随时间演化,原则上允许从表面自转周期估计恒星的年龄。在这里,我们感兴趣的是测量由美国宇航局开普勒任务观察到的类太阳恒星的表面旋转周期。不同的方法已经发展到跟踪旋转信号的开普勒光度光变曲线:基于小波技术的时频分析,自相关和复合光谱。我们使用随机森林分类器的学习能力,在分析的两个关键步骤中做出决策。首先,给定一些输入参数,我们认为开普勒目标之间的旋转MS星,非旋转MS星,红巨星,双星和双星。然后,我们使用第二个分类器只对MS旋转目标,以决定最佳的数据分析处理。

英文摘要:

For a solar-like star, the surface rotation evolves with time, allowing in principle to estimate the age of a star from its surface rotation period. Here we are interested in measuring surface rotation periods of solar-like stars observed by the NASA Kepler mission. Different methods have been developed to track rotation signals in Kepler photometric light curves: time-frequency analysis based on wavelet techniques, autocorrelation and composite spectrum. We use the learning abilities of random forest classifiers to take decisions during two crucial steps of the analysis. First, given some input parameters, we discriminate the considered Kepler targets between rotating MS stars, non-rotating MS stars, red giants, binaries and pulsators. We then use a second classifier only on the MS rotating targets to decide the best data-analysis treatment.

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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