FUNDAMENTAL PARAMETERS OF MAIN-SEQUENCE STARS IN AN INSTANT WITH MACHINE LEARNING
FUNDAMENTAL PARAMETERS OF MAIN-SEQUENCE STARS IN AN INSTANT WITH MACHINE LEARNING
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DOI：
10.3847/0004-637x/830/1/31
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发表时间：
2016-10-10
影响因子：
4.9
通讯作者：
Guggenberger, Elisabeth
中科院分区：
文献类型：
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作者：
Bellinger, Earl P.;Angelou, George C.;Guggenberger, Elisabeth
由于开普勒等太空天文台具有卓越的光度测量精度，我们首次对我们之外的恒星和行星系统进行了大规模表征。这些特征对于寻找类地行星和太阳双胞胎、了解控制恒星演化的机制以及追踪银河系的动态等工作至关重要。然而，随着可用数据量的增加，需要准确、快速地处理这些信息。虽然现有方法可以从这些观测中限制基本的恒星参数，例如年龄、质量和半径，但它们需要大量的计算工作才能做到这一点。我们开发了一种基于机器学习的方法，用于根据经典和星震观测快速估计主序类太阳恒星的基本参数。我们首先在野兔和猎犬演习中演示了这种方法，然后将其应用于太阳、16 Cyg A 和 B 以及开普勒航天器观测到的 34 颗候选行星。我们发现我们的估计及其相关的不确定性与其他方法的结果相当，但具有能够在使用更少的计算时间的同时探索更多恒星参数的额外好处。我们还使用这种方法来提供经验扩散质量关系的证据。我们的方法是开源的，可供社区免费使用。
Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such as searching for Earth-like planets and solar twins, understanding the mechanisms that govern stellar evolution, and tracing the dynamics of our Galaxy. The volume of data that is becoming available, however, brings with it the need to process this information accurately and rapidly. While existing methods can constrain fundamental stellar parameters such as ages, masses, and radii from these observations, they require substantial computational effort to do so. We develop a method based on machine learning for rapidly estimating fundamental parameters of main-sequence solar-like stars from classical and asteroseismic observations. We first demonstrate this method on a hare-and-hound exercise and then apply it to the Sun, 16 Cyg A and B, and 34 planet-hosting candidates that have been observed by the Kepler spacecraft. We find that our estimates and their associated uncertainties are comparable to the results of other methods, but with the additional benefit of being able to explore many more stellar parameters while using much less computation time. We furthermore use this method to present evidence for an empirical diffusion-mass relation. Our method is open source and freely available for the community to use.