基于机器学习的主序星瞬时基本参数
Fundamental Parameters of Main-Sequence Stars in an Instant with Machine Learning
摘要：
由于像开普勒这样的空间天文台具有非凡的测光精度，我们现在第一次可以详细地描述我们以外的恒星和行星系统。这些特征对于诸如寻找类地行星和太阳双胞胎，理解控制恒星演化的机制以及追踪我们银河系的动力学等工作至关重要。然而，随着数据量的增加，需要准确和迅速地处理这些信息。虽然现有的方法可以限制基本的恒星参数，如年龄，质量和半径从这些观测，他们需要大量的计算工作来做到这一点。我们开发了一种基于机器学习的方法，用于从经典和星震观测中快速估计主序星类太阳恒星的基本参数。我们首先在野兔和猎犬练习中演示了这种方法，然后将其应用于太阳，16 Cyg A & B和34颗开普勒航天器观测到的行星候选者。我们发现，我们的估计及其相关的不确定性是可比的其他方法的结果，但额外的好处是能够探索更多的恒星参数，而使用更少的计算时间。此外，我们使用这种方法来提供证据的经验扩散质量关系。我们的方法是开源的，可供社区免费使用。所有分析的源代码和本手稿中出现的所有图都可以在https://github.com/earlbellinger/asteroseismology上找到
英文摘要：
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 efforts 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 & 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. The source code for all analyses and for all figures appearing in this manuscript can be found electronically at https://github.com/earlbellinger/asteroseismology
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