Stellar Parameters in an Instant with Machine Learning - Application to Kepler LEGACY Targets
Stellar Parameters in an Instant with Machine Learning - Application to Kepler LEGACY Targets
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DOI：
10.1051/epjconf/201716005003
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发表时间：
2017-05
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影响因子：
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通讯作者：
E. Bellinger;G. Angelou;S. Hekker;S. Basu;W. Ball;E. Guggenberger
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作者：
E. Bellinger;G. Angelou;S. Hekker;S. Basu;W. Ball;E. Guggenberger
随着专门的光度学太空任务的到来，快速处理巨大恒星星表的能力变得至关重要。Bellinger和Angelou等人。[1]最近提出了一种基于机器学习的新方法来推断具有类太阳振荡的主序星的恒星参数。该方法做出了与其他方法一致的准确预测，但具有能够探索更多参数而几乎不花费时间的优点。在这里，我们将该方法应用于开普勒太空任务所观测到的52颗所谓的“遗留”主序恒星。对于每一颗恒星，我们给出了质量、年龄、半径、光度、核心氢丰度、表面氦丰度、表面重力、初始氦丰度和初始金属丰度的估计和不确定性，以及它们的混合长度、超射系数和扩散倍增因子等演化模型参数的估计。我们得到了恒星年龄、质量和半径的中位数不确定度分别为14.8%、3.6%和1.7%。本手稿中出现的所有分析和所有数字的源代码可在https://github.com/earlbellinger/asteroseismology上以电子方式找到
With the advent of dedicated photometric space missions, the ability to rapidly process huge catalogues of stars has become paramount. Bellinger and Angelou et al. [1] recently introduced a new method based on machine learning for inferring the stellar parameters of main-sequence stars exhibiting solar-like oscillations. The method makes precise predictions that are consistent with other methods, but with the advantages of being able to explore many more parameters while costing practically no time. Here we apply the method to 52 so-called “LEGACY“ main-sequence stars observed by the Kepler space mission. For each star, we present estimates and uncertainties of mass, age, radius, luminosity, core hydrogen abundance, surface helium abundance, surface gravity, initial helium abundance, and initial metallicity as well as estimates of their evolutionary model parameters of mixing length, overshooting coeffcient, and diffusion multiplication factor. We obtain median uncertainties in stellar age, mass, and radius of 14.8%, 3.6%, and 1.7%, respectively. The source code for all analyses and for all figures appearing in this manuscript can be found electronically at https://github.com/earlbellinger/asteroseismology