EPJ Web Conf.
Volume 160, 2017Seismology of the Sun and the Distant Stars 2016 – Using Today’s Successes to Prepare the Future – TASC2 & KASC9 Workshop – SPACEINN & HELAS8 Conference
|Number of page(s)||5|
|Section||Synergies: Stellar Evolution, Galactic Populations, Binaries and Planets|
|Published online||27 October 2017|
Stellar Parameters in an Instant with Machine Learning
Application to Kepler LEGACY Targets
Max-Planck-Institut für Sonnensystemforschung, Göttingen, Germany
2 Department of Astronomy, Yale University, New Haven, CT, USA
3 Stellar Astrophysics Centre, Department of Physics and Astronomy, Aarhus University, Denmark
4 Institut für Informatik, Georg-August-Universität Göttingen, Germany
5 Institut für Astrophysik, Georg-August-Universität Göttingen, Germany
⋆ e-mail: email@example.com
Published online: 27 October 2017
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.  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
© Owned by the authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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