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  Stellar Parameters in an Instant with Machine Learning : Application to Kepler LEGACY Targets

Bellinger, E. P., Angelou, G. C., Hekker, S., Basu, S., Ball, W. H., & Guggenberger, E. (2017). Stellar Parameters in an Instant with Machine Learning: Application to Kepler LEGACY Targets. In EPJ Web of Conferences: Seismology of the Sun and the Distant Stars 2016. doi:10.1051/epjconf/201716005003.

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 Creators:
Bellinger, Earl P.1, Author              
Angelou, George C.1, Author              
Hekker, Saskia1, Author              
Basu, Sarbani, Author
Ball, Warrick H., Author
Guggenberger, Elisabet, Author
Affiliations:
1Max Planck Research Group in Stellar Ages and Galactic Evolution (SAGE), Max Planck Institute for Solar System Research, Max Planck Society, ou_2265636              

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 MPIS_GROUPS: Research Group SAGE
 Abstract: 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

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Language(s): eng - English
 Dates: 2018-02-022017
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1051/epjconf/201716005003
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Title: TASC2 & KASC9 Workshop – SPACEINN & HELAS8 Conference
Place of Event: Angra do Heroísmo, Terceira-Açores, Portugal
Start-/End Date: 2016-07-11 - 2016-07-15

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Title: EPJ Web of Conferences : Seismology of the Sun and the Distant Stars 2016
Source Genre: Proceedings
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Pages: - Volume / Issue: 160 Sequence Number: 05003 Start / End Page: - Identifier: -