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  Asteroseismic inference of subgiant evolutionary parameters with deep learning

Hon, M., Bellinger, E. P., Hekker, S., Stello, D., & Kuszlewicz, J. S. (2020). Asteroseismic inference of subgiant evolutionary parameters with deep learning. Monthly Notices of the Royal Astronomical Society, 499(2), 2445-2461. doi:10.1093/mnras/staa2853.

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Genre: Journal Article

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 Creators:
Hon, Marc, Author
Bellinger, Earl P.1, 2, Author              
Hekker, Saskia1, Author              
Stello, Dennis, Author
Kuszlewicz, James S.1, 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              
2IMPRS on Physical Processes in the Solar System and Beyond, Max Planck Institute for Solar System Research, Max Planck Society, ou_1832290              

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Free keywords: asteroseismology, methods: data analysis, stars: evolution, stars: oscillations
 Abstract: With the observations of an unprecedented number of oscillating subgiant stars expected from NASA's TESS mission, the asteroseismic characterization of subgiant stars will be a vital task for stellar population studies and for testing our theories of stellar evolution. To determine the fundamental properties of a large sample of subgiant stars efficiently, we developed a deep learning method that estimates distributions of fundamental parameters like age and mass over a wide range of input physics by learning from a grid of stellar models varied in eight physical parameters. We applied our method to four Kepler subgiant stars and compare our results with previously determined estimates. Our results show good agreement with previous estimates for three of them (KIC 11026764, KIC 10920273, KIC 11395018). With the ability to explore a vast range of stellar parameters, we determine that the remaining star, KIC 10005473, is likely to have an age 1 Gyr younger than its previously determined estimate. Our method also estimates the efficiency of overshooting, undershooting, and microscopic diffusion processes, from which we determined that the parameters governing such processes are generally poorly constrained in subgiant models. We further demonstrate our method's utility for ensemble asteroseismology by characterizing a sample of 30 Kepler subgiant stars, where we find a majority of our age, mass, and radius estimates agree within uncertainties from more computationally expensive grid-based modelling techniques.

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Language(s): eng - English
 Dates: 2020
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISI: 000587762900067
DOI: 10.1093/mnras/staa2853
 Degree: -

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Title: Monthly Notices of the Royal Astronomical Society
  Other : Mon. Not. R. Astron. Soc.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 499 (2) Sequence Number: - Start / End Page: 2445 - 2461 Identifier: ISSN: 1365-8711
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000024150