Name: Inverse Problems in Asteroseismology
Creator: Bellinger, Earl Patrick
Keywords: FOS: Computer and information sciences, Computer Science - Machine Learning, machine learning, Astrophysics - Solar and Stellar Astrophysics, 0103 physical sciences, FOS: Physical sciences, asteroseismology, 01 natural sciences, Solar and Stellar Astrophysics (astro-ph.SR), Machine Learning (cs.LG)

descriptionPublicationkeyboard_double_arrow_right Doctoral thesis , Thesis , Article , Preprint 21 Feb 2022Embargo end date: 01 Jan 2018Publisher:University Goettingen RepositoryFunded by:EC | STELLARAGES

Authors: Bellinger, Earl Patrick;

doi: 10.53846/goediss-6957 , 10.5281/zenodo.2390979 , 10.5281/zenodo.2390978 , 10.48550/arxiv.1808.06649

arXiv: http://arxiv.org/abs/1808.06649

Inverse Problems in Asteroseismology

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Abstract

Asteroseismology allows us to probe the internal structure of stars through their global modes of oscillation. Thanks to missions such as the NASA Kepler space observatory, we now have high-quality asteroseismic data for nearly 100 solar-type stars. In this thesis, new techniques to measure the ages, masses, and radii of stars are presented, as well as a way to infer their internal structure.

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Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, machine learning, Astrophysics - Solar and Stellar Astrophysics, FOS: Physical sciences, asteroseismology, Solar and Stellar Astrophysics (astro-ph.SR), Machine Learning (cs.LG)

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