Bayesian hierarchical inference of asteroseismic inclination angles

James S. Kuszlewicz, William J. Chaplin, Thomas S. H. North, Will M. Farr, Keaton J. Bell, Guy R. Davies, Tiago L. Campante, Saskia Hekker

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
133 Downloads (Pure)

Abstract

The stellar inclination angle – the angle between the rotation axis of a star and our line of sight – provides valuable information in many different areas, from the characterization of the geometry of exoplanetary and eclipsing binary systems to the formation and evolution of those systems. We propose a method based on asteroseismology and a Bayesian hierarchical scheme for extracting the inclination angle of a single star. This hierarchical method therefore provides a means to both accurately and robustly extract inclination angles from red giant stars. We successfully apply this technique to an artificial data set with an underlying isotropic inclination angle distribution to verify the method. We also apply this technique to 123 red giant stars observed with Kepler. We also show the need for a selection function to account for possible population-level biases, which are not present in individual star-by-star cases, in order to extend the hierarchical method towards inferring underlying population inclination angle distributions.
Original languageEnglish
Pages (from-to)572–589
Number of pages18
JournalMonthly Notices of the Royal Astronomical Society
Volume488
Issue number1
Early online date19 Jun 2019
DOIs
Publication statusPublished - 1 Sep 2019

Bibliographical note

20 pages, 12 figures, accepted for publication in MNRAS

Keywords

  • astro-ph.SR
  • asteroseismology
  • methods: statistical
  • methods: data analysis

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