Bayesian hierarchical inference of asteroseismic inclination angles

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 characterisation 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 dataset with an underlying isotropic inclination angle distribution to verify the method. We also apply this technique to 123 red giant stars observed with $\text{Kepler}$. We also show the need for a selection function to account for possible population-level biases, that are not present in individual star-by-star cases, in order to extend the hierarchical method towards inferring underlying population inclination angle distributions.

20 pages, 12 figures, accepted for publication in MNRAS

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Cited by in corpus (5)

• On the Inference of a Star's Inclination Angle from its Rotation Velocity and Projected Rotation Velocity
• Fast and Automated Peak Bagging with DIAMONDS (FAMED)
• Hierarchically modelling Kepler dwarfs and subgiants to improve inference of stellar properties with asteroseismology
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• Automated approach to measure stellar inclinations: validation through large-scale measurements on the red giant branch