# Bayesian hierarchical inference of asteroseismic inclination angles

## TL;DR

This paper introduces a Bayesian hierarchical method to accurately determine stellar inclination angles from asteroseismic data, improving understanding of star system geometries and evolution.

## Contribution

It presents a novel hierarchical Bayesian approach for extracting stellar inclination angles, validated on simulated and real Kepler data, and discusses population bias considerations.

## Key findings

- Successfully applied to artificial data with isotropic inclination distribution.
- Effectively analyzed 123 red giant stars from Kepler data.
- Highlighted importance of accounting for population biases.

## 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 $\textit{Kepler}$. 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.