BESTP -- An Automated Bayesian Modeling Tool for Asteroseismology

TL;DR

BESTP is an automated Bayesian tool that efficiently estimates stellar parameters by matching models to observations, significantly improving precision when detailed oscillation data and Gaia parallax are included.

Contribution

The paper introduces BESTP, a novel automated Bayesian modeling tool that uses nested sampling to accurately determine stellar properties from observational data.

Findings

BESTP successfully estimates stellar parameters for the Sun and HD 222076.

Including individual oscillation frequencies improves parameter precision.

Adding Gaia parallax further enhances age estimation accuracy.

Abstract

Asteroseismic observations are crucial to constrain stellar models with precision. Bayesian Estimation of STellar Parameters (BESTP) is a tool that utilizes Bayesian statistics and nested sampling Monte Carlo algorithm to search for the stellar models that best match a given set of classical and asteroseismic constraints from observations. The computation and evaluation of models are efficiently performed in an automated and a multi-threaded way. To illustrate the capabilities of BESTP, we estimate fundamental stellar properties for the Sun and the red-giant star HD 222076. In both cases, we find models that are consistent with the observations. We also evaluate the improvement in the precision of stellar parameters when the oscillation frequencies of individual modes are included as constraints, compared to the case when only the the large frequency separation is included. For the solar case, the uncertainties of estimated masses, radii and ages are reduced by 0.7%, 0.3% and 8% respectively. For HD 222076, they are reduced even more noticeably by 2%, 0.5% and 4.7%. We also note an improvement of 10% for the age of HD 222076 when the Gaia parallax is included as a constraint compared to the case when only the large separation is included as constraint.