BESTP - An automated Bayesian modeling tool for asteroseismology

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 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 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 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% respectively. 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 a constraint.

Automated summary

Bayesian Estimation of STellar Parameters (BESTP) is a tool that utilizes Bayesian statistics and Monte Carlo algorithm to search for stellar models that best match observational constraints. It efficiently improves the precision of stellar parameter estimation and reduces uncertainties of important properties such as mass, radius, and age. The tool demonstrates applications for various types of stars and shows significant improvements, especially when considering individual oscillation frequencies as constraints.