Modelling the solar twin 18 Sco

TL;DR

This study uses Bayesian modeling and asteroseismic data to accurately estimate the physical properties of the solar twin 18 Sco, highlighting the importance of seismic data in constraining stellar parameters.

Contribution

It introduces a Bayesian approach with Gaussian mixture models for seismic likelihoods to improve stellar parameter estimation for solar twins.

Findings

Mass estimated with 6% precision

Age bimodal distribution with modes at 4.67 and 6.95 Gyr

Seismic data significantly improve parameter constraints

Abstract

Solar twins are objects of great interest in that they allow us to understand better how stellar evolution and structure are affected by variations of the stellar mass, age and chemical composition in the vicinity of the commonly accepted solar values. We aim to use the existing spectrophotometric, interferometric and asteroseismic data for the solar twin 18 Sco to constrain stellar evolution models. 18 Sco is the brightest solar twin and is a good benchmark for the study of solar twins. The goal is to obtain realistic estimates of its physical characteristics (mass, age, initial chemical composition, mixing-length parameter) and realistic associated uncertainties using stellar models. We set up a Bayesian model that relates the statistical properties of the data to the probability density of the stellar parameters. Special care is given to the modelling of the likelihood for the seismic data, using Gaussian mixture models. The probability densities of the stellar parameters are approximated numerically using an adaptive MCMC algorithm. From these approximate distributions we proceeded to a statistical analysis. We also performed the same exercise using local optimisation. The precision on the mass is approximately 6%. The precision reached on X0 and Z0 and the mixing-length parameter are respectively 6%, 9%, and 35%. The posterior density for the age is bimodal, with modes at 4.67 Gyr and 6.95 Gyr, the first one being slightly more likely. We show that this bimodality is directly related to the structure of the seismic data. When asteroseismic data or interferometric data are excluded, we find significant losses of precision for the mass and the initial hydrogen-mass fraction. Our final estimates of the uncertainties from the Bayesian analysis are significantly larger than values inferred from local optimization.