Modelling stars with Gaussian Process Regression: Augmenting Stellar Model Grid

TL;DR

This paper applies Gaussian Process Regression to interpolate sparse stellar model grids, enabling continuous mapping of stellar parameters and improving the accuracy and precision of stellar age and mass estimations.

Contribution

It introduces a novel application of GP regression to turn sparse stellar model grids into continuous functions, enhancing parameter inference accuracy.

Findings

GP models accurately predict stellar outputs without systematic offsets.

Inferred stellar masses and ages closely match true values within one standard deviation.

GP-based interpolation yields more precise stellar ages than traditional sparse grid methods.

Abstract

Grid-based modelling is widely used for estimating stellar parameters. However, stellar model grid is sparse because of the computational cost. This paper demonstrates an application of a machine-learning algorithm using the Gaussian Process (GP) Regression that turns a sparse model grid onto a continuous function. We train GP models to map five fundamental inputs (mass, equivalent evolutionary phase, initial metallicity, initial helium fraction, and the mixing-length parameter) to observable outputs (effective temperature, surface gravity, radius, surface metallicity, and stellar age). We test the GP predictions for the five outputs using off-grid stellar models and find no obvious systematic offsets, indicating good accuracy in predictions.As a further validation, we apply these GP models to characterise 1,000 fake stars. Inferred masses and ages determined with GP models well recover true values within one standard deviation. An important consequence of using GP-based interpolation is that stellar ages are more precise than those estimated with the original sparse grid because of the full sampling of fundamental inputs.