Benchmarking Machine Learning Emulators of Stellar Evolution for Precision Asteroseismology
Fast and accurate stellar evolution emulators---surrogate models that approximate expensive simulation outputs with machine learning (ML)---are powerful tools for modern stellar characterization, hierarchical inference, and population synthesis. We analyze the grid density required for reliable emulation by training ML algorithms on main-sequence models with masses M=[0.7,1.2] solar masses. This range is challenging to emulate due to rapidly varying evolutionary behavior caused by the radiative-…