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-to-convective core transition, as well as the requirement to match the part-per-thousand seismic precision that has been delivered for such stars from the NASA Kepler mission. Generating grids from analytical models, as well as MESA, YREC, MIST, and ASTEC, we compare linear interpolation, k-nearest neighbors, random forests, and neural networks (NNs) in interpolating the stellar observables: T_eff, L, Delta nu, and nu_max. While NNs outperform other methods, sparse grids induce localized failures in the core-transition region, resulting in unstable derivatives, ensemble disagreement, and fragmented posterior distributions during inference. Performance gains from denser grids are non-uniform, suggesting that adaptive grid generation should be favored over uniform refinement. Finally, we show that NN ensembles allow for localized uncertainty propagation, more accurately reflecting emulator reliability across parameter space than global uncertainty estimates. As we consider only the two-dimensional case of varying only stellar mass and age along the main sequence, these results represent a lower bound on the challenge in emulating stellar evolution simulations for precision asteroseismology.