LACHESIS: Robust stellar parameters through Bayesian model averaging of isochrone grids

Abstract

Accurate stellar parameters underpin much of astrophysics, from exoplanetary systems to Galactic archaeology. For isolated field stars, masses and especially ages are usually inferred from stellar evolution models, yet these inferences depend on the chosen model grid, and the systematic differences between grids can rival the statistical uncertainties of any single pipeline. We present LACHESIS, a Python package that determines stellar masses, radii, and ages from photometry and spectroscopy while explicitly accounting for the systematic uncertainty from the choice of model grid. We interpolate five independent isochrone grids in [Fe/H], log age, and equivalent evolutionary phase conditioned on the data, sample each with nested sampling, and combine the per-grid posteriors by Bayesian model averaging, weighting each grid by its Bayesian evidence. We validate LACHESIS against a benchmark of Kepler asteroseismic dwarfs and subgiants. Masses are recovered with a robust scatter of ~4% and radii to ~2%, with negligible radius bias (<1%) and a small ~3% mass systematic. Age is recovered with a robust scatter of 30% that depends strongly on evolutionary state; the grid-to-grid systematic contributes a ~9% floor, a subdominant but real term that a single-grid fit omits. Model averaging is better calibrated than selecting a single grid: its credible intervals approach nominal coverage once the reference uncertainty is included, and cover near the nominal rate in injection tests even when the generating model lies outside the ensemble. By marginalizing over an ensemble of stellar model grids, LACHESIS folds the choice-of-grid systematic directly into the posterior, so the reported uncertainties no longer reflect only within-grid data noise. This delivers homogeneous, well-characterized masses, radii, and ages for exoplanet hosts and stellar population studies.