A statistical framework for quantitative spectroscopy of luminous blue stars

TL;DR

This paper introduces MAUI, a Bayesian inference framework using Gaussian-process emulators to efficiently analyze stellar spectra, significantly reducing computational costs while maintaining accuracy for luminous blue stars.

Contribution

The paper presents a novel emulator-based Bayesian method for stellar spectroscopy that drastically improves efficiency without sacrificing model fidelity.

Findings

Emulators reproduce full atmosphere model predictions within uncertainties.

The approach reduces computational costs by orders of magnitude.

Posterior distributions are well calibrated and conservative.

Abstract

Context: Quantitative spectroscopy of luminous blue stars relies on detailed non-LTE model atmospheres whose increasing physical realism makes direct, iterative analyses computationally demanding. Aims: We introduce MAUI (Machine-learning Assisted Uncertainty Inference), a statistical framework designed for efficient Bayesian inference of stellar parameters using emulator-based spectral models. Methods: MAUI employs Gaussian-process-based emulators trained on a limited set of non-LTE simulations, combined with Markov Chain Monte Carlo (MCMC) sampling to explore posterior distributions. We validate the approach with recovery experiments and demonstrate it on Galactic late-type O dwarf and early-type B dwarf/subgiant stars. Results: The emulator reproduces the predictions of full atmosphere models within quoted uncertainties while reducing computational cost by orders of magnitude. Posterior distributions are well calibrated, with conservative coverage across all stellar parameters. Conclusions: Emulator-driven Bayesian inference retains the accuracy of classical analyses at a fraction of the computational expense, enabling posterior sampling that would be prohibitive with direct model evaluations. This positions emulators as a practical tool for high-fidelity spectroscopy of massive stars as atmosphere models grow more demanding.