A statistical framework for quantitative spectroscopy of luminous blue stars
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.