2026A&A...707A.249U

Context. Quantitative spectroscopy of luminous blue stars relies on detailed non-local thermodynamic equilibrium (non-LTE) model atmospheres whose increasing physical realism makes direct iterative analyses computationally demanding.
Aims. We introduce Machine-learning assisted Uncertainty inference (MAUI), a statistical framework designed for an 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 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 and subgiant stars.
Results. The emulator reproduces the predictions of full atmosphere models within the quoted uncertainties while reducing computational cost by several orders of magnitude. Posterior distributions are well calibrated with a conservative coverage across all stellar parameters.
Conclusions. The emulator-driven Bayesian inference retains the accuracy of classical analyses at a fraction of the computational expense, which enables 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.