A statistical framework for the quantitative spectroscopy of luminous blue stars

M. A. Urbaneja

doi:10.1051/0004-6361/202558001/pdf

February 9, 2026

A statistical framework for the quantitative spectroscopy of luminous blue stars

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Key Points

The central aim is to develop an efficient statistical framework for the Bayesian inference of stellar parameters from spectral data of luminous blue stars.
Introduced machine-learning assisted uncertainty inference framework.
Utilized Gaussian-process-based emulators trained on limited non-LTE simulations.
Applied Markov chain Monte Carlo sampling for posterior distribution exploration.
Conducted validation through recovery experiments on various stellar types.
Emulator reproduces atmosphere model predictions within specified uncertainties.
Reduced computational costs by several orders of magnitude compared to direct analyses.
Achieved well-calibrated posterior distributions across all stellar parameters.

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Implication

Framework employs machine learning to improve Bayesian inference of stellar parameters in luminous blue stars, indicating significant computational efficiency gains.

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M. A. Urbaneja (2026) studied this question.

synapsesocial.com/papers/699405774e9c9e835dfd663a https://doi.org/10.1051/0004-6361/202558001/pdf

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A statistical framework for the quantitative spectroscopy of luminous blue stars

Key Points

No abstract yet

Implication

Discussion

Cite This Study

A statistical framework for the quantitative spectroscopy of luminous blue stars

Key Points

No abstract yet

Implication

Discussion

Cite This Study

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