Quantitative spectroscopy of single and multiple OB-type stars. Non-LTE spectrum analysis with machine learning

The plethora of spectra of OB-type stars in observatory archives and the much larger numbers to come from the WEAVE and 4MOST spectroscopic facilities require efficient, but also accurate and precise methods for (semi)automatic quantitative analyses. Neural networks were used to emulate the spectra of single- and multi-star systems, trained on hybrid non-local thermodynamic equilibrium (non-LTE) models that cover a wide range of atmospheric parameters and chemical compositions. To derive the full set of stellar atmospheric parameters and uncertainties, a Markov chain Monte Carlo algorithm was implemented to fit high-resolution spectra within 3000Å-10500Å. The neural networks and fitting algorithm were bundled into a programme called Spectral Analysis Tool Using Restricted Neural networks ( Saturn ). In its current implementation, Saturn facilitates the emulation of synthetic spectra for spectral types O7 to B9, which differ only negligibly from computed models. Saturn was tested on a number of benchmark stars that have been studied before, including single OB stars and a detached eclipsing binary (DEB) system. Excellent agreement of atmospheric parameters and elemental abundances for up to ten metal species is found with respect to the data in the literature, often with reduced uncertainties of about 1% in effective temperature and 0.05,dex in surface gravity (1σ values). For DEB components, the uncertainties are larger, in particular for the fainter secondaries when only a single-epoch spectrum is considered, and lower if surface gravities and the effective temperature ratio from previous detailed light curve and orbital dynamics studies are used as additional fit constraints. Uncertainties of elemental abundances are typically $<0.10,dex. Some first applications of Saturn for analyses of new targets are shown to demonstrate its capabilities, such as fast rotators, including HD,149757 (ζ$ Oph). Consistent results are also found at reduced spectral resolutions relevant for observations with WEAVE and 4MOST.

Authors

Institutions

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2026-04-06
DOI
https://doi.org/10.1051/0004-6361/202658852/pdf
Primary Topic
Stellar, planetary, and galactic studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quantitative spectroscopy of single and multiple OB-type stars. Non-LTE spectrum analysis with machine learning

N. Przybilla, P. Aschenbrenner
Springer Link (Chiba Institute of Technology)
Stellar, planetary, and galactic studies
article

Quantitative spectroscopy of single and multiple OB-type stars. Non-LTE spectrum analysis with machine learning

N. Przybilla, P. Aschenbrenner
article en

Abstract

The plethora of spectra of OB-type stars in observatory archives and the much larger numbers to come from the WEAVE and 4MOST spectroscopic facilities require efficient, but also accurate and precise methods for (semi)automatic quantitative analyses. Neural networks were used to emulate the spectra of single- and multi-star systems, trained on hybrid non-local thermodynamic equilibrium (non-LTE) models that cover a wide range of atmospheric parameters and chemical compositions. To derive the full set of stellar atmospheric parameters and uncertainties, a Markov chain Monte Carlo algorithm was implemented to fit high-resolution spectra within 3000Å-10500Å. The neural networks and fitting algorithm were bundled into a programme called Spectral Analysis Tool Using Restricted Neural networks ( Saturn ). In its current implementation, Saturn facilitates the emulation of synthetic spectra for spectral types O7 to B9, which differ only negligibly from computed models. Saturn was tested on a number of benchmark stars that have been studied before, including single OB stars and a detached eclipsing binary (DEB) system. Excellent agreement of atmospheric parameters and elemental abundances for up to ten metal species is found with respect to the data in the literature, often with reduced uncertainties of about 1% in effective temperature and 0.05,dex in surface gravity (1σ values). For DEB components, the uncertainties are larger, in particular for the fainter secondaries when only a single-epoch spectrum is considered, and lower if surface gravities and the effective temperature ratio from previous detailed light curve and orbital dynamics studies are used as additional fit constraints. Uncertainties of elemental abundances are typically $<0.10,dex. Some first applications of Saturn for analyses of new targets are shown to demonstrate its capabilities, such as fast rotators, including HD,149757 (ζ$ Oph). Consistent results are also found at reduced spectral resolutions relevant for observations with WEAVE and 4MOST.

Springer Link (Chiba Institute of Technology)
Universität Innsbruck (AT)
Aarhus Universitet, European Southern Observatory, Universitetet i Oslo, Háskóli Íslands, Turun Yliopisto, Stockholms Universitet, Universität Innsbruck, Instituto de Astrofísica de Andalucía, Canadian Space Agency
Openalex Percentile: Top 8%
Stellar, planetary, and galactic studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.