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

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 3000A-10500A. 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. For DEB components, the uncertainties are larger, in particular for the fainter secondaries when only a single-epoch spectrum is considered. Uncertainties of elemental abundances are typically <0.10dex. Some first applications of SATURN for analyses of new targets are shown to demonstrate its capabilities, such as fast rotators, including HD149757 (Zeta Ophiuchi). Consistent results are also found at reduced spectral resolutions relevant for observations with WEAVE and 4MOST.

Figures (9)

• Figure 1: Mean squared error as a function of training epoch. The solid line is the MSE for the training data and the dotted line for the test data. The dashed vertical lines mark the epochs where the learning rate was reduced.
• Figure 2: Spectral energy distribution fits of the reddened combined Atlas9 model fluxes (red) to photometric measurements (black squares) and observed IUE spectrophotometry and Gaia XP spectra Gaia2023 (black line), if available. The contributions of the primary and secondary are shown in blue and magenta, respectively. Top panel: SED fit for HD 259135 (V578 Mon); bottom panel: SED fit for HD 77464 (CV Vel).
• Figure 3: Comparison between the spectrum of the benchmark DEB HD 259135 (V578 Mon) in black and the global best-fitting model in red. The difference between observed and model flux is shown in grey at an offset of $1.05$. The flux contributions of the primary and secondary star are shown in blue and magenta, respectively. The strongest spectral lines for the primary and secondary star are marked via solid and dotted lines, respectively. The DIBs are marked by dashed lines.
• Figure 4: Comparison between the spectrum of the very fast rotator HD 149757 ($\zeta$ Oph) in black and the global best-fitting model in red. The difference between observed and model flux is shown in grey at an offset of $1.05$. The strongest spectral lines are marked by solid lines, DIBs are marked by dashed lines.
• Figure 5: Comparison between the spectrum of the fast-rotating bright giant/supergiant HD 93827 in black and the global best-fitting model in red. The difference between observed and model flux is shown in grey at an offset of $1.05$. The strongest spectral lines are marked via solid lines. The DIBs are indicated by dashed lines.