Next generation spectroscopic analysis for large samples of massive stars

TL;DR

This paper introduces a new spectroscopic analysis pipeline designed for large-scale surveys of massive stars, leveraging statistical methods to improve accuracy and efficiency over traditional techniques.

Contribution

The novel pipeline utilizes the entire spectrum and accounts for model errors, enabling homogeneous analysis of thousands of massive star spectra with minimal manual inspection.

Findings

Effective analysis of large stellar samples is achieved.

Inclusion of model errors improves fit accuracy.

The pipeline covers B to early O stars spectrum range.

Abstract

Upcoming large-scale spectroscopic surveys such as WEAVE and 4MOST will provide thousands of spectra of massive stars, which need to be analysed in an efficient and homogeneous way. Studies on massive stars are usually based on samples of a few hundred objects which pushes current spectroscopic analysis tools to their limits because visual inspection is necessary to verify the spectroscopic fit. The novel spectroscopic analysis pipeline takes advantage of the statistics that large samples provide, and determines the model error to account for imperfections in stellar atmosphere codes due to simplified, wrong or missing physics. Considering observational plus model uncertainties improve spectroscopic fits. The pipeline utilises the entire spectrum rather than selected diagnostic lines allowing a wider range of temperature from B to early O stars to be analysed. A small fraction of stars like peculiar, contaminated or spectroscopic binaries require visual inspection, which are identified through their larger uncertainties.