Bayesian Statistics as a New Tool for Spectral Analysis: I. Application for the Determination of Basic Parameters of Massive Stars

TL;DR

This paper introduces a Bayesian statistical method for spectral analysis of massive stars, enabling simultaneous determination of multiple stellar parameters using all spectral lines, improving accuracy over traditional methods.

Contribution

The paper presents a novel Bayesian approach for spectral analysis that considers all spectral lines simultaneously, reducing errors and uncertainties in stellar parameter estimation.

Findings

Bayesian method provides more consistent stellar parameters.

B-star microturbulence velocities are often underestimated.

Cluster B stars tend to be faster rotators than field B stars.

Abstract

Spectral analysis is a powerful tool to investigate stellar properties and it has been widely used for decades now. However, the methods considered to perform this kind of analysis are mostly based on iteration among a few diagnostic lines to determine the stellar parameters. While these methods are often simple and fast, they can lead to errors and large uncertainties due to the required assumptions. Here we present a method based on Bayesian statistics to find simultaneously the best combination of effective temperature, surface gravity, projected rotational velocity, and microturbulence velocity, using all the available spectral lines. Different tests are discussed to demonstrate the strength of our method, which we apply to 54 mid-resolution spectra of field and cluster B stars obtained at the Observatoire du Mont-M\'egantic. We compare our results with those found in the literature. Differences are seen which are well explained by the different methods used. We conclude that the B-star microturbulence velocities are often underestimated. We also confirm the trend that B stars in clusters are on average faster rotators than field B stars.