Bayesian inference of fundamental Be Star parameters by means of spectroscopic observations

Abstract

The research on Be stars - rapid rotating main sequence objects that form a viscous decretion disk - counts on a range of different observational techniques and advanced theoretical tools to investigate the complex phenomena that surrounds these stars. Under the supervision of Professor Alex Carciofi, the BEACON group recently began implementing Markov Chain Monte Carlo (MCMC) techniques in the modeling efforts, using Bayesian inference on spectroscopic data in order to determine the relevant stellar and disk parameters of Be stars. This work revealed previously unknown correlations between parameters and indicated that small uncertainties in the stellar parameters can imply in substantial uncertainties in disk parameters. Therefore, BEACON's research can benefit as a whole if we improve our knowledge of fundamental stellar parameters, which is the main objective of this proposed project. So far, we have only used the spectral energy distribution (SED), mainly in the UV domain, to infer the stellar parameters with the MCMC Bayesian approach. The next step is to include spectral lines in the analysis. To achieve this task, two main steps are necessary: 1) Compute a grid of synthetic line profiles covering the entire range of relevant stellar parameters. 2) Modify our currently used software to include the line profiles in the Bayesian inference. Once the necessary tools are ready, the implementation will be tested using well-known stars, mainly Achernar, as templates for the results. It is expected that the inclusion of the line profile will enhance our ability to extract reliable fundamental stellar parameters from spectra and thus will have a very significant impact in the research done by our group.