Dust modeling in red supergiants Using Bayesian inference

Abstract

Supergiant stars are massive, post-main sequence stars that lose large quantities of mass, forming a dense circumstellar outflow. Since they are cold, with effective temperatures between 3500 and 4500 K, large quantities of dust grains are formed in the ejected matter. The spectral energy distribution (SED) is usually the main source of information about the circumstellar dust in these objects, and carry information of how much UV and visible radiation from the star is reprocessed into the IR domain (the so-called IR excess).The goal of this project is to model the characteristics of these grains and their spatial distribution in the red supergiant VY CMa. This star has a long observational history, and therefore a quite complete SED is available for this study. A novel approach here proposed is to combine the HDUST code that performs the radiative transfer, with a Bayesian inference based on Markov Chain Monte Carlo techniques (MCMC). The Bayesian inference will allow for the many parameters involved in the modelling to be put in a seamless perspective, the relative role of each one on shaping the SED, as well their cross-correlations, apparent from the posterior probabilities provided by the MCMC method. This initial project will likely grow in complexity and scientific interest as the new proposed method is implemented, tested and expanded.