3D Extinction Mapping Using Hierarchical Bayesian Models

TL;DR

This paper introduces a hierarchical Bayesian approach for 3D extinction mapping that improves accuracy and precision by leveraging the hierarchical structure of the Galaxy and stars, adaptable to various observational data.

Contribution

It presents a novel hierarchical Bayesian model for 3D extinction mapping that enhances precision and can be applied to diverse datasets and Galactic studies.

Findings

Improved accuracy over previous methods

Model is adaptable to different surveys

Potential to study Galactic structure and star formation

Abstract

The Galaxy and the stars in it form a hierarchical system, such that the properties of individual stars are influenced by those of the Galaxy. Here, an approach is described which uses hierarchical Bayesian models to simultaneously and empirically determine the mean distance-extinction relationship for a sightline and the properties of stars which populate it. By exploiting the hierarchical nature of the problem, the method described is able to achieve significantly improved precision and accuracy with respect to previous 3D extinction mapping techniques. This method is not tied to any individual survey and could be applied to any observations, or combination of observations available. Furthermore, it is extendible and, in addition, could be employed to study Galactic structure as well as factors such as the initial mass function and star formation history in the Galaxy.