Marginal likelihoods of distances and extinctions to stars: computation and compact representation

TL;DR

This paper introduces a method to compute and compactly represent the likelihood of a star's distance and extinction from photometric data, using Gaussian mixture models for efficient analysis.

Contribution

The authors develop a novel approach to marginalize over stellar properties and represent likelihood functions with Gaussian mixtures, enhancing dust mapping accuracy and efficiency.

Findings

Likelihood functions can be accurately modeled with Gaussian mixtures.

Monochromatic extinctions are preferred over bandpass extinctions for dust mapping.

Provided conversion tables for different stellar types.

Abstract

We present a method for obtaining the likelihood function of distance and extinction to a star given its photometry. The other properties of the star (its mass, age, metallicity and so on) are marginalised assuming a simple Galaxy model. We demonstrate that the resulting marginalised likelihood function can be described faithfully and compactly using a Gaussian mixture model. For dust mapping applications we strongly advocate using monochromatic over bandpass extinctions, and provide tables for converting from the former to the latter for different stellar types.