Adaptive Langevin Sampler for Separation of t-Distribution Modelled Astrophysical Maps

TL;DR

This paper introduces an adaptive Langevin sampler that models astrophysical maps with Student's t-distribution priors, significantly improving the efficiency of source separation through a novel MCMC scheme.

Contribution

It presents a new Bayesian source separation method using an efficient Langevin-based MCMC with adaptive t-distribution priors for astrophysical maps.

Findings

Reduces computation time by two orders of magnitude.

Effective separation of astrophysical sources demonstrated.

Adaptive t-distribution parameters improve modeling accuracy.

Abstract

We propose to model the image differentials of astrophysical source maps by Student's t-distribution and to use them in the Bayesian source separation method as priors. We introduce an efficient Markov Chain Monte Carlo (MCMC) sampling scheme to unmix the astrophysical sources and describe the derivation details. In this scheme, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and reduces the computation time significantly (by two orders of magnitude). In addition, Student's t-distribution parameters are updated throughout the iterations. The results on astrophysical source separation are assessed with two performance criteria defined in the pixel and the frequency domains.