Astrophysical component separation with Langevin Sampler

We propose a new Monte Carlo method for the astrophysical image separation problem. In this Bayesian simulation context, we used Langevin stochastic equation to generate the samples instead of the conventional random walk model. Since the samples are produced in parallel and tested pixel-by-pixel in the Metropolis-Hasting scheme, there is a significant gain in the processing time at the cost of a modest decrease in performance. An additional advantage of our method is the on-line estimation of the Markov Random Fields (MRF) model parameters.