Joint Bayesian separation and restoration of CMB from convolutional mixtures

Kayabol Koray, Sanz Jose Luis, Herranz Diego, Kuruoglu Ercan Engin, Salerno Emanuele

Bayesian source separation  Astrophysical images  Student t distribution  Langevin sampler 

We propose a Bayesian approach to joint source separation and restoration for astrophysical diff use sources. We constitute a prior statistical model for the source images by using their gradient maps. We assume a t-distribution for the gradient maps in di fferent directions, because it is able to fit both smooth and sparse data. A Monte Carlo technique, called Langevin sampler, is used to estimate the source images and all the model parameters are estimated by using deterministic techniques.