Bayesian Separation of Independent Sources in Astrophysical Radiation Maps Using MCMC

In this work, we present a novel approach to the recovery of independent sources of radiations in sky maps. The work is motivated by the need to resolve CMB and other specific sources of radiation from a mixture of sources in the observations that are to be made by the Planck satellite after its launch in 2007. In particular, we present a numerical approach for Bayesian estimation namely Markov Chain Monte Carlo (MCMC) that exploits available prior information about radiation sources and hence differs from most of other work in the literature which are generally blind. MCMC provides large flexibility in modelling the problem and avoids analytical difficulties by resorting to numerical techniques. Results demonstrate the success and the flexibility of the approach.