A functional approximation to implement Bayesian source separation analysis is introduced and applied to separation of the Cosmic Microwave Background (CMB) using WMAP data. The approximation allows for tractable full-sky map reconstructions at the scale of both WMAP and Planck data and models the spatial smoothness of sources through a Gaussian Markov random field prior. It is orders of magnitude faster than the usual MCMC approaches. The performance and limitations of the approximation are also discussed.
@article{arxiv.1011.4018,
title = {Bayesian ICA-based source separation of Cosmic Microwave Background by a discrete functional approximation},
author = {Simon P. Wilson and Jiwon Yoon},
journal= {arXiv preprint arXiv:1011.4018},
year = {2015}
}