Joint Bayesian separation and restoration of CMB from convolutional mixtures
Instrumentation and Methods for Astrophysics
2015-05-20 v1 Cosmology and Nongalactic Astrophysics
Abstract
We propose a Bayesian approach to joint source separation and restoration for astrophysical diffuse 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 different 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.
Keywords
Cite
@article{arxiv.1101.1397,
title = {Joint Bayesian separation and restoration of CMB from convolutional mixtures},
author = {K. Kayabol and J. L. Sanz and D. Herranz and E. E. Kuruoglu and E. Salerno},
journal= {arXiv preprint arXiv:1101.1397},
year = {2015}
}
Comments
11 pages, 6 figures. Submitted to MNRAS