Bayesian CMB foreground separation with a correlated log-normal model
  The extraction of foreground and CMB maps from multi-frequency observations
relies mostly on the different frequency behavior of the different components.
Existing Bayesian methods additionally make use of a Gaussian prior for the CMB
whose correlation structure is described by an unknown angular power spectrum.
We argue for the natural extension of this by using non-trivial priors also for
the foreground components. Focusing on diffuse Galactic foregrounds, we propose
a log-normal model including unknown spatial correlations within each component
and cross-correlations between the different foreground components. We present
case studies at low resolution that demonstrate the superior performance of
this model when compared to an analysis with flat priors for all components.
