Correlated Component Analysis for diffuse component separation with
  error estimation on simulated Planck polarization data
  We present a data analysis pipeline for CMB polarization experiments, running
from multi-frequency maps to the power spectra. We focus mainly on component
separation and, for the first time, we work out the covariance matrix
accounting for errors associated to the separation itself. This allows us to
propagate such errors and evaluate their contributions to the uncertainties on
the final products.The pipeline is optimized for intermediate and small scales,
but could be easily extended to lower multipoles. We exploit realistic
simulations of the sky, tailored for the Planck mission. The component
separation is achieved by exploiting the Correlated Component Analysis in the
harmonic domain, that we demonstrate to be superior to the real-space
application (Bonaldi et al. 2006). We present two techniques to estimate the
uncertainties on the spectral parameters of the separated components. The
component separation errors are then propagated by means of Monte Carlo
simulations to obtain the corresponding contributions to uncertainties on the
component maps and on the CMB power spectra. For the Planck polarization case
they are found to be subdominant compared to noise.
