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We present in this paper the PolEMICA (Polarized Expectation-Maximization Independent Component Analysis) algorithm which is an extension to polarization of the SMICA (Spectral Matching Independent Component Analysis) temperature multi-detectors multi-components (MD-MC) component separation method (Delabrouille et al. 2003). This algorithm allows us to estimate blindly in harmonic space multiple physical components from multi-detectors polarized sky maps. Assuming a linear noisy mixture of components we are able to reconstruct jointly the anisotropies electromagnetic spectra of the components for each mode T, E and B, as well as the temperature and polarization spatial power spectra, TT, EE, BB, TE, TB and EB for each of the physical components and for the noise on each of the detectors. PolEMICA is specially developed to estimate the CMB temperature and polarization power spectra from sky observations including both CMB and foreground emissions. This has been tested intensively using as a first approach full sky simulations of the Planck satellite polarized channels for a 14-months nominal mission assuming a simplified linear sky model including CMB, and optionally Galactic synchrotron emission and a Gaussian dust emission. Finally, we have applied our algorithm to more realistic Planck full sky simulations, including synchrotron, realistic dust and free-free emissions.
The polarization modes of the cosmological microwave background are an invaluable source of information for cosmology, and a unique window to probe the energy scale of inflation. Extracting such information from microwave surveys requires disentangli
We present in this paper a new Bayesian semi-blind approach for foreground removal in observations of the 21-cm signal with interferometers. The technique, which we call HIEMICA (HI Expectation-Maximization Independent Component Analysis), is an exte
We combine the latest datasets obtained with different surveys to study the frequency dependence of polarized emission coming from Extragalactic Radio Sources (ERS). We consider data over a very wide frequency range starting from $1.4$ GHz up to $217
We present a study of the effect of component separation on the recovered cosmic microwave background (CMB) temperature distribution, considering Gaussian and non-Gaussian input CMB maps. First, we extract the CMB component from simulated Planck data
We discuss an approach to the component separation of microwave, multi-frequency sky maps as those typically produced from Cosmic Microwave Background (CMB) Anisotropy data sets. The algorithm is based on the two step, parametric, likelihood-based te