Polarized cosmic microwave background map recovery with sparse component separation
Laboratoire CosmoStat, AIM, UMR CEA-CNRS-Paris 7, Irfu, SAp/SEDI, Service
d’Astrophysique, CEA Saclay,
Received: 2 March 2015
Accepted: 29 July 2015
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 this information from microwave surveys requires distinguishing between foreground emissions and the cosmological signal, which means solving a component separation problem. Component separation techniques have been widely studied for the recovery of cosmic microwave background (CMB) temperature anisotropies, but very rarely for the polarization modes. In this case, most component separation techniques make use of second-order statistics to distinguish between the various components. More recent methods, which instead emphasize the sparsity of the components in the wavelet domain, have been shown to provide low-foreground, full-sky estimates of the CMB temperature anisotropies. Building on sparsity, we here introduce a new component separation technique dubbed the polarized generalized morphological component analysis (PolGMCA), which refines previous work to specifically work on the estimation of the polarized CMB maps: i) it benefits from a recently introduced sparsity-based mechanism to cope with partially correlated components; ii) it builds upon estimator aggregation techniques to further yield a better noise contamination/non-Gaussian foreground residual trade-off. The PolGMCA algorithm is evaluated on simulations of full-sky polarized microwave sky simulations using the Planck Sky Model (PSM). The simulations show that the proposed method achieves a precise recovery of the CMB map in polarization with low-noise and foreground contamination residuals. It provides improvements over standard methods, especially on the Galactic center, where estimating the CMB is challenging.
Key words: methods: data analysis / cosmic background radiation / methods: statistical
© ESO, 2015