Foreground removal from WMAP 7yr polarization maps using an MLP neural
  network
  One of the fundamental problems in extracting the cosmic microwave background
signal (CMB) from millimeter/submillimeter observations is the pollution by
emission from the Milky Way: synchrotron, free-free, and thermal dust emission.
To extract the fundamental cosmological parameters from CMB signal, it is
mandatory to minimize this pollution since it will create systematic errors in
the CMB power spectra. In previous investigations, it has been demonstrated
that the neural network method provide high quality CMB maps from temperature
data. Here the analysis is extended to polarization maps. As a concrete
example, the WMAP 7-year polarization data, the most reliable determination of
the polarization properties of the CMB, has been analysed. The analysis has
adopted the frequency maps, noise models, window functions and the foreground
models as provided by the WMAP Team, and no auxiliary data is included. Within
this framework it is demonstrated that the network can extract the CMB
polarization signal with no sign of pollution by the polarized foregrounds. The
errors in the derived polarization power spectra are improved compared to the
errors derived by the WMAP Team.
