Foreground removal from WMAP 7yr polarization maps using an MLP neural network

TL;DR

This paper demonstrates that a neural network approach can effectively remove foreground contamination from WMAP 7-year polarization maps, improving the accuracy of the CMB polarization signal extraction without auxiliary data.

Contribution

The study extends neural network foreground removal techniques to polarization maps, achieving cleaner CMB polarization signals from WMAP data.

Findings

Neural networks successfully extract CMB polarization with minimal foreground contamination.

Errors in polarization power spectra are reduced compared to WMAP Team results.

Method does not require auxiliary data, relying solely on provided WMAP models.

Abstract

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.