Eliminating polarization leakage effect for neutral hydrogen intensity mapping with deep learning

The neutral hydrogen (H I) intensity mapping (IM) survey is regarded as a promising approach for cosmic large-scale structure studies. A major issue for the H I IM survey is to remove the bright foreground contamination. A key to successfully removing the bright foreground is to well control or eliminate the instrumental effects. In this work, we consider the instrumental effects of polarization leakage and use the U-Net approach, a deep learning-based foreground removal technique, to eliminate the polarization leakage effect. The thermal noise is assumed to be a subdominant factor compared with the polarization leakage for future H I IM surveys and ignored in this analysis. In this method, the principal component analysis (PCA) foreground subtraction is used as a pre-processing step for the U-Net foreground subtraction. Our results show that the additional U-Net processing could either remove the foreground residual after the conservative PCA subtraction or compensate for the signal loss caused by the aggressive PCA pre-processing. Finally, we test the robustness of the U-Net foreground subtraction technique and show that it is still reliable in the case of existing constraint error on H I fluctuation amplitude.