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The neutral hydrogen (HI) intensity mapping (IM) survey is regarded as apromising approach for cosmic large-scale structure (LSS) studies. A majorissue for the HI IM survey is to remove the bright foreground contamination. Akey to successfully remove the bright foreground is to well control oreliminate the instrumental effects. In this work, we consider the instrumentaleffect of polarization leakage and use the U-Net approach, a deeplearning-based foreground removal technique, to eliminate the polarizationleakage effect. The thermal noise is assumed to be a subdominant factorcompared with the polarization leakage for future HI IM surveys and ignored inthis analysis. In this method, the principal component analysis (PCA)foreground subtraction is used as a preprocessing step for the U-Net foregroundsubtraction. Our results show that the additional U-Net processing could eitherremove the foreground residual after the conservative PCA subtraction orcompensate for the signal loss caused by the aggressive PCA preprocessing.Finally, we test the robustness of the U-Net foreground subtraction techniqueand show that it is still reliable in the case of existing constraint error onHI fluctuation amplitude.

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