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

TL;DR

This paper introduces a deep learning approach using U-Net to effectively remove polarization leakage effects in neutral hydrogen intensity mapping, improving foreground subtraction and signal recovery for large-scale structure studies.

Contribution

The study demonstrates that a U-Net deep learning model can enhance foreground removal in HI intensity mapping by addressing polarization leakage, outperforming traditional PCA methods.

Findings

U-Net effectively reduces polarization leakage residuals.

U-Net compensates for signal loss from PCA preprocessing.

Method remains robust under constraint errors on HI fluctuations.

Abstract

The neutral hydrogen (HI) intensity mapping (IM) survey is regarded as a promising approach for cosmic large-scale structure (LSS) studies. A major issue for the HI IM survey is to remove the bright foreground contamination. A key to successfully remove the bright foreground is to well control or eliminate the instrumental effects. In this work, we consider the instrumental effect 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 HI IM surveys and ignored in this analysis. In this method, the principal component analysis (PCA) foreground subtraction is used as a preprocessing 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 preprocessing. 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 HI fluctuation amplitude.