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

作     者：Gao, Li-Yang Li, Yichao Ni, Shulei Zhang, Xin 

作者机构： College of Sciences Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Polarization 

摘      要：The neutral hydrogen (HI) intensity mapping (IM) survey is regarded as a promising approach for cosmic large-scale structure studies. A major issue for the HI 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 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. Copyright © 2022, The Authors. All rights reserved.