Foreground masking and incomplete sky coverage complicate CMB polarization analyses by inducing mode coupling and imperfect E/B separation, with particularly strong impact on searches for primordial $B$-modes. We present SkyReconNet-P, a convolutional neural network for inpainting CMB polarization maps that extends the SkyReconNet framework to jointly reconstruct the polarization $(Q,U)$ maps from partial-sky observations. The method combines regional processing with a hybrid design, utilizing standard convolution and dilated convolution to do a multiscale feature integration. We evaluate performance at both the map and power spectrum level using two masking scenarios: a generated random mask and the Planck 2018 common polarization inpainting mask. For both masking scenarios, SkyReconNet-P reproduces the large-scale morphology of the target maps. In power-spectrum space, we find that the reconstructed $E$-mode spectrum closely tracks the target at low multipoles, while small biases emerge at higher $\ell$. For $B$-mode, the raw reconstructed spectra exhibit a larger multipole-dependent bias, which we mitigate using a simulation-based linear calibration. We show that the calibrated $B$-mode spectrum preserve more information by comparing it with spectrum estimation using pseudo-$C_\ell$. Finally, we demonstrate cosmological parameter inference from calibrated reconstructed spectra by fitting $(r, A_{\rm lens})$ with a Gaussian bandpower likelihood, recovering posteriors consistent with injected parameters across three test ensembles down to $r \sim 10^{-3}$. These results support inpainting as a complementary route to cut-sky approaches when downstream pipelines can greatly benefit from statistically well-characterized, gap-filled polarization maps.
Inpainting via Generative Adversarial Networks for CMB data analysis
Institute for Research in Fundamental Sciences (IPM)（基础科学研究所（IPM））；Scuola Internazionale Superiore di Studi Avanzati (SISSA)（国际高等研究院（SISSA））；Institute for Fundamental Physics of the Universe (IFPU)（宇宙基础物理研究所（IFPU））
Mitigating point-source contamination in CMB polarization: a Generalized Point Spread Function fitting approach
Key Laboratory of Particle Astrophysics, Institute of High Energy Physics, Chinese Academy of Sciences（高能物理研究所粒子天体物理重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
