Multiscale feature integration network for inpainting of full-sky CMB $B$-modes
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| Format: | Preprint |
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2026
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| _version_ | 1866911450141818880 |
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| author | Lambaga, Reyhan D. Sudevan, Vipin Chen, Pisin |
| author_facet | Lambaga, Reyhan D. Sudevan, Vipin Chen, Pisin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_14547 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multiscale feature integration network for inpainting of full-sky CMB $B$-modes Lambaga, Reyhan D. Sudevan, Vipin Chen, Pisin Cosmology and Nongalactic Astrophysics 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. |
| title | Multiscale feature integration network for inpainting of full-sky CMB $B$-modes |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2602.14547 |