Delensing of Cosmic Microwave Background Polarization with Machine Learning
Autor: | Ye-Peng Yan, Guo-Jian Wang, Si-Yu Li, Jun-Qing Xia |
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Jazyk: | angličtina |
Rok vydání: | 2023 |
Předmět: | |
Zdroj: | The Astrophysical Journal Supplement Series, Vol 267, Iss 1, p 2 (2023) |
Druh dokumentu: | article |
ISSN: | 1538-4365 0067-0049 |
DOI: | 10.3847/1538-4365/acd2ce |
Popis: | Primordial B -mode detection is one of the main goals of next-generation cosmic microwave background (CMB) experiments. Primordial B -modes are a unique signature of primordial gravitational waves (PGWs). However, the gravitational interaction of CMB photons with large-scale structures will distort the primordial E modes, adding a lensing B -mode component to the primordial B -mode signal. Removing the lensing effect (“delensing”) from observed CMB polarization maps will be necessary to improve the constraint of PGWs and obtain a primordial E -mode signal. Here, we introduce a deep convolutional neural network model named multi-input multi-output U-net (MIMO-UNet) to perform CMB delensing. The networks are trained on simulated CMB maps with size 20° × 20°. We first use MIMO-UNet to reconstruct the unlensing CMB polarization ( Q and U ) maps from observed CMB maps. The recovered E -mode power spectrum exhibits excellent agreement with the primordial EE power spectrum. The recovery of the primordial B -mode power spectrum for noise levels of 0, 1, and 2 μ K-arcmin is greater than 98% at the angular scale of ℓ < 150. We additionally reconstruct the lensing B map from observed CMB maps. The recovery of the lensing B -mode power spectrum is greater than roughly 99% at the scales of ℓ > 200. We delens the observed B -mode power spectrum by subtracting the reconstructed lensing B -mode spectrum. The recovery of tensor B -mode power spectrum for noise levels of 0, 1, and 2 μ K-arcmin is greater than 98% at the angular scales of ℓ < 120. Even at ℓ = 160, the recovery of tensor B -mode power spectrum is still around 71%. |
Databáze: | Directory of Open Access Journals |
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