paper

Delensing of Cosmic Microwave Background Polarization with machine learning

arXiv:2305.02490 · doi:10.3847/1538-4365/acd2ce

Abstract

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 . We first use MIMO-UNet to reconstruct the unlensing CMB polarization ( and ) 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 K-arcmin is greater than 98\% at the angular scale of . 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 . We delens observed B-mode power spectrum by subtracting reconstructed lensing B-mode spectrum. The recovery of tensor B-mode power spectrum for noise levels of 0, 1, 2 K-arcmin is greater than 98 \% at the angular scales of . Even at , the recovery of tensor B-mode power spectrum is still around 71 \%.

18 pages, 14 figures, 1 table, accepted by ApJS

References in corpus (6)

Cited by in corpus (2)

Delensing of Cosmic Microwave Background Polarization with machine learning · wovepaper