Delensing of Cosmic Microwave Background Polarization with Machine Learning

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 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 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%.

Keywords

• Cosmic microwave background radiation
• Observational cosmology
• Convolutional neural networks