CMB delensing with deep learning

TL;DR

This paper demonstrates that deep learning, specifically the U-Net++ architecture, can effectively delens CMB maps, restoring the unlensed power spectrum and improving the accuracy of cosmological parameter estimation.

Contribution

The study introduces a novel application of U-Net++ deep learning for CMB delensing, showing it can closely recover the unlensed power spectrum from lensed data.

Findings

U-Net++ effectively reduces lensing effects in CMB maps.

The delensed power spectra closely match the unlensed spectra.

Deep learning enhances CMB analysis for future experiments.

Abstract

The cosmic microwave background (CMB) stands as a pivotal source for studying weak gravitational lensing. While the lensed CMB aids in constraining cosmological parameters, it simultaneously smooths the original CMB's features. The angular power spectrum of the unlensed CMB showcases sharper acoustic peaks and more pronounced damping tails, enhancing the precision of inferring cosmological parameters that influence these aspects. Although delensing diminishes the $B$-mode power spectrum, it facilitates the pursuit of primordial gravitational waves and enables a lower variance reconstruction of lensing and additional sources of secondary CMB anisotropies. In this work, we explore the potential of deep learning techniques, specifically the U-Net++ algorithm, to play a pivotal role in CMB delensing. We analyze three fields, namely $T$, $Q$, and $U$ sky maps, present the angular power spectra of the CMB delensed $TT$, $EE$, $BB$, and $TE$, and compare them with the unlensed CMB angular power spectra. Our findings reveal that the angular power spectrum of the lensed CMB, processed by U-Net++, closely aligns with that of the unlensed CMB. Thus, U-Net++ based CMB delensing proves to be effective in mitigating the impacts of weak gravitational lensing, paving the way for enhancing the CMB delensing power spectrum in forthcoming CMB experiments. The code utilized for this analysis is available on GitHub.