Reconstruction of full sky CMB $\bf{E}$ and $\bf{B}$ modes spectra removing $\bf{E}$-to-$\bf{B}$ leakage from partial sky using deep learning

TL;DR

This paper introduces a deep learning method using CNNs to accurately reconstruct full sky CMB $E$ and $B$ modes spectra from partial sky data, effectively removing leakage and preserving statistical properties.

Contribution

The paper presents a novel CNN-based approach to eliminate $E$-to-$B$ leakage in partial sky CMB polarization analysis, improving reconstruction accuracy.

Findings

CNN accurately predicts full sky spectra from partial data

Method preserves cosmic variance and statistical properties

Effective for masks covering 80% and 10% of the sky

Abstract

Incomplete sky analysis of cosmic microwave background (CMB) polarization spectra poses a major problem of leakage between $E$- and $B$-modes. We present a machine learning approach to remove this $E$-to-$B$ leakage using a convolutional neural network (CNN) in presence of detector noise. The CNN predicts the full sky $E$- and $B$-modes spectra for multipoles $2 \leq \ell \leq 384$ from the partial sky spectra for $N_{\rm{side}} = 256$. We use tensor-to-scalar ratio $r=0.001$ to simulate the CMB polarization maps. We train our CNN using $10^5$ full sky target spectra and an equal number of noise contaminated partial sky spectra obtained from the simulated maps. The CNN works well for two masks covering the sky area of $\sim 80\%$ and $\sim 10\%$ respectively after training separately for each mask. For the assumed theoretical $E$- and $B$-modes spectra, predicted full sky $E$- and $B$-modes spectra agree well with the corresponding target spectra and their means agree with theoretical spectra. The CNN preserves the cosmic variances at each multipole, effectively removes correlations of the partial sky $E$- and $B$-modes spectra, and retains the entire statistical properties of the targets avoiding the problem of so-called $E$-to-$B$ leakage for the chosen theoretical model.