# Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach

## TL;DR

This paper presents a GAN-based U-Net approach for accurate CMB map reconstruction, effectively removing foregrounds and instrumental effects using realistic simulations, achieving high fidelity even in challenging regions.

## Contribution

The study introduces a novel GAN architecture tailored for CMB reconstruction that corrects for foregrounds and systematics, demonstrating superior performance over traditional methods.

## Key findings

- Achieves less than 1% difference between input and recovered maps outside the Galactic region.
- Effectively corrects for foreground contamination and instrumental systematics.
- Maintains low reconstruction error within the Galactic plane for temperature and polarization maps.

## Abstract

Extracting cosmological information from microwave sky observations requires accurate estimation of the underlying Cosmic Microwave Background (CMB) by removing foreground contamination, instrumental noise, and the effects of beam convolution. In this work, we develop a machine learning-based approach for CMB reconstruction using a generative adversarial network (GAN) architecture, where the generator is modeled as a U-Net-based convolutional neural network. To train the network, we generate realistic microwave sky maps by simulating Planck-like observations: scanning HEALPix-simulated skies with real Planck beam profile, actual scan patterns, and anisotropic noise consistent with Planck data. Our method achieves high-fidelity reconstruction, with the difference between the input and recovered maps being less than $1\%$ (approximately $2\mu\mathrm{K}$ for temperature and less than $0.5\mu\mathrm{K}$ for polarization) outside the Galactic region. Even within the Galactic plane, the reconstruction error stays below $2$-$3\%$ for temperature maps across most regions, and is even smaller for polarization, apart from a few isolated pixels.. Most importantly, we demonstrate, for the first time, that a GAN-based method can effectively correct for foreground contamination, the systematic effects of non-circular beams and the asymmetric Planck scan pattern for both T and E-mode skymaps. Our results demonstrate the effectiveness of our method for robust and accurate recovery of the CMB signal, even in the presence of strong astrophysical foregrounds and instrumental systematics.