# Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach

## TL;DR

This paper introduces a novel graph-based Bayesian convolutional neural network that effectively cleans cosmic microwave background maps from foreground contamination, providing accurate reconstructions with uncertainty estimates, based on simulated Planck data.

## Contribution

It presents a new deep learning model combining graph-based Bayesian CNNs with U-Net architecture for CMB cleaning, including pixel-wise uncertainty quantification.

## Key findings

- Accurately recovers CMB maps and power spectra from simulated data.
- Provides pixel-wise uncertainty estimates in CMB reconstruction.
- Demonstrates potential for application to real observational data.

## Abstract

The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe. However, observations of the CMB are contaminated by foreground emissions, obscuring the CMB signal and reducing its efficacy in constraining cosmological parameters. We employ deep learning as a data-driven approach to CMB cleaning from multi-frequency full-sky maps. In particular, we develop a graph-based Bayesian convolutional neural network based on the U-Net architecture that predicts cleaned CMB with pixel-wise uncertainty estimates. We demonstrate the potential of this technique on realistic simulated data based on the Planck mission. We show that our model accurately recovers the cleaned CMB sky map and resulting angular power spectrum while identifying regions of uncertainty. Finally, we discuss the current challenges and the path forward for deploying our model for CMB recovery on real observations.