Component Separation method for CMB using Convolutional Neural Networks

TL;DR

This paper presents a CNN-based method for separating CMB signals from foreground emissions in Planck and QUIJOTE data, achieving accurate temperature maps and novel polarized intensity maps, aiding cosmological studies.

Contribution

Introduces a deep CNN approach for component separation in CMB data, providing a new method that recovers both temperature and polarized intensity maps, including novel polarized maps from QUIJOTE.

Findings

CNN successfully recovers CMB signals in temperature and polarization.

Reconstructed temperature maps are consistent with Planck results.

Polarized intensity maps are newly obtained and scientifically valuable.

Abstract

The aim of this project is to recover the CMB anisotropies maps in temperature and polarized intensity by means of a deep convolutional neural network (CNN) which, after appropiate training, can remove the foregrounds from Planck and QUIJOTE data. The results are then compared with those obtained by COMMANDER, based on Bayesian parametric component separation. The CNN successfully recovered the CMB signal for both All Sky and Partial Sky maps showing frequency dependant results, being optimum for central frequencies where there is less contamination by foregrounds emissions such as galactic synchrotron and thermal dust emissions. Recovered maps in temperature are consistent with those obtained by Planck Collaboration, while polarized intensity has been recovered as a new observable. The polarized intensity maps recovered from QUIJOTE experiment are novel and of potential interest to the scientific community for the detection of primordial gravitational waves. The way forward will be to recover the maps at higher NSIDE and make them available to the scientific community.