Foreground Removal in Ground-Based CMB Observations Using a Transformer Model

TL;DR

This paper introduces a Transformer-based deep learning method, TCMB, for foreground removal in ground-based CMB observations, demonstrating improved accuracy and direct spherical map processing over CNN approaches.

Contribution

The paper presents the novel TCMB Transformer model for CMB foreground removal, outperforming CNN methods and enabling direct processing of HEALPix spherical sky maps.

Findings

TCMB achieves lower variance in CMB map reconstruction.

CMB power spectra recovered closely match true values.

Transformer-based approach outperforms CNN-based methods.

Abstract

We present a novel method for Cosmic Microwave Background (CMB) foreground removal based on deep learning techniques. This method employs a Transformer model, referred to as \texttt{TCMB}, which is specifically designed to effectively process HEALPix-format spherical sky maps. \texttt{TCMB} represents an innovative application in CMB data analysis, as it is an image-based technique that has rarely been utilized in this field. Using simulated data with noise levels representative of current ground-based CMB polarization observations, the \texttt{TCMB} method demonstrates robust performance in removing foreground contamination. The mean absolute variance for the reconstruction of the noisy CMB Q/U map is significantly less than the CMB polarization signal. To mitigate biases caused by instrumental noise, a cross-correlation approach using two half-mission maps was employed, successfully recovering CMB EE and BB power spectra that align closely with the true values, and these results validate the effectiveness of the \texttt{TCMB} method. Compared to the previously employed convolutional neural network (CNN)-based approach, the \texttt{TCMB} method offers two significant advantages: (1) It demonstrates superior effectiveness in reconstructing CMB polarization maps, outperforming CNN-based methods. (2) It can directly process HEALPix spherical sky maps without requiring rectangular region division, a step necessary for CNN-based approaches that often introduces uncertainties such as boundary effects. This study highlights the potential of Transformer-based models as a powerful tool for CMB data analysis, offering a substantial improvement over traditional CNN-based techniques.