Foreground Removal in Ground-Based CMB Observations Using a Transformer Model
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914079916949504 |
|---|---|
| author | Yan, Ye-Peng Li, Si-Yu Liu, Yang Xia, Jun-Qing Li, Hong |
| author_facet | Yan, Ye-Peng Li, Si-Yu Liu, Yang Xia, Jun-Qing Li, Hong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09071 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Foreground Removal in Ground-Based CMB Observations Using a Transformer Model Yan, Ye-Peng Li, Si-Yu Liu, Yang Xia, Jun-Qing Li, Hong Cosmology and Nongalactic Astrophysics 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. |
| title | Foreground Removal in Ground-Based CMB Observations Using a Transformer Model |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2502.09071 |