Recovering Cosmic Microwave Background Polarization Signals with Machine Learning
Primordial B-mode detection is one of the main goals of current and future cosmic microwave background (CMB) experiments. However, the weak B-mode signal is overshadowed by several Galactic polarized emissions, such as thermal dust emission and synchrotron radiation. Subtracting foreground component...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/acbfb4 |
| _version_ | 1827831332315594752 |
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| author | Ye-Peng Yan Guo-Jian Wang Si-Yu Li Jun-Qing Xia |
| author_facet | Ye-Peng Yan Guo-Jian Wang Si-Yu Li Jun-Qing Xia |
| author_sort | Ye-Peng Yan |
| collection | DOAJ |
| description | Primordial B-mode detection is one of the main goals of current and future cosmic microwave background (CMB) experiments. However, the weak B-mode signal is overshadowed by several Galactic polarized emissions, such as thermal dust emission and synchrotron radiation. Subtracting foreground components from CMB observations is one of the key challenges in searching for the primordial B-mode signal. Here, we construct a deep convolutional neural network (CNN) model, called CMBFSCNN (Cosmic Microwave Background Foreground Subtraction with CNN), which can cleanly remove various foreground components from simulated CMB observational maps at the sensitivity of the CMB-S4 experiment. Noisy CMB Q (or U) maps are recovered with a mean absolute difference of 0.018 ± 0.023 μ K (or 0.021 ± 0.028 μ K). To remove the residual instrumental noise from the foreground-cleaned map, inspired by the needlet internal linear combination method, we divide the whole data set into two “half-split maps,” which share the same sky signal, but have uncorrelated noise, and perform a cross-correlation technique to reduce the instrumental noise effects at the power spectrum level. We find that the CMB EE and BB power spectra can be precisely recovered with significantly reduced noise effects. Finally, we apply this pipeline to current Planck observations. As expected, various foregrounds are cleanly removed from the Planck observational maps, with the recovered EE and BB power spectra being in good agreement with the official Planck results. |
| first_indexed | 2024-03-12T04:43:06Z |
| format | Article |
| id | doaj.art-9070d8dd882c4f71b422c96fb2b185c3 |
| institution | Directory Open Access Journal |
| issn | 1538-4357 |
| language | English |
| last_indexed | 2024-03-12T04:43:06Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj.art-9070d8dd882c4f71b422c96fb2b185c32023-09-03T09:30:45ZengIOP PublishingThe Astrophysical Journal1538-43572023-01-0194712910.3847/1538-4357/acbfb4Recovering Cosmic Microwave Background Polarization Signals with Machine LearningYe-Peng Yan0Guo-Jian Wang1https://orcid.org/0000-0003-0272-5032Si-Yu Li2Jun-Qing Xia3Department of Astronomy, Beijing Normal University , Beijing 100875, People's Republic of China ; xiajq@bnu.edu.cnSchool of Chemistry and Physics, University of KwaZulu-Natal , Westville Campus, Private Bag X54001, Durban, 4000, South Africa; NAOC-UKZN Computational Astrophysics Centre (NUCAC), University of KwaZulu-Natal , Durban, 4000, South AfricaKey Laboratory of Particle Astrophysics, Institute of High Energy Physics, Chinese Academy of Science , P.O. Box 918-3, Beijing 100049, People's Republic of ChinaDepartment of Astronomy, Beijing Normal University , Beijing 100875, People's Republic of China ; xiajq@bnu.edu.cnPrimordial B-mode detection is one of the main goals of current and future cosmic microwave background (CMB) experiments. However, the weak B-mode signal is overshadowed by several Galactic polarized emissions, such as thermal dust emission and synchrotron radiation. Subtracting foreground components from CMB observations is one of the key challenges in searching for the primordial B-mode signal. Here, we construct a deep convolutional neural network (CNN) model, called CMBFSCNN (Cosmic Microwave Background Foreground Subtraction with CNN), which can cleanly remove various foreground components from simulated CMB observational maps at the sensitivity of the CMB-S4 experiment. Noisy CMB Q (or U) maps are recovered with a mean absolute difference of 0.018 ± 0.023 μ K (or 0.021 ± 0.028 μ K). To remove the residual instrumental noise from the foreground-cleaned map, inspired by the needlet internal linear combination method, we divide the whole data set into two “half-split maps,” which share the same sky signal, but have uncorrelated noise, and perform a cross-correlation technique to reduce the instrumental noise effects at the power spectrum level. We find that the CMB EE and BB power spectra can be precisely recovered with significantly reduced noise effects. Finally, we apply this pipeline to current Planck observations. As expected, various foregrounds are cleanly removed from the Planck observational maps, with the recovered EE and BB power spectra being in good agreement with the official Planck results.https://doi.org/10.3847/1538-4357/acbfb4Cosmic microwave background radiationObservational cosmologyConvolutional neural networks |
| spellingShingle | Ye-Peng Yan Guo-Jian Wang Si-Yu Li Jun-Qing Xia Recovering Cosmic Microwave Background Polarization Signals with Machine Learning The Astrophysical Journal Cosmic microwave background radiation Observational cosmology Convolutional neural networks |
| title | Recovering Cosmic Microwave Background Polarization Signals with Machine Learning |
| title_full | Recovering Cosmic Microwave Background Polarization Signals with Machine Learning |
| title_fullStr | Recovering Cosmic Microwave Background Polarization Signals with Machine Learning |
| title_full_unstemmed | Recovering Cosmic Microwave Background Polarization Signals with Machine Learning |
| title_short | Recovering Cosmic Microwave Background Polarization Signals with Machine Learning |
| title_sort | recovering cosmic microwave background polarization signals with machine learning |
| topic | Cosmic microwave background radiation Observational cosmology Convolutional neural networks |
| url | https://doi.org/10.3847/1538-4357/acbfb4 |
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