{"title":"CMBFSCNN:利用卷积神经网络进行宇宙微波背景极化前景减法","authors":"Ye-Peng Yan, Si-Yu Li, Guo-Jian Wang, Zirui Zhang, Jun-Qing Xia","doi":"10.3847/1538-4365/ad5c66","DOIUrl":null,"url":null,"abstract":"In our previous study, we introduced a machine learning technique, namely Cosmic Microwave Background Foreground Subtraction with Convolutional Neural Networks (<monospace>CMBFSCNN</monospace>), for the removal of foreground contamination in cosmic microwave background (CMB) polarization data. This method was successfully employed on actual observational data from the Planck mission. In this study, we extend our investigation by considering the CMB lensing effect in simulated data and utilizing the <monospace>CMBFSCNN</monospace> approach to recover the CMB lensing B-mode power spectrum from multifrequency observational maps. Our method is first applied to simulated data with the performance of the CMB-S4 experiment. We achieve reliable recovery of the noisy CMB <italic toggle=\"yes\">Q</italic> (or <italic toggle=\"yes\">U</italic>) maps with a mean absolute difference of 0.016 ± 0.008 <italic toggle=\"yes\">μ</italic>K (or 0.021 ± 0.002 <italic toggle=\"yes\">μ</italic>K) for the CMB-S4 experiment. To address the residual instrumental noise in the foreground-cleaned map, we employ a “half-split maps” approach, where the entire data set is divided into two segments sharing the same sky signal but having uncorrelated noise. Using cross-correlation techniques between two recovered half-split maps, we effectively reduce instrumental noise effects at the power spectrum level. As a result, we achieve precise recovery of the CMB EE and lensing B-mode power spectra. Furthermore, we also extend our pipeline to full-sky simulated data with the performance of the LiteBIRD experiment. As expected, various foregrounds are cleanly removed from the foregrounds contamination observational maps, and recovered EE and lensing B-mode power spectra exhibit excellent agreement with the true results. Finally, we discuss the dependency of our method on the foreground models.","PeriodicalId":22368,"journal":{"name":"The Astrophysical Journal Supplement Series","volume":"44 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network\",\"authors\":\"Ye-Peng Yan, Si-Yu Li, Guo-Jian Wang, Zirui Zhang, Jun-Qing Xia\",\"doi\":\"10.3847/1538-4365/ad5c66\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In our previous study, we introduced a machine learning technique, namely Cosmic Microwave Background Foreground Subtraction with Convolutional Neural Networks (<monospace>CMBFSCNN</monospace>), for the removal of foreground contamination in cosmic microwave background (CMB) polarization data. This method was successfully employed on actual observational data from the Planck mission. In this study, we extend our investigation by considering the CMB lensing effect in simulated data and utilizing the <monospace>CMBFSCNN</monospace> approach to recover the CMB lensing B-mode power spectrum from multifrequency observational maps. Our method is first applied to simulated data with the performance of the CMB-S4 experiment. We achieve reliable recovery of the noisy CMB <italic toggle=\\\"yes\\\">Q</italic> (or <italic toggle=\\\"yes\\\">U</italic>) maps with a mean absolute difference of 0.016 ± 0.008 <italic toggle=\\\"yes\\\">μ</italic>K (or 0.021 ± 0.002 <italic toggle=\\\"yes\\\">μ</italic>K) for the CMB-S4 experiment. To address the residual instrumental noise in the foreground-cleaned map, we employ a “half-split maps” approach, where the entire data set is divided into two segments sharing the same sky signal but having uncorrelated noise. Using cross-correlation techniques between two recovered half-split maps, we effectively reduce instrumental noise effects at the power spectrum level. As a result, we achieve precise recovery of the CMB EE and lensing B-mode power spectra. Furthermore, we also extend our pipeline to full-sky simulated data with the performance of the LiteBIRD experiment. As expected, various foregrounds are cleanly removed from the foregrounds contamination observational maps, and recovered EE and lensing B-mode power spectra exhibit excellent agreement with the true results. 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引用次数: 0
摘要
在之前的研究中,我们介绍了一种机器学习技术,即卷积神经网络宇宙微波背景前景减法(CMBFSCNN),用于去除宇宙微波背景(CMB)极化数据中的前景污染。该方法已成功应用于普朗克任务的实际观测数据。在本研究中,我们扩展了研究范围,考虑了模拟数据中的 CMB 透镜效应,并利用 CMBFSCNN 方法从多频观测图中恢复 CMB 透镜 B 模式功率谱。我们的方法首先应用于具有 CMB-S4 实验性能的模拟数据。我们可靠地恢复了有噪声的 CMB Q(或 U)图,CMB-S4 实验的平均绝对差值为 0.016 ± 0.008 μK(或 0.021 ± 0.002 μK)。为了解决前景净化图中的残余仪器噪声问题,我们采用了 "半分割图 "方法,即将整个数据集划分为共享相同天空信号但噪声不相关的两个区段。利用两个恢复的半分割图之间的交叉相关技术,我们可以有效地减少功率谱级的仪器噪声影响。因此,我们实现了对 CMB EE 和透镜 B 模式功率谱的精确恢复。此外,我们还利用 LiteBIRD 实验的性能,将我们的管道扩展到全天空模拟数据。不出所料,各种前景都被从前景污染观测图中清除了,恢复的 EE 和 Lensing B 模式功率谱与真实结果呈现出极好的一致性。最后,我们讨论了我们的方法对前景模型的依赖性。
CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network
In our previous study, we introduced a machine learning technique, namely Cosmic Microwave Background Foreground Subtraction with Convolutional Neural Networks (CMBFSCNN), for the removal of foreground contamination in cosmic microwave background (CMB) polarization data. This method was successfully employed on actual observational data from the Planck mission. In this study, we extend our investigation by considering the CMB lensing effect in simulated data and utilizing the CMBFSCNN approach to recover the CMB lensing B-mode power spectrum from multifrequency observational maps. Our method is first applied to simulated data with the performance of the CMB-S4 experiment. We achieve reliable recovery of the noisy CMB Q (or U) maps with a mean absolute difference of 0.016 ± 0.008 μK (or 0.021 ± 0.002 μK) for the CMB-S4 experiment. To address the residual instrumental noise in the foreground-cleaned map, we employ a “half-split maps” approach, where the entire data set is divided into two segments sharing the same sky signal but having uncorrelated noise. Using cross-correlation techniques between two recovered half-split maps, we effectively reduce instrumental noise effects at the power spectrum level. As a result, we achieve precise recovery of the CMB EE and lensing B-mode power spectra. Furthermore, we also extend our pipeline to full-sky simulated data with the performance of the LiteBIRD experiment. As expected, various foregrounds are cleanly removed from the foregrounds contamination observational maps, and recovered EE and lensing B-mode power spectra exhibit excellent agreement with the true results. Finally, we discuss the dependency of our method on the foreground models.