NAOC Open IR
Deep residual detection of radio frequency interference for FAST
Yang, Zhicheng1; Yu, Ce1; Xiao, Jian1; Zhang, Bo2,3
2020-02-01
Source PublicationMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
ISSN0035-8711
Volume492Issue:1Pages:1421-1431
AbstractRadio frequency interference (RFI) detection and excision are key steps in the data-processing pipeline of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Because of its high sensitivity and large data rate, FAST requires more accurate and efficient RFI flagging methods than its counterparts. In the last decades, approaches based upon artificial intelligence (AI), such as codes using convolutional neural networks (CNNs), have been proposed to identify RFI more reliably and efficiently. However, RFI flagging of FAST data with such methods has often proved to be erroneous, with further manual inspections required. In addition, network construction as well as preparation of training data sets for effective RFI flagging has imposed significant additional workloads. Therefore, rapid deployment and adjustment of AI approaches for different observations is impractical to implement with existing algorithms. To overcome such problems, we propose a model called RFI-Net. With the input of raw data without any processing, RFI-Net can detect RFI automatically, producing corresponding masks without any alteration of the original data. Experiments with RFI-Net using simulated astronomical data show that our model has outperformed existing methods in terms of both precision and recall. Besides, compared with other models, our method can obtain the same relative accuracy with fewer training data, thus reducing the effort and time required to prepare the training data set. Further, the training process of RFI-Net can be accelerated, with overfittings being minimized, compared with other CNN codes. The performance of RFI-Net has also been evaluated with observing data obtained by FAST and the Bleien Observatory. Our results demonstrate the ability of RFI-Net to accurately identify RFI with fine-grained, high-precision masks that required no further modification.
Keywordmethods: data analysis methods: observational techniques: image processing
Funding OrganizationNational Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; NSFC ; NSFC ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; NSFC ; NSFC ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; NSFC ; NSFC ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; NSFC ; NSFC ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences
DOI10.1093/mnras/stz3521
WOS KeywordNEURAL-NETWORKS ; MITIGATION ; TELESCOPE
Language英语
Funding ProjectNational Natural Science Foundation of China (NSFC)[U1731125] ; National Natural Science Foundation of China (NSFC)[U1731243] ; Chinese Academy of Sciences[U1731125] ; Chinese Academy of Sciences[U1731243] ; NSFC[11903056] ; NSFC[U1531246] ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences
Funding OrganizationNational Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; NSFC ; NSFC ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; NSFC ; NSFC ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; NSFC ; NSFC ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; NSFC ; NSFC ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences ; Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciences
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000512329900109
PublisherOXFORD UNIV PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/54118
Collection中国科学院国家天文台
Corresponding AuthorYu, Ce; Xiao, Jian
Affiliation1.Tianjin Univ, Coll Intelligence & Comp, 135 Yaguan Rd, Tianjin 300350, Peoples R China
2.Chinese Acad Sci, Natl Astron Observ, 20 Datun Rd, Beijing 100012, Peoples R China
3.Chinese Acad Sci, Natl Astron Observ, CAS Key Lab FAST, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Yang, Zhicheng,Yu, Ce,Xiao, Jian,et al. Deep residual detection of radio frequency interference for FAST[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2020,492(1):1421-1431.
APA Yang, Zhicheng,Yu, Ce,Xiao, Jian,&Zhang, Bo.(2020).Deep residual detection of radio frequency interference for FAST.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,492(1),1421-1431.
MLA Yang, Zhicheng,et al."Deep residual detection of radio frequency interference for FAST".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 492.1(2020):1421-1431.
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