Deep residual detection of radio frequency interference for FAST
文献类型：期刊论文
| 作者 | Yang, Zhicheng1; Yu, Ce1; Xiao, Jian1; Zhang, Bo2,3 |
| 刊名 | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
 |
| 出版日期 | 2020-02-01 |
| 卷号 | 492期号:1页码:1421-1431 |
| 关键词 | methods: data analysis methods: observational techniques: image processing |
| ISSN号 | 0035-8711 |
| DOI | 10.1093/mnras/stz3521 |
| 英文摘要 | Radio 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. |
| WOS关键词 | NEURAL-NETWORKS ; MITIGATION ; TELESCOPE |
| 资助项目 | National 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 |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:000512329900109 |
| 出版者 | OXFORD UNIV PRESS |
| 资助机构 | 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 ; 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 |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/54117]  |
| 专题 | 中国科学院国家天文台 |
| 通讯作者 | Yu, Ce; Xiao, Jian |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：国家天文台
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