Pulsar candidate selection using ensemble networks for FAST drift-scan survey

查看全文 作　　者：HongFeng [1,2,3]Wang;WeiWei [2]Zhu;Ping [4]Guo;Di [2,5]Li;SiBo [1]Feng;Qian [1]Yin;ChenChen [2,5]Miao;ZhenZhao [2,7]Tao;ZhiChen [2]Pan;Pei [2]Wang;Xin [1]Zheng;XiaoDan [4]Deng;ZhiJie [6]Liu;XiaoYao [6]Xie;XuHong [6]Yu;ShanPing [6]You;Hui [6]Zhang;FAST [8]Collaboration

机构地区：[1]Image Processing and Pattern Recognition Laboratory,College of Information Science and Technology,Beijing Normal University,Beijing 100875,China;[2]CAS Key Laboratory of FAST,Chinese Academy of Sciences,Beijing 100101,China;[3]School of Information Management,Dezhou University,Dezhou 253023,China;[4]Image Processing and Pattern Recognition Laboratory,School of Systems Science,Beijing Normal University,Beijing 100875,China;[5]University of Chinese Academy of Sciences,Beijing 100049,China;[6]Key Laboratory of Information and Computing Science Guizhou Province,Guizhou Normal University,Guiyang 550001,China;[7]School of Physics and Electronic Science,Guizhou Normal University,Guiyang 550001,China;[8]不详

出　　处：《Science China(Physics,Mechanics & Astronomy)》索引2019年第62卷第5期,共10页

基　　金：supported by the National Key Research and Development Program of China(Grant No.2017YFA0402600);the Natural Science Foundation of Shandong(Grant No.ZR2015FL006);the CAS International Partnership Program(Grant No.114A11KYSB20160008);the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB23000000);the Chinese Academy of Sciences Pioneer Hundred Talents Program;the National Natural Science Foundation of China(Grant Nos.61472043,11743002,11873067,11690024,and 11725313);the Joint Research Fund in Astronomy(Grant No.U1531242)under Cooperative Agreement between the NSFC and CAS and National Natural Science Foundation of China(Grant No.11673005) 摘　　要：The Commensal Radio Astronomy Five-hundred-meter Aperture Spherical radio Telescope(FAST) Survey(CRAFTS) utilizes the novel drift-scan commensal survey mode of FAST and can generate billions of pulsar candidate signals. The human experts are not likely to thoroughly examine these signals, and various machine sorting methods are used to aid the classification of the FAST candidates. In this study, we propose a new ensemble classification system for pulsar candidates. This system denotes the further development of the pulsar image-based classification system(PICS), which was used in the Arecibo Telescope pulsar survey, and has been retrained and customized for the FAST drift-scan survey. In this study, we designed a residual network model comprising 15 layers to replace the convolutional neural networks(CNNs) in PICS. The results of this study demonstrate that the new model can sort >96% of real pulsars to belong the top 1% of all candidates and classify >1.6 million candidates per day using a dual-GPU and 24-core computer. This increased speed and efficiency can help to facilitate real-time or quasi-real-time processing of the pulsar-search data stream obtained from CRAFTS. In addition, we have published the labeled FAST data used in this study online, which can aid in the development of new deep learning techniques for performing pulsar searches. 关键词：PULSARS NEURAL NETWORKS data analysis

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Pulsar candidate selection using ensemble networks for FAST drift-scan survey

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