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

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  • Special Topic: The Science and Technology of FAST
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Abstract

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.

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Authors and Affiliations

  1. Image Processing and Pattern Recognition Laboratory, College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China

    HongFeng Wang, SiBo Feng, Qian Yin & Xin Zheng

  2. CAS Key Laboratory of FAST, Chinese Academy of Sciences, Beijing, 100101, China

    HongFeng Wang, WeiWei Zhu, Di Li, ChenChen Miao, ZhenZhao Tao, ZhiChen Pan & Pei Wang

  3. School of Information Management, Dezhou University, Dezhou, 253023, China

    HongFeng Wang

  4. Image Processing and Pattern Recognition Laboratory, School of Systems Science, Beijing Normal University, Beijing, 100875, China

    Ping Guo & XiaoDan Deng

  5. University of Chinese Academy of Sciences, Beijing, 100049, China

    Di Li & ChenChen Miao

  6. Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, 550001, China

    ZhiJie Liu, XiaoYao Xie, XuHong Yu, ShanPing You & Hui Zhang

  7. School of Physics and Electronic Science, Guizhou Normal University, Guiyang, 550001, China

    ZhenZhao Tao

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  1. HongFeng Wang
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FAST Collaboration

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Correspondence to HongFeng Wang, WeiWei Zhu or Ping Guo.

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Wang, H., Zhu, W., Guo, P. et al. Pulsar candidate selection using ensemble networks for FAST drift-scan survey. Sci. China Phys. Mech. Astron. 62, 959507 (2019). https://doi.org/10.1007/s11433-018-9388-3

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