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

  • HongFeng WangEmail author
  • WeiWei ZhuEmail author
  • Ping GuoEmail author
  • Di Li
  • SiBo Feng
  • Qian Yin
  • ChenChen Miao
  • ZhenZhao Tao
  • ZhiChen Pan
  • Pei Wang
  • Xin Zheng
  • XiaoDan Deng
  • ZhiJie Liu
  • XiaoYao Xie
  • XuHong Yu
  • ShanPing You
  • Hui Zhang
  • FAST Collaboration
Article Special Topic: The Science and Technology of FAST

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.

Keywords

pulsars neural networks data analysis 

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • HongFeng Wang
    • 1
    • 2
    • 3
    Email author
  • WeiWei Zhu
    • 2
    Email author
  • Ping Guo
    • 4
    Email author
  • Di Li
    • 2
    • 5
  • SiBo Feng
    • 1
  • Qian Yin
    • 1
  • ChenChen Miao
    • 5
    • 2
  • ZhenZhao Tao
    • 7
    • 2
  • ZhiChen Pan
    • 2
  • Pei Wang
    • 2
  • Xin Zheng
    • 1
  • XiaoDan Deng
    • 4
  • ZhiJie Liu
    • 6
  • XiaoYao Xie
    • 6
  • XuHong Yu
    • 6
  • ShanPing You
    • 6
  • Hui Zhang
    • 6
  • FAST Collaboration
  1. 1.Image Processing and Pattern Recognition Laboratory, College of Information Science and TechnologyBeijing Normal UniversityBeijingChina
  2. 2.CAS Key Laboratory of FASTChinese Academy of SciencesBeijingChina
  3. 3.School of Information ManagementDezhou UniversityDezhouChina
  4. 4.Image Processing and Pattern Recognition Laboratory, School of Systems ScienceBeijing Normal UniversityBeijingChina
  5. 5.University of Chinese Academy of SciencesBeijingChina
  6. 6.Key Laboratory of Information and Computing Science Guizhou ProvinceGuizhou Normal UniversityGuiyangChina
  7. 7.School of Physics and Electronic ScienceGuizhou Normal UniversityGuiyangChina

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