Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2019, Vol. 30 ›› Issue (8): 120 doi: 10.1007/s41365-019-0649-4

• NUCLEAR PHYSICS AND INTERDISCIPLINARY RESEARCH • Previous Articles     Next Articles

Experimental Validation for the Material Discrimination Ability of Muon Scattering Tomography with Four Materials Using TUMUTY

Xing-Yu Pan 1 , Yi-Fan Zheng2 3, Zhi Zeng1, Xue-Wu Wang1, Jian-Ping Cheng4   

  1. 1 Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084, China
    2 Department of Nuclear Engineering, University of California, Berkeley, CA 94709, USA
    3 Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143, USA
    4 College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875, China
  • Received:2019-02-22 Revised:2019-04-11 Accepted:2019-04-11
  • Contact: Zhi Zeng E-mail:zengzhi@tsinghua.edu.cn
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Xing-Yu Pan, Yi-Fan Zheng, Zhi Zeng, Xue-Wu Wang, Jian-Ping Cheng. Experimental Validation for the Material Discrimination Ability of Muon Scattering Tomography with Four Materials Using TUMUTY.Nuclear Science and Techniques, 2019, 30(8): 120     doi: 10.1007/s41365-019-0649-4
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Abstract: Muon scattering tomography is believed to be a promising technique for cargo container inspection, owing to the ability of natural muons to penetrate into dense materials and the absence of artificial radiation. In this work, the material discrimination ability of muon scattering tomography is evaluated based on experiments at the Tsinghua University cosmic ray muon tomography facility, with four materials: flour (as drugs substitute), aluminum, steel, and lead. The features of the different materials could be discriminated with cluster analysis and classifiers based on support vector machine. The overall discrimination precisions for these four materials could reach 70, 95, and 99% with 1-, 5-, and 10-min-long measurement, respectively.

Key words: Muon tomography, Cargo container inspection, Material discrimination, SVM classifier