Advertisement

Experimental validation of material discrimination ability of muon scattering tomography at the TUMUTY facility

  • Xing-Yu Pan
  • Yi-Fan Zheng
  • Zhi ZengEmail author
  • Xue-Wu Wang
  • Jian-Ping Cheng
Article

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.

Keywords

Muon tomography Cargo container inspection Material discrimination SVM classifier 

References

  1. 1.
    K.A. Olive, K. Agashe, C. Amsler et al., Review of particle physics. Chin. Phys. C 38, 090001 (2014).  https://doi.org/10.1103/PhysRevD.98.030001 CrossRefGoogle Scholar
  2. 2.
    S. Procureur, Muon imaging: principles, technologies and applications. Nucl. Instrum. Methods A 878, 169–179 (2017).  https://doi.org/10.1016/j.nima.2017.08.004 CrossRefGoogle Scholar
  3. 3.
    V. Antonuccio, M. Bandieramonte, D.L. Bonanno et al., The muon portal project: design and construction of a scanning portal based on muon tomography. Nucl. Instrum. Methods A 845, 322–325 (2017).  https://doi.org/10.1016/j.nima.2016.05.006 CrossRefGoogle Scholar
  4. 4.
    G. Blanpied, S. Kumar, D. Dorroh et al., Material discrimination using scattering and stopping of cosmic ray muons and electrons: differentiating heavier from lighter metals as well as low-atomic weight materials. Nucl. Instrum. Methods A 784, 352–358 (2015).  https://doi.org/10.1016/j.nima.2014.11.027 CrossRefGoogle Scholar
  5. 5.
    P. Baesso, D. Cussans, C. Thomay et al., Toward a RPC-based muon tomography system for cargo containers. J. Instrum. 9, C10041 (2014).  https://doi.org/10.1088/1748-0221/9/10/C10041 CrossRefGoogle Scholar
  6. 6.
    L. Frazão, J. Velthuis, C. Thomay et al., Discrimination of high-z materials in concrete-filled containers using muon scattering tomography. J. Instrum. 11, P07020 (2016).  https://doi.org/10.1088/1748-0221/11/07/P07020 CrossRefGoogle Scholar
  7. 7.
    D. Poulson, J.M. Durham, E. Guardincerri et al., Cosmic ray muon computed tomography of spent nuclear fuel in dry storage casks. Nucl. Instrum. Methods A 842, 48–53 (2017).  https://doi.org/10.1016/j.nima.2016.10.040 CrossRefGoogle Scholar
  8. 8.
    J.M. Durham, D. Poulson, J. Bacon et al., Verification of spent nuclear fuel in sealed dry storage casks via measurements of cosmic-ray muon scattering. Phys. Rev. Appl. 9, 44013 (2018).  https://doi.org/10.1103/PhysRevApplied.9.044013 CrossRefGoogle Scholar
  9. 9.
    Y. Zheng, Z. Zeng, M. Zeng et al., Discrimination of drugs and explosives in cargo inspections by applying machine learning in muon tomography. High Power Laser Part. Beams 30, 086002 (2018).  https://doi.org/10.11884/HPLPB201830.180062 CrossRefGoogle Scholar
  10. 10.
    X. Wang, M. Zeng, Z. Zeng et al., The cosmic ray muon tomography facility based on large scale MRPC detectors. Nucl. Instrum. Methods A 784, 390–393 (2015).  https://doi.org/10.1016/j.nima.2015.01.024 CrossRefGoogle Scholar
  11. 11.
    L.J. Schultz, Dissertation, Portland State University (2003)Google Scholar
  12. 12.
    B. Yu, Z. Zhao, X. Wang et al., An MAP algorithm with edge-preserving prior for muon tomography, in IEEE Nuclear Science Symposium & Medical Imaging Conference. IEEE (2014).  https://doi.org/10.1109/nssmic.2014.7431083

Copyright information

© China Science Publishing & Media Ltd. (Science Press), Shanghai Institute of Applied Physics, the Chinese Academy of Sciences, Chinese Nuclear Society and Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Xing-Yu Pan
    • 1
  • Yi-Fan Zheng
    • 2
    • 3
  • Zhi Zeng
    • 1
    Email author
  • Xue-Wu Wang
    • 1
  • Jian-Ping Cheng
    • 4
  1. 1.Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering PhysicsTsinghua UniversityBeijingChina
  2. 2.Department of Nuclear EngineeringUniversity of CaliforniaBerkeleyUSA
  3. 3.Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoUSA
  4. 4.College of Nuclear Science and TechnologyBeijing Normal UniversityBeijingChina

Personalised recommendations