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Material discrimination using cosmic ray muon scattering tomography with an artificial neural network

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Abstract

Introduction

Muon scattering tomography (MST) can be employed to scan cargo containers and vehicles for special nuclear materials by using cosmic muons. However, the flux of cosmic ray muons is relatively low for direct detection. Thus, the detection has to be done in a short timescale with small numbers of muons to satisfy the demands of practical applications.

Method

In this paper, we propose an artificial neural network (ANN) algorithm for material discrimination using MST. The muon scattering angles were simulated using Geant4 to formulate the training set, and the muon scatter angles were measured by Micromegas detection system to create the test set.

Results

The ANN-based algorithm presented here ensures a discrimination accuracy of 98.0% between aluminum, copper and tungsten in a 5 min measurement of 4 × 4 × 4 cm3 blocks.

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Acknowledgements

This work was supported by the Program of National Natural Science Foundation of China Grant Nos. 11805168 and 21805251. Thanks are due to Dr. Zhiyong Zhang for designing the micromegas detectors and Prof. Changqing Feng for providing the readout electronics. The authors would like to thank Prof. Shubin Liu and Dr. Yu Wang for their assistance with the muon tomography experiments. They are all from University of Science and Technology of China.

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Authors and Affiliations

  1. Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China

    Weibo He, Dingyue Chang, Rengang Shi, Maobing Shuai & Sa Xiao

  2. Key Laboratory of Green Manufacturing of Superlight Elastomer Materials of State Ethnic Affairs Commission, Hubei Minzu University, Enshi, 445000, China

    Yingru Li

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  1. Weibo He
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  2. Dingyue Chang
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  3. Rengang Shi
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  4. Maobing Shuai
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  5. Yingru Li
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Correspondence to Weibo He, Yingru Li or Sa Xiao.

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He, W., Chang, D., Shi, R. et al. Material discrimination using cosmic ray muon scattering tomography with an artificial neural network. Radiat Detect Technol Methods 6, 254–261 (2022). https://doi.org/10.1007/s41605-022-00319-3

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  • DOI: https://doi.org/10.1007/s41605-022-00319-3

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