Development and Commissioning of a Compact Cosmic Ray Muon Imaging Prototype
Abstract
Due to the muon tomography's capability of imaging high Z materials, some potential applications have been reported on inspecting smuggled nuclear materials in customs. A compact Cosmic Ray Muons (CRM) imaging prototype, Lanzhou University Muon Imaging System (LUMIS), is comprehensively introduced in this paper including the structure design, assembly, data acquisition and analysis, detector performance test, and material imaging commissioning etc. Casted triangular prism plastic scintillators (PS) were coupled with Si-PMs for sensitive detector components in system. LUMIS's experimental results show that the detection efficiency of an individual detector layer is about 98%, the position resolution for vertical incident muons is 2.5 mm and the angle resolution is 8.73 mrad given a separation distance of 40.5 cm. Moreover, the image reconstruction software was developed based on the Point of Closest Approach (PoCA) to detect lead bricks as our target. The reconstructed images indicate that the profile of the lead bricks in the image is highly consistent with the target. Subsequently, the capability of LUMIS to distinguish different materials, such as Pb, Cu, Fe, and Al, was investigated as well. The lower limit of response time for rapidly alarming high-Z materials is also given and discussed. The successful development and commissioning of the LUMIS prototype have provided a new solution option in technology and craftsmanship for developing compact CRM imaging systems that can be used in many applications.
Cite
@article{arxiv.2203.05910,
title = {Development and Commissioning of a Compact Cosmic Ray Muon Imaging Prototype},
author = {Xujia Luo and Quanxiao Wang and Kemian Qin and Heng Tian and Zhiqiang Fu and Yanwei Zhao and Zhongtao Shen and Hao Liu and Yuanyong Fu and Guorui Liu and Kaiqiang Yao and Xiangping Qian and Jian Rong and Weixiong Zhang and Xiaogang Luo and Chunxian Liu and Xiangsheng Tian and Minghai Yu and Feng Wu and Jingjing Chen and Juntao Liu and Zhiyi Liu},
journal= {arXiv preprint arXiv:2203.05910},
year = {2022}
}