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A novel reconstruction algorithm based on density clustering for cosmic-ray muon scattering inspection

机译:一种基于密度聚类的新型重建算法,用于宇宙射线散射检查的密度聚类

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As a relatively new radiation imaging method, the cosmic-ray muon scattering imaging technology can be used to prevent nuclear smuggling and is of considerable significance to nuclear safety. Proposed in this paper is a new reconstruction algorithm based on density clustering, aiming to improve inspection quality with better performance. Firstly, this new algorithm is introduced in detail. Then in order to eliminate the inequity of the density threshold caused by the heterogeneity of the muon flux in different positions, a new flux correction method is proposed. Finally, three groups of simulation experiments are carried out with the help of Geant4 toolkit to optimize the algorithm parameters, verify the correction method and test the inspection quality under shielded condition, and compare this algorithm with another common inspection algorithm under different conditions. The results show that this algorithm can effectively identify and locate nuclear material with low misjudging and missing rates even when there is shielding and momentum precision is low, and the threshold correcting method is universally effective for density clustering algorithms.
机译:作为相对较新的辐射成像方法,宇宙射线散射成像技术可用于防止核走私,对核安全具有相当大的意义。本文提出的是一种基于密度聚类的新型重建算法,旨在提高性能更好的检测质量。首先,详细介绍了这种新算法。然后为了消除由不同位置中μ子通量的异质性引起的密度阈值的不等性,提出了一种新的磁通校正方法。最后,在GEANT4工具包的帮助下进行了三组仿真实验,以优化算法参数,验证校正方法并在屏蔽条件下测试检测质量,并将该算法与另一种常用检查算法进行比较。结果表明,即使存在屏蔽和动量精度低,该算法也能有效地识别和定位核材料,误误判和缺失率。阈值校正方法对密度聚类算法普遍有效。

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