在缪子成像中利用模式识别检测毒品与爆炸物

在集装箱安检中一个很重要的亟待解决的问题是如何有效地检测出藏在金属中的毒品和爆炸物。传统的X射线CT难以穿透较厚的金属材料，而中子CT引入了很大的放射性，其屏蔽问题是一个难点。相较之下，宇宙线缪子成像是一种有前景的非破坏性成像技术，因为缪子来源于天然的宇宙射线且有足够的能量完全穿透大型集装箱。本文研究了在可接受的测量时间内，宇宙线缪子成像方法能够在何种程度上识别毒品爆炸物与空气和金属。基于清华大学缪子实验平台TUMUTY并通过Geant4模拟宇宙线缪子与物质的相互作用，毒品爆炸物及不同金属材料的散射密度能够被重建出来。基于模式识别的SVM分类器被训练出来对这些材料进行自动识别分类。结果显示，对于边长为20 cm的不同材料的物块，在10到30 min的测量时间内，能够通过缪子成像方法识别毒品爆炸物与金属材料和本底，分类的错误率约为1%；测量时间为1 min时，分类的错误率恶化为12.9%。

Discrimination of drugs and explosives in cargo inspections byapplyingmachine learningin muon tomography

A previously under-explored difficulty in cargo inspections is how to efficiently detect drugs and explosives concealed in large dense metals.Cosmic ray muon tomography is a promising non-destructive imaging technique to solve the problem because muons are naturally generated in the atmosphere and have sufficient energy to completely penetrate large dense containers.In this work it is investigated that to what extent drugs and explosives of a certain size could be discriminated from air background and metals by muon tomography within acceptable measuring time.A Geant4 Monte Carlo simulation is built based on the Tsinghua University MUon Tomography facility (TUMUTY) and a support vector machine (SVM) classifier based on machine learning is trained to differentiate drugs and explosives from air background and metals automatically.For various 20 cm×20 cm×20 cm objects, with 10 min to 30 min measuring time, drugs and explosives could be discriminated from background and metals by muon tomography with an error rate of about 1%.With 1 min, the error rate deteriorates to 12.9%.