Discrimination of drugs and explosives in cargo inspections by applying machine learning in 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%. (authors)