Explosives detection using prompt-gamma neutron activation and neural networks

This work describes a study of the application of a neural network to determine the presence of explosives using the neutron capture prompt gamma-ray spectra of the substances as patterns which were simulated via Monte Carlo N-particle transport code, version 4B. After the training of the neural networks, it was possible to determine the presence of the C-4 explosive, even when they were occluded by several materials. The neural network was a powerful tool, able to recognize prompt gamma-ray explosive patterns in spite of the presence of occluding materials. Besides that, the network was able to generalize, identify the presence of explosive in cases in which it had not been trained. In that way, it was revealed as a potential tool for in situ inspection systems.