Prediction of explosives by a de-broadening model based on RBF neural network

Abstract

The de-broadening algorithm based on RBFNN is proposed to solve the problems of overlapping peaks and ROI selection in the traditional peak area ratio method. This algorithm is combined with the tagged neutron method for the detection of hidden explosives inside packages and Geant4 is applied for modeling the experimental setup. The broadened energy spectra from 3.5 MeV to 6.8 MeV are chosen as input for the prediction of the unbroadened energy spectra from 3.5 MeV to 6.5 MeV. The result shows that the de-broadening algorithm improves the degree of separation of the samples compared to the peak area ratio method. In the reliability validation of the model, the trained RBFNN can effectively predict the samples within the database and has 99 % identification accuracy in a blind experiment. Meanwhile, the established model predicts other kinds of samples, different masses of samples, and untrained experimental spectra, the good prediction results verify the universality of the model.