LAUSR

Abstract Recent developments of digital signal processing have played an effective role to achieve a fast and accurate neutron-gamma discrimination. Thus, we present in this research work, a novel method which combines supervised and unsupervised machine learning to perform the neutron-gamma discrimination task at the output of a stilbene organic scintillation detector. We propose a three steps procedure that highly qualifies the discrimination. First, the detector’s output signals are processed as mixtures of several unknown sources, through nonnegative matrix factorization algorithms. Second, the continuous wavelet transform is performed to characterize the recovered original sources. The resulting time-scale representation is considered as an image which is segmented in order to extract main features of neutron signals versus the gamma ones. The features are then used as input of a nonlinear support vector machine classifier to finally achieve the neutron-gamma discrimination in mixed radiation field. Furthermore, the proposed method provides the classification precision for each radiation.