NeutralNet: an application of deep neural networks to pulse shape discrimination for use with accelerator-based neutron sources

Recent works have implemented machine learning based solutions for many complex classification tasks including pulse shape discrimination in radiation detection. The present work aims to advance the application of machine learning to pulse shape discrimination in neutron detection. A machine learning based neutron-gamma discrimination technique is investigated for various neutron energy distributions produced from DD, DT, (α,n), and spontaneous fission neutron sources. Comprehensive investigations on the training data generation techniques, the impact of the PMT bias, and the discrimination performance are conducted. With the increase of the PMT bias voltage, the neutron classification performance peaked at 1500 V with 81 % of validation neutrons being identified at a false positive rate of 1E-6 while the further bias increase led to a notable degradation in performance. The unsatisfactory classification performance encountered when training off of one neutron source type and classifying neutrons from the other source types was greatly improved with the application of the transfer learning techniques. The remaining variation in the performance was accounted for by the energy dependence of the neutron classification. It was demonstrated that at the 1E-6 FPR specificity level, the events within the region of overlap for neutron and photon populations could be separated, down to a detected energy of 30 keVee. An overall intrinsic neutron detection efficiency of 12.5 % was achieved for the ²⁵²Cf neutron source at a false positive rate of 1E-6.