Improving Neutron-Gamma Discrimination with Stilbene Organic Scintillation Detector Using Blind Nonnegative Matrix and Tensor Factorization Methods

Abstract

In order to perform highly qualified neutron-gamma discrimination in mixed radiation field, we investigate the application of blind source separation methods based on nonnegative matrix and tensor factorization algorithms as new and robust neutron-gamma discrimination software-based approaches. These signal processing tools have allowed to recover original source components from real-world mixture signals which have been recorded at the output of the stilbene scintillation detector. The computation of the performance index of separability of each tested nonnegative algorithm has allowed to select Second-Order NMF algorithm and NTF-2 model as the most efficient techniques for discriminating neutrons and gammas. Furthermore, the neutron-gamma discrimination is highlighted through the computation of the cross-correlation function. The performance of the blind source separation methods has been quantified through the obtained results that prove a good neutron-gamma separation.

Multilayer Perceptron Model Versus Charge Comparison Method for Neutron/Gamma Discrimination in Plastic Scintillator According to Sampling Frequency and Energy Radiation

“…ML techniques have been explored to improve discrimination performance in liquid and stilbene scintillators [12]- [17]. The authors of [12] propose a non-negative matrix factorization to discriminate neutrons and gamma-rays with stilbene scintillator. Another study proposes a Gaussian Mixture model with EJ309 liquid scintillator [13].…”