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Efficient Discrimination of Neutron and Gamma Rays using Different Digital Pulse Processing Algorithms

In different neutron measurement experiments, it is necessary to apply pulse processing method to distinguish neutron pulses from gamma pulses. The discrimination process is based on the different decaying response of the detector for both neutron and gamma events. Different proposed algorithms are presented in this paper for determining the radiation type of detector output. In the proposed algorithms, features are extracted from the input radiation event. These features are extracted using charge integration, Hilbert transformation, and matched filtering methods. The extracted features are then fed the discriminator which is an Artificial Neural Network (ANN) or Support Vector Machine (SVM) discriminators. The obtained results prove that, the proposed approaches can be used efficiently for the neutron and gamma discrimination purpose and that method based on the Hilbert transformation achieves the highest discrimination rates.