Artificial neural networks for neutron/γ discrimination in the neutron detectors of NEDA

Three different Artificial Neural Network architectures have been applied to perform neutron/gamma discrimination in NEDA based on waveform and time-of-flight information. Using the coincident gamma-rays from AGATA, we have been able to measure and compare on real data the performances of the Artificial Neural Networks as classifiers. While the general performances are quite similar for the data set we used, differences, in particular related to the computing times, have been highlighted. One of the Artificial Neural Network architecture has also been found more robust to time misalignment of the waveforms. Such a feature is of great interest for online processing of waveforms.