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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.

A BC523A liquid scintillator loaded with boron-10 was tested as a detector for both fast and thermal neutrons. A pulse shape discrimination (PSD) method based on a zero-crossing principle was applied to distinguish between neutron and gamma radiation. High quantum efficiency Photonis XP5500B photomultiplier was used to enhance light detection from the scintillator. This allowed a good registration of the energy spectrum of neutron capture events on boron-10, corresponding to about 60 keV gamma-rays. The applied PSD method proved to be useful for n/gamma discrimination. A good resolving power of the method was achieved even without gating on neutron capture events. A comparison with a standard BC501A liquid scintillator was done to evaluate thermal neutrons detection efficiency in BC523A., QC 20100525