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

X. Fabian, G. Baulieu, L. Ducroux, O. Stézowski*, A. Boujrad, E. Clément, S. Coudert, G. de France, N. Erduran, S. Ertürk, V. González, G. Jaworski, J. Nyberg, D. Ralet, E. Sanchis, R. Wadsworth

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Three different Artificial Neural Network architectures have been applied to perform neutron/γ discrimination in NEDA based on waveform and time-of-flight information. Using the coincident γ-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.

Original languageEnglish
Article number164750
Number of pages9
JournalNuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Publication statusPublished - 11 Jan 2021


  • Artificial neural networks
  • Machine learning
  • n-γ discrimination
  • Neutron detector
  • Pulse-shape discrimination
  • γ-ray spectroscopy

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