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

Research output: Contribution to journalArticlepeer-review

Published copy (DOI)

Author(s)

  • 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

Department/unit(s)

Publication details

JournalNuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
DateAccepted/In press - 7 Oct 2020
DatePublished (current) - 11 Jan 2021
Volume986
Number of pages9
Original languageEnglish

Abstract

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.

    Research areas

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

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