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

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Research output: Contribution to journal › Article › peer-review

Published copy (DOI)

10.1016/j.nima.2020.164750

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

Journal	Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Date	Accepted/In press - 7 Oct 2020
Date	Published (current) - 11 Jan 2021
Volume	986
Number of pages	9
Original language	English

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

Projects

Nuclear Physics Consolidated Grant
Project: Research project (funded) › Research
Nuclear Physics Consolidated Grant
Project: Research project (funded) › Research

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