Söderström, P. A., Jaworski, G., Valiente Dobón, J. J. et al. (22 more authors) (2019) Neutron detection and γ-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. pp. 238-245. ISSN 0168-9002
Abstract
In this work we present a comparison between the two liquid scintillators BC-501A and BC-537 in terms of their performance regarding the pulse-shape discrimination between neutrons and γ rays. Special emphasis is put on the application of artificial neural networks. The results show a systematically higher γ-ray rejection ratio for BC-501A compared to BC-537 applying the commonly used charge comparison method. Using the artificial neural network approach the discrimination quality was improved to more than 95% rejection efficiency of γ rays over the energy range 150 to 1000 keV for both BC-501A and BC-537. However, due to the larger light output of BC-501A compared to BC-537, neutrons could be identified in BC-501A using artificial neural networks down to a recoil proton energy of 800 keV compared to a recoil deuteron energy of 1200 keV for BC-537. We conclude that using artificial neural networks it is possible to obtain the same γ-ray rejection quality from both BC-501A and BC-537 for neutrons above a low-energy threshold. This threshold is, however, lower for BC-501A, which is important for nuclear structure spectroscopy experiments of rare reaction channels where low-energy interactions dominates.
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Copyright, Publisher and Additional Information: | © 2018 Elsevier B.V. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. | ||||||
Keywords: | BC-501A, BC-537, Digital pulse-shape discrimination, Fast-neutron detection, Liquid scintillator, Neural networks | ||||||
Dates: |
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Institution: | The University of York | ||||||
Academic Units: | The University of York > York Institute for Materials Research The University of York > Faculty of Sciences (York) > Physics (York) |
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Funding Information: |
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Depositing User: | Pure (York) | ||||||
Date Deposited: | 14 Jan 2019 16:00 | ||||||
Last Modified: | 06 Dec 2023 12:54 | ||||||
Published Version: | https://doi.org/10.1016/j.nima.2018.11.122 | ||||||
Status: | Published | ||||||
Refereed: | Yes | ||||||
Identification Number: | https://doi.org/10.1016/j.nima.2018.11.122 | ||||||
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