Neutron detection and γ-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537

P. A. Söderström*, G. Jaworski, J. J. Valiente Dobón, J. Nyberg, J. Agramunt, G. de Angelis, S. Carturan, J. Egea, M. N. Erduran, S. Ertürk, G. de France, A. Gadea, A. Goasduff, V. González, K. Hadyńska-Klȩk, T. Hüyük, V. Modamio, M. Moszynski, A. Di Nitto, M. PalaczN. Pietralla, E. Sanchis, D. Testov, A. Triossi, R. Wadsworth

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)238-245
Number of pages8
JournalNuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Early online date30 Nov 2018
Publication statusPublished - 1 Feb 2019

Bibliographical note

© 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.


  • BC-501A
  • BC-537
  • Digital pulse-shape discrimination
  • Fast-neutron detection
  • Liquid scintillator
  • Neural networks

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