@Article{Soderstrom_etal2019,
author="Soderstrom, P. A. et al
and Agramunt, J.
and Egea, J.
and Gadea, A.
and Huyuk, T.",
title="Neutron detection and gamma-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537",
journal="Nuclear Instruments {\&} Methods in Physics Research A",
year="2019",
publisher="Elsevier Science Bv",
volume="916",
pages="238--245",
optkeywords="BC-501A; BC-537; Digital pulse-shape discrimination; Fast-neutron detection; Liquid scintillator; Neural networks",
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 gamma rays. Special emphasis is put on the application of artificial neural networks. The results show a systematically higher gamma-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 gamma 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 gamma-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.",
optnote="WOS:000455016800033",
optnote="exported from refbase (https://references.ific.uv.es/refbase/show.php?record=3869), last updated on Fri, 18 Jan 2019 09:13:40 +0000",
issn="0168-9002",
doi="10.1016/j.nima.2018.11.122",
opturl="https://doi.org/10.1016/j.nima.2018.11.122",
language="English"
}