%0 Journal Article
%T Neutron detection and gamma-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537
%A Soderstrom, P. A. et al
%A Agramunt, J.
%A Egea, J.
%A Gadea, A.
%A Huyuk, T.
%J Nuclear Instruments & Methods in Physics Research A
%D 2019
%V 916
%I Elsevier Science Bv
%@ 0168-9002
%G English
%F Soderstrom_etal2019
%O WOS:000455016800033
%O exported from refbase (https://references.ific.uv.es/refbase/show.php?record=3869), last updated on Fri, 18 Jan 2019 09:13:40 +0000
%X 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.
%K BC-501A
%K BC-537
%K Digital pulse-shape discrimination
%K Fast-neutron detection
%K Liquid scintillator
%K Neural networks
%R 10.1016/j.nima.2018.11.122
%U https://doi.org/10.1016/j.nima.2018.11.122
%P 238-245