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Neutron detection and ?-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537
(Elsevier B.V., 2019) Söderström P.-A.; Jaworski G.; Valiente Dobón J.J.; Nyberg J.; Agramunt J.; de Angelis G.; González V.
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 ? rays. Special emphasis is put on the application of artificial neural networks. The results show a systematically higher ?-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 ? 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 ?-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. © 2018