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

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info:eu-repo/semantics/closedAccessTarih
2018Yazar
Soderstrom, P-AJaworski, G.
Dobon, J. J. Valiente
Nyberg, J.
Agramunt, J.
de Angelis, G.
Carturan, S.
Egea, J.
Erduran, Mustafa Nizamettin
Erturk, S.
de France, G.
Gadea, A.
Goasduff, A.
Gonzalez, V
Hadynska-Klek, K.
Huyuk, T.
Modamio, V
Moszynski, M.
Di Nitto, A.
Palacz, M.
Pietralla, N.
Sanchis, E.
Testov, D.
Triossi, A.
Wadsworth, R.
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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.