PT Journal
AU Soderstrom, PAea
   Agramunt, J
   Egea, J
   Gadea, A
   Huyuk, T
TI Neutron detection and gamma-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537
SO Nuclear Instruments & Methods in Physics Research A
JI Nucl. Instrum. Methods Phys. Res. A
PY 2019
BP 238
EP 245
VL 916
DI 10.1016/j.nima.2018.11.122
LA English
DE BC-501A; BC-537; Digital pulse-shape discrimination; Fast-neutron detection; Liquid scintillator; Neural networks
AB 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.
ER