TY  - JOUR
AU  - Soderstrom, P. A. et al
AU  - Agramunt, J.
AU  - Egea, J.
AU  - Gadea, A.
AU  - Huyuk, T.
PY  - 2019
DA  - 2019//
TI  - Neutron detection and gamma-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537
T2  - Nucl. Instrum. Methods Phys. Res. A
JO  - Nuclear Instruments & Methods in Physics Research A
SP  - 238
EP  - 245
VL  - 916
PB  - Elsevier Science Bv
KW  - BC-501A
KW  - BC-537
KW  - Digital pulse-shape discrimination
KW  - Fast-neutron detection
KW  - Liquid scintillator
KW  - 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.
SN  - 0168-9002
UR  - https://doi.org/10.1016/j.nima.2018.11.122
DO  - 10.1016/j.nima.2018.11.122
LA  - English
N1  - WOS:000455016800033
ID  - Soderstrom_etal2019
ER  -