Optimization of 6LiF:ZnS(Ag) Scintillator Light Yield using GEANT4

Published: June 01, 2018


Y. Yehuda-Zada, Kevin NMN Pritchard, Jeffrey B Ziegler, Catherine C Cooksey, Kerry NMN Siebein, M. Jackson, C. Hurlbut, Y. Kadmon, Y. Cohen, R. M. Ibberson, Charles F. Majkrzak, Nick C. Maliszewskyj, I. Orion, A. Osovizky


A new cold neutron detector has been developed at the NCNR for the CANDoR project. Geometric and performance constraints dictate that this detector be exceptionally thin (~2nm). For this reason, the design of the detector consists of 6LiF:ZnS(Ag) scinitallator with embedded wavelength shifting (WLS) fiber. We used the GEANT4 package to simulate neutron capture and light transport in the detector optimizations between the competing requirements of high neutron capture probability and light production and transport. In the process, we have developed a method for predicting light collection and total neuron detection efficiency for different detector configurations. The simulation was performed by adjusting crucial parameters such as the scintillator stoichiometry, light yield, component grain size, WLS fiber geometry, and reflectors at the outside edges of the scintillator volume. Three different detector configurations were fabricated and their test results were correlated with the simulations. Through this correlation we have managed to find a common photon threshold for the different detector configurations which was then used to simulate and predict the efficiencies for many other detector configurations. New detectors fabricated based on simulation results yield the desired sensitivity of 90% for 3.27 meV cold neutrons. The simulation has proven to be useful tool by dramatically reducing the development period and the required number of detector prototypes. It can be used to test new designs with different thicknesses and different target neutron energies.
Citation: Nuclear Instruments & Methods in Physics Research A
Volume: 892
Pub Type: Journals

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GEANT4, scintillator, neutron scattering, neutron detection
Created June 01, 2018, Updated June 21, 2018