We are developing a detector that uses liquid scintillator loaded with Li-6 for the efficient measurement of fast neutrons and their energy. Such a detector would have both scientific applications such as quantification of neutron energy spectra in underground science facilities, as well as security applications such as detection of contraband fissile materials. Fast neutrons deposit energy in the scintillator by proton recoil scattering. If the neutron loses enough energy, it can be captured by lithium, and produce a burst of scintillation photons. Segmentation of the detector would provide significantly improved energy resolution. We present methods to identify this sequence of proton recoil and delayed neutron capture events. Since background gammas also produce scintillation light, we must reject them with high probability. One can discriminate background gamma events from proton recoil and neutron capture events because the scintillation light emission time probability density functions for electronic recoil events and nuclear recoil events are different. In a prototype detector, we measure voltage pulses produced at a photomultiplier tube by scintillation light due to different neutron sources and a gamma ray source. For nuclear recoil and electronic recoil events, we determine a template voltage pulse by robust signal averaging and cluster analysis. For candidate event sequences, we compute the Matusita distances between the normalized voltage pulses in the start and stop regions of the pulse with respect to the template pulses. Based on the ratio of these distances, and information about the voltage pulse amplitude generated by neutron capture, we identify events of interest and determine a voltage pulse amplitude distribution due to the fast neutrons. In future work, we will develop a nonlinear calibration model to predict neutron energy based on the observed voltage pulse.
Citation: IEEE Transactions on Nuclear Science
Pub Type: Journals
fast neutrons, homeland security, pulse shape discrimination, spectroscopy, statistical methods, underground science