Abstract
Cs2LiYCl6: Ce3+ (CLYC) is a dual-mode gamma-neutron scintillator with a medium gamma-ray resolution and pulse-shape discrimination (PSD) capability. The PSD performance of CLYC is greatly weakened when coupled with silicon photomultipliers (SiPMs) because of SiPMs’ low detection efficiency for the ultrafast Core-Valence-Luminescence (CVL) component under gamma excitation. In our previous work, the PSD Figure-of-Merit (FoM) value was optimized to 2.45 at the gamma-equivalent energy region of the thermal neutron by using the charge comparison method. However, this value was reduced to 1.37 at the lower gamma-equivalent energy region of more than 325 keV, and neutrons were difficult to distinguish from gamma rays. Hence, new algorithms should be studied to improve the PSD performance at low gamma-equivalent energy regions. Convolutional Neural Networks (CNNs) have excellent image recognition capabilities, and thus, neutron and gamma-ray waveforms can be discriminated by their characteristics through a known training set. In this study, neutron and gamma-ray waveforms were measured with a 137Cs source and moderated 252Cf source via an SiPM array-coupled CLYC detector and divided into two groups: training and PSD testing. The CNN training set comprised 137Cs characteristic gamma-ray waveforms and thermal neutron waveforms that were discriminated by the charge comparison method from the training group. A CNN with two convolution-pooling layers was designed to accomplish PSD with the test group. The PSD FoM value of the CNN method was calculated to be 37.20 at the gamma-equivalent energy region of more than 325 keV. This result was much higher than that of the charge comparison method, indicating that neutrons and gamma rays could be better distinguished with the CNN method, especially at low gamma-equivalent energy regions.
Highlights
The secondary particles produced by γ-rays in Cs2LiYCl6: Ce3+ (CLYC) crystals are electrons, while thermal neutrons cause 6 Li (n, α)3 H, and fast neutrons will have 35 Cl (n, p)35 S, 35 Cl (n, α) nuclear reactions [4]
When comparing the input of different formats, we found that the input signal of the two-dimensional matrix format was better than the one-dimensional vector format in terms of training time and effect
After further analysis of the feature extraction and transmission processes of the Convolutional Neural Networks (CNNs), it was found that the CNN could effectively extract the features of the input signal and amplify the feature variance of different types of signals during the transmission process
Summary
A Cs2 LiYCl6 : Ce3+ (CLYC) scintillator is a scintillation crystal capable of simultaneously detecting neutrons and gamma rays [1,2]. CLYC crystals have four scintillation mechanisms [3]: core-to-valence luminescence (CVL), Ce3+ Ce3+ ), Vk (binary Vk -electron diffusion), and self-trapped exciton (STE) emission, where. The secondary particles produced by γ-rays in CLYC crystals are electrons, while thermal neutrons cause 6 Li (n, α) H, and fast neutrons will have 35 Cl (n, p) S, 35 Cl (n, α) nuclear reactions [4]. Electrons in CLYC will have CVL, Appl. Electrons in CLYC will have CVL, Ce3+, Vk, and STE mechanisms, while heavy ions such as α particles and 3H only have Vk and [5].
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