# Pulse-Shape Discrimination of SiPM Array-Coupled CLYC Detector Using Convolutional Neural Network
###### Abstract  
C\({}_{\rm 52}\)LiYCl\({}_{\rm 6}\): Ce\({}^{3+}\) (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 \({}^{137}\)Cs source and moderated \({}^{252}\)Cf source via an SiPM array-coupled CLYC detector and divided into two groups: training and _PSD_ testing. The CNN training set comprised \({}^{137}\)Cs 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. charge comparison method; CLYC; Convolutional Neural Network; pulse-shape discrimination; SiPM +
Footnote †: journal: _Sciences_  
-CVL, Ce\({}^{3+}\), V\({}_{k}\), and STE mechanisms, while heavy ions such as \(\alpha\) particles and \({}^{3}\)H only have V\({}_{k}\) and STE mechanisms [5]. Therefore, the different emission mechanisms of neutrons and \(\gamma\)-rays in CLYC lead to the difference in pulse waveforms. Silicon photomultipliers (SiPMs) are solid-state photomultipliers with the following features: small size, high gain, low voltage, and magnetic field insensitivity [6]. Therefore, SiPM-coupled CLYC detectors are used in small spectrometers [7], environmental dose detection [8], space radiation measurement [9], and other fields. In these fields, CLYC detectors work in complex radiation environments where pulse-shape discrimination (_PSD_) is required. Commonly used _PSD_ methods are: charge comparison, rise-time [10], and frequency-domain analyses (wavelet transform [11] and pulse-gradient analysis [12]). However, the discrimination effect of the rise-time method is generally not as good as that of the charge comparison method, and the frequency-domain analysis method needs to convert the time-domain signal to the frequency domain, which requires additional calculation. Therefore, the charge comparison method is widely used and very suitable for the current pulse [5]. The charge comparison method uses the ratio of the integral amplitude of the signal in different time windows as the discrimination parameter to discriminate the particles, namely the fast-integration time window and the slow-integration time window. The fast time window includes the sharp rise part and the peak part of the signal, while the slow time window includes the signal-attenuation part, as shown in Figure 1. However, SiPMs' large parasitic capacitance will integrate the initial current pulse, resulting in the reduction in the difference between the neutron and \(\gamma\) signal shape, and therefore reducing the _PSD_ performance of the charge comparison method [13]. [PERSON] et al.