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.

Neutron imaging is an effective nondestructive testing (NDT) technique widely applied to detect structural defects and the enrichment of nuclear fuel elements due to its high penetration and nuclide-sensitive properties. Since the fuel element pellet is sealed in the cladding, the transmission imaging result is a superposition of the two parts. Therefore, the attenuation of neutrons by the cladding is interference that must be considered in the enrichment analysis. It is necessary to extract and separate cladding and pellets using an edge extraction method. However, the low neutron cross-section of the cladding material (e.g., aluminum and zirconium) leads to poor grayscale contrast at the cladding edge in the imaging result, and the intensity of the cladding edge is significantly lower than that of the pellet edge. In addition, affected by the noise from the imaging environment, the boundaries of targets are further blurred, making edge detection more challenging. Traditional detection algorithms extract the weak edges of cladding incompletely, and the results are discontinuous, with obvious edge breaks and missing areas. This paper proposes a method to extract edges in neutron images based on phase congruency (PC). This study utilized the classical perceptual field model to improve contrast at weak edges. The enriched edge map was generated using our PC model from six directions, allowing more weak edges to be detected accurately. The non-maximum suppression ensured precise localization and avoided edge breaks. Furthermore, the edge results were optimized by eliminating noise through morphological operations. The experimental results demonstrate that the proposed method effectively detects the weak edges of the cladding, is superior in accuracy and integrity to traditional detection, and is able to obtain stable and reliable results with different materials of neutron images. The edge integrity improved by 64.1%, and the edge localization accuracy reached 94.3%. The extracted edge information is useful in the next stage of the high-precision enrichment analysis.