Improving Neutron-gamma discrimination in CLYC with Gaussian mixture model

Neutron and gamma discrimination is a common issue in neutron detection. Cs2LiYCl6 : Ce (CLYC) crystal, with its unique core-to-valence luminescence (CVL) mechanism for gamma rays, has excellent capability for neutron-gamma discrimination. This paper proposes a new preprocessing method called constant fraction alignment (CFA), based on the fast rise and slow decay characteristics of CLYC, to effectively improve discrimination performance. Unsupervised classification methods that do not require training data have a great advantage since pure neutron sources are difficult to obtain. As an unsupervised clustering method, Gaussian mixture model (GMM) is used to achieve neutron-gamma discrimination of data detected by CLYC, and the impact of different parameters on GMM results is studied. When the input dimension is high, GMM not only requires significantly more time but also reduces precision, so dimensionality reduction is necessary. Principal component analysis (PCA) is a commonly used dimensionality reduction method, and through analysis, it is found that using the first three components of PCA as inputs achieves the best performance. In the GMM solving process, selecting different covariance matrices based on data types can reduce running time while maintaining high accuracy. Diagonal mode is the most suitable according to comparison. Based on the same dataset, the accuracy of GMM is higher than that of charge comparison method and k-means++, with an accuracy of 99.96% for 252C f source and a false alarm rate as low as 0.01% for 137Cs source.