Neutrons/Gamma (n/γ) discrimination is critical for neutron detection in the presence of γ radiation and traditional pulse shape discrimination methods suffer from unstable discrimination accuracy.

This study aims to implement a machine-learning method that combines the kernel principal component analysis (KPCA), marine predator algorithm (MPA), and extreme learning machine (ELM) is proposed to improve the n/γ discrimination efficiency and accuracy against the traditional pulse shape discrimination methods.

The KPCA was used to reduce the dimensionality of the pulse signal characteristics of neutrons and gamma rays. Owing to the randomness in the ELM input layer weight and hidden layer bias, the MPA was employed to optimize the foregoing factors to improve the n/γ discrimination accuracy of the ELM. Finally, experimental data of Pu-C neutron source using BC-501A liquid scintillator detector were applied to effectiveness comparison of training and test with and without KPCA dimensionality reduction.

Comparison results reveal that the average discrimination accuracy of the KPCA-MPA-ELM is as high as 99.07%, which is 12.19%, 2.52%, and 1.56% higher than those of the ELM, MPA-ELM, and KPCA-ELM models, respectively. Compared with the charge comparison method and pulse gradient analysis method, the accuracy is improved by 1.80% and 5.91%, respectively.

The proposed model has a simple structure, exhibits good stability, hence be applied to handling high-dimensional data with good discrimination and generalization ability.