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Neutron/gamma (n/γ) discrimination method based on KPCA-MPA-ELM

HU Wanping ; ZHANG Guiyu ; ZHANG Yunlong ; TUO Xianguo ; LI Hulin

Hé jìshū, 2024-04, Vol.47 (4), p.040403-040403 [Periódico revisado por pares]

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Título:
Neutron/gamma (n/γ) discrimination method based on KPCA-MPA-ELM
Autor: HU Wanping ; ZHANG Guiyu ; ZHANG Yunlong ; TUO Xianguo ; LI Hulin
Assuntos: extreme learning machine ; kernel principal component analysis ; machine learning ; marine predator algorithm ; n/γ discrimination
É parte de: Hé jìshū, 2024-04, Vol.47 (4), p.040403-040403
Descrição: BackgroundNeutrons/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.PurposeThis 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.MethodsThe 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
Editor: Science Press
Idioma: Chinês

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