摘要
The precise measurement of cosmic-ray(CR) knees of different primaries is essential to reveal CR acceleration and propagation mechanisms, as well as to explore new physics. However, the classification of CR components is a difficult task, especially for groups with similar atomic numbers. Given that deep learning achieved remarkable breakthroughs in numerous fields, we seek to leverage this technology to improve the classification performance of the CR Proton and Light groups in the LHAASO-KM2A experiment. In this study, we propose a fused graph neural network model for KM2A arrays, where the activated detectors are structured into graphs. We find that the signal and background are effectively discriminated in this model, and its performance outperforms both the traditional physicsbased method and the convolutional neural network(CNN)-based model across the entire energy range.
作者
Chao Jin
Song-zhan Chen
Hui-hai He
靳超;陈松战;何会海(Key Laboratory of Particle Astrophysics,Institute of High Energy Physics,Chinese Academy of Sciences,Beijing 100049,China;University of Chinese Academy of Sciences,19 A Yuquan Rd,Shijingshan District,Beijing 100049,China)
基金
Supported by the National Key R&D Program of China(2018YFA0404201)
the Natural Sciences Foundation of China(11575203,11635011)。
