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Classifying cosmic-ray proton and light groups in LHAASO-KM2A experiment with graph neural network

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【作者】 靳超陈松战何会海

【Author】 Chao Jin;Song-zhan Chen;Hui-hai He;Key Laboratory of Particle Astrophysics, Institute of High Energy Physics, Chinese Academy of Sciences;University of Chinese Academy of Sciences;

【机构】 Key Laboratory of Particle Astrophysics, Institute of High Energy Physics, Chinese Academy of SciencesUniversity of Chinese Academy of Sciences

【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.

【Abstract】 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.

【关键词】 cosmic ray kneegraph neural network
【Key words】 cosmic ray kneegraph neural network
【基金】 Supported by the National Key R&D Program of China (2018YFA0404201);the Natural Sciences Foundation of China (11575203,11635011)
  • 【文献出处】 Chinese Physics C ,中国物理C , 编辑部邮箱 ,2020年06期
  • 【分类号】TP183;O572.1
  • 【被引频次】1
  • 【下载频次】22
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