Speaker
JIN Chao
Description
Precise measurement about the cosmic-ray (CR) component knee is essential for revealing the mistery of CR's acceleration and propagation mechanism, as well as exploring the new physics. However, strictly classifying the CR species is a tough task owing to the same hadronic interaction mechanism during their impinging on the atomosphere. Recently, rapid growth has occured in the analysis of the graph-structed data with the deep learning algorithm, which is manifested the strong strength in dealing with the structured and sparse data common in the physics researching field. In this work, we leverage the Graph Neural Network (GNN) to improve the CR-Proton and light-component classification performance on the LHAASO-KM2A experiment. The LHAASO-KM2A detectors activated by the extensive air shower are constructed as the Graph, and both the spatial manifold and the lateral distribution information of the showers are utilized. We demonstrate the effectiveness of the GNN architecture, which presents its adaptive nature over the 2 orders of magnitude of the energy, and outperforms the traditional physics-based method.
Primary author
JIN Chao
Co-authors
Prof.
Huihai He
(Institute of High Energy Physics, CAS)
Mr
songzhan（松战） CHEN（陈）
(中科院高能物理研究所)
