Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms
Feng Zhang, F. R Zhu, S. M Liu, Y. C Hao, Chaoming He, Jianfeng Hou, Zhilin Li · Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021) · 2021
Identification of proton and gamma plays an essential role in ultra-high energy gamma-ray astronomy with LHAASO-KM2A. In this work, two neural networks (deep neural networks (DNN) and graph neural networks (GNN)) are applied to distinguish proton and gamma in the LHAASO-KM2A simulation data. The receiver operating characteristic (ROC) curves are used to evaluate the quality of the model. Both KM2A-DNN and KM2A-GNN models give higher Area Under Curve (AUC) scores than the traditional baseline model.