PoS - Proceedings of Science
Volume 395 - 37th International Cosmic Ray Conference (ICRC2021) - GAI - Gamma Ray Indirect
Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms
F. Zhang*  on behalf of the LHAASO Collaboration, F.R. Zhu, S.M. Liu, Y.C. Hao, C. He, J. Hou and Z. Li
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Pre-published on: July 05, 2021
Published on: March 18, 2022
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.
DOI: https://doi.org/10.22323/1.395.0741
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