Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms
文献类型：会议论文
| 作者 | Zhang, F.1; Zhu, F. R.1; Liu, S. M.1; Hao, Y. C.2; He, C.3; Hou, J.3; Li, Z.4; Cao, Zhen5,6,7; Aharonian, F.8,9; An, Q.10,11 |
| 出版日期 | 2022-03-18 |
| 会议日期 | 2021-07-12 |
| 会议地点 | Virtual, Berlin, Germany |
| 卷号 | 395 |
| DOI | 10.22323/1.395.0741 |
| 英文摘要 | 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 LHAASOKM2A 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. © Copyright owned by the author(s) under the terms of the Creative Commons. |
| 产权排序 | 第33完成单位 |
| 资助机构 | National Natural Science Foundation of China[11947404] ; Department of Science and Technology of Sichuan Province[2020YFSY0016,2021YFSY0031] |
| 会议录 | Proceedings of Science
 |
| 会议录出版者 | Sissa Medialab Srl |
| 文献子类 | Conference article (CA) |
| 学科主题 | 天文学 ; 天体物理学 ; 高能天体物理学 ; 核科学技术 |
| 语种 | 英语 |
| URL标识 | 查看原文 |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/25722]  |
| 专题 | 星系类星体研究组 |
| 作者单位 | 1.School of Physical Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China; 2.Graduate School of Tangshan, Southwest Jiaotong University, Tangshan, 063000, China; 3.School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China; 4.Key Laboratory of Particle Astrophysics, Institute of High Energy Physics, Beijing, 100049, China; 5.Key Laboratory of Particle Astrophyics, Experimental Physics Division, Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China; 6.University of Chinese Academy of Sciences, Beijing, 100049, China; 7.TIANFU Cosmic Ray Research Center, Sichuan, Chengdu, China; 8.Dublin Institute for Advanced Studies, 31 Fitzwilliam Place, Dublin 2, Ireland; 9.Max-Planck-Institut for Nuclear Physics, P.O. Box 103980, Heidelberg, 69029, Germany; 10.State Key Laboratory of Particle Detection and Electronics, China; |
| 推荐引用方式 GB/T 7714 | Zhang, F.,Zhu, F. R.,Liu, S. M.,et al. Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms[C]. 见:. Virtual, Berlin, Germany. 2021-07-12. |
入库方式： OAI收割
来源：云南天文台
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