Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms

doi:10.22323/1.395.0741

YNAO OpenIR > 星系类星体研究组

	Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms
	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, Zhen 5,6,7; Aharonian, F.8,9; An, Q.10,11; Axikegu 12; Bai, L. X.13; Bai, Y. X.5,7; Bao, Y. W.14; Bastieri, D.15; Bi, X. J.5,6,7; Bi, Y. J.5,7; Cai, H.16; Cai, J. T.15; Cao, Zhe 10,11; Chang, J.17; Chang, J. F.5,7,10; Chen, B. M.18; Chen, E. S.5,6,7; Chen, J.13; Chen, Liang 5,6,7; Chen, Liang 19; Chen, Long 12; Chen, M. J.5,7; Chen, M. L.5,7,10; Chen, Q. H.12; Chen, S. H.5,6,7; Chen, S. Z.5,7; Chen, T. L.20; Chen, X. L.5,6,7; Chen, Y.14; Cheng, N.5,7; Cheng, Y. D.5,7; Cui, S. W.18; Cui, X. H.21; Cui, Y. D.22; D'Ettorre Piazzoli, B.23; Dai, B. Z.24; Dai, H. L.5,7,10; Dai, Z. G.11; Danzengluobu 20; della Volpe, D.25; Dong, X. J.5,7; Duan, K. K.17; Fan, J. H.15; Fan, Y. Z.17; Fan, Z. X.5,7; Fang, J.24; Fang, K.5,7; Feng, C. F.26; Feng, L.17; Feng, S. H.5,7; Feng, Y. L.17; Gao, B.5,7; Gao, C. D.26; Gao, L. Q.5,6,7; Gao, Q.20; Gao, W.26; Ge, M. M.24; Geng, L. S.5,7; Gong, G. H.27; Gou, Q. B.5,7; Gu, M. H.5,7,10; Guo, F. L.19; Guo, J. G.5,6,7; Guo, X. L.12; Guo, Y. Q.5,7; Guo, Y. Y.5,6,7,17; Han, Y. A.28; He, H. H.5,6,7; He, H. N.17; He, J. C.5,6,7; He, S. L.15; He, X. B.22; He, Y.12; Heller, M.25; Hor, Y. K.22; Hou, C.5,7; Hu, H. B.5,6,7; Hu, S.13; Hu, S. C.5,6,7; Hu, X. J.27; Huang, D. H.12; Huang, Q. L.5,7; Huang, W. H.26; Huang, X. T.26; Huang, X. Y.17; Huang, Z. C.12; Ji, F.5,7; Ji, X. L.5,7,10; Jia, H. Y.12; Jiang, K.10,11; Jiang, Z. J.24; Jin, C.5,6,7; Ke, T.5,7; Kuleshov, D.29; Levochkin, K.29; Li, B. B.18; Li, Cheng 10,11; Li, Cong 5,7; Li, F.5,7,10; Li, H. B.5,7; Li, H. C.5,7; Li, H. Y.11,17; Li, J.5,7,10; Li, K.5,7; Li, W. L.26; Li, X. R.5,7; Li, Xin 10,11; Li, Xin 12; Li, Y.13; Li, Y. Z.5,6,7; Li, Zhe 5,7; Li, Zhuo 30; Liang, E. W.31; Liang, Y. F.31; Lin, S. J.22; Liu, B.11; Liu, C.5,7; Liu, D.26; Liu, H.12; Liu, H. D.28; Liu, J.5,7; Liu, J. L.32; Liu, J. S.22; Liu, J. Y.5,7; Liu, M. Y.20; Liu, R. Y.14; Liu, S. M.12; Liu, W.5,7; Liu, Y.15; Liu, Y. N.27; Liu, Z. X.13; Long, W. J.12; Lu, R.24; Lv, H. K.5,7; Ma, B. Q.30; Ma, L. L.5,7; Ma, X. H.5,7; Mao JR(毛基荣)33 ; Masood, A.12; Min, Z.5,7; Mitthumsiri, W.34; Montaruli, T.25; Nan, Y. C.26; Pang, B. Y.12; Pattarakijwanich, P.34; Pei, Z. Y.15; Qi, M. Y.5,7; Qi, Y. Q.18; Qiao, B. Q.5,7; Qin, J. J.11; Ruffolo, D.34; Rulev, V.29; Sáiz, A.34; Shao, L.18; Shchegolev, O.29,35; Sheng, X. D.5,7; Shi, J. Y.5,7; Song, H. C.30; Stenkin, Yu. V.29,35; Stepanov, V.29; Su, Y.36; Sun, Q. N.12; Sun, X. N.31; Sun, Z. B.37; Tam, P. H.22
会议录名称	Proceedings of Science
	2022-03-18
卷号	395
DOI	10.22323/1.395.0741
产权排序	第33完成单位
收录类别	EI
会议名称	37th International Cosmic Ray Conference, ICRC 2021
会议日期	2021-07-12
会议地点	Virtual, Berlin, Germany
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.
资助项目	National Natural Science Foundation of China[11947404] ; Department of Science and Technology of Sichuan Province[2020YFSY0016] ; Department of Science and Technology of Sichuan Province[2021YFSY0031]
项目资助者	National Natural Science Foundation of China[11947404] ; Department of Science and Technology of Sichuan Province[2020YFSY0016, 2021YFSY0031]
语种	英语
学科领域	天文学 ; 天体物理学 ; 高能天体物理学 ; 核科学技术
文章类型	Conference article (CA)
出版者	Sissa Medialab Srl
URL	查看原文
EI入藏号	20230113326294
EI主题词	Gamma rays
EI分类号	461.4 Ergonomics and Human Factors Engineering - 723.4.2 Machine Learning - 931.3 Atomic and Molecular Physics - 932.1 High Energy Physics
引用统计
文献类型	会议论文
条目标识符	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; 11.University of Science and Technology of China, Anhui, Hefei, 230026, China; 12.School of Physical Science and Technology, School of Information Science and Technology, Southwest Jiaotong University, Sichuan, Chengdu, 610031, China; 13.College of Physics, Sichuan University, Sichuan, Chengdu, 610065, China; 14.School of Astronomy and Space Science, Nanjing University, Jiangsu, Nanjing, 210023, China; 15.Center for Astrophysics, Guangzhou University, Guangdong, Guangzhou, 510006, China; 16.School of Physics and Technology, Wuhan University, Hubei, Wuhan, 430072, China; 17.Key Laboratory of Dark Matter and Space Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, Jiangsu, Nanjing, 210023, China; 18.Hebei Normal University, Hebei, Shijiazhuang, 050024, China; 19.Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai, 200030, China; 20.Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, Tibet, Lhasa, 850000, China; 21.National Astronomical Observatories, Chinese Academy of Sciences, Beijing, 100101, China; 22.School of Physics and Astronomy, School of Physics (Guangzhou), Sun Yat-sen University, Guangdong, Zhuhai, 519000, China; 23.Dipartimento di Fisica, Università di Napoli 24.School of Physics and Astronomy, Yunnan University, Yunnan, Kunming, 650091, China; 25.D'epartement de Physique Nucl'eaire et Corpusculaire, Facult'e de Sciences, Universit'e de Gen'eve, 24 Quai Ernest Ansermet, Geneva, 1211, Switzerland; 26.Institute of Frontier and Interdisciplinary Science, Shandong University, Shandong, Qingdao, 266237, China; 27.Department of Engineering Physics, Tsinghua University, Beijing, 100084, China; 28.School of Physics and Microelectronics, Zhengzhou University, Henan, Zhengzhou, 450001, China; 29.Institute for Nuclear Research of Russian Academy of Sciences, Moscow, 117312, Russia; 30.School of Physics, Peking University, Beijing, 100871, China; 31.School of Physical Science and Technology, Guangxi University, Guangxi, Nanning, 530004, China; 32.Tsung-Dao Lee Institute, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China; 33.Yunnan Observatories, Chinese Academy of Sciences, Yunnan, Kunming, 650216, China; 34.Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand; 35.Moscow Institute of Physics and Technology, Moscow, 141700, Russia; 36.Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, Jiangsu, Nanjing, 210023, China; 37.National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, 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]:Sissa Medialab Srl,2022.

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