Development of machine learning analyses with graph neural network for the WASA-FRS experiment

被引：0
|
作者：
Ekawa, H. [1 ]
Dou, W. [1 ,2 ]
Gao, Y. [1 ,3 ,4 ]
He, Y. [1 ,5 ]
Kasagi, A. [1 ,6 ]
Liu, E. [1 ,3 ,4 ]
Muneem, A. [1 ,7 ]
Nakagawa, M. [1 ]
Rappold, C. [8 ]
Saito, N. [1 ]
Saito, T. R. [1 ,5 ,9 ]
Taki, M. [10 ]
Tanaka, Y. K. [1 ]
Wang, H. [1 ]
Yoshida, J. [1 ,11 ]
机构：
[1] RIKEN, High Energy Nucl Phys Lab, Cluster Pioneering Res, Wako, Japan
[2] Saitama Univ, Dept Phys, Saitama, Japan
[3] Chinese Acad Sci, Inst Modern Phys, Lanzhou, Peoples R China
[4] Univ Chinese Acad Sci, Beijing, Peoples R China
[5] Lanzhou Univ, Sch Nucl Sci & Technol, Lanzhou, Peoples R China
[6] Gifu Univ, Grad Sch Engn, Gifu, Japan
[7] Ghulam Ishaq Khan Inst Engn Sci & Technol, Fac Engn Sci, Topi, Pakistan
[8] CSIC, Inst Estruct Mat, Madrid, Spain
[9] GSI Helmholtz Ctr Heavy Ion Res, Darmstadt, Germany
[10] Rikkyo Univ, Grad Sch Artificial Intelligence & Sci, Tokyo, Japan
[11] Tohoku Univ, Dept Phys, Sendai, Japan
来源：
EUROPEAN PHYSICAL JOURNAL A | 2023年 / 59卷 / 05期
关键词：
BINDING-ENERGY VALUES; COLLISIONS;
D O I：
10.1140/epja/s10050-023-01016-5
中图分类号：
O57 [原子核物理学、高能物理学];
学科分类号：
070202 ;
摘要：
The WASA-FRS experiment aims to reveal the nature of light A hypernuclei with heavy-ion beams. The lifetimes of hypernuclei are measured precisely from their decay lengths and kinematics. To reconstruct a p(- )track emitted from hypernuclear decay, track finding is an important issue. In this study, a machine learning analysis method with a graph neural network (GNN), which is a powerful tool for deducing the connection between data nodes, was developed to obtain track associations from numerous combinations of hit information provided in detectors based on a Monte Carlo simulation. An efficiency of 98% was achieved for tracking p(-) mesons using the developed GNN model. The GNN model can also estimate the charge and momentum of the particles of interest. More than 99.9% of the negative charged particles were correctly identified with a momentum accuracy of 6.3%.
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页数：13
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