https://doi.org/10.1140/epja/s10050-023-01016-5
Special Article - New Tools and Techniques
Development of machine learning analyses with graph neural network for the WASA-FRS experiment
1
High Energy Nuclear Physics Laboratory, Cluster for Pioneering Research, RIKEN, Wako, Japan
2
Department of Physics, Saitama University, Saitama, Japan
3
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
4
University of Chinese Academy of Sciences, Beijing, China
5
School of Nuclear Science and Technology, Lanzhou University, Lanzhou, China
6
Graduate School of Engineering, Gifu University, Gifu, Japan
7
Faculty of Engineering Sciences, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan
8
Instituto de Estructura de la Materia, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
9
GSI Helmholtz Center for Heavy Ion Research, Darmstadt, Germany
10
Graduate School of Artificial Intelligence and Science, Rikkyo University, Tokyo, Japan
11
Department of Physics, Tohoku University, Sendai, Japan
Received:
29
July
2022
Accepted:
24
April
2023
Published online:
12
May
2023
The WASA-FRS experiment aims to reveal the nature of light 
hypernuclei with heavy-ion beams. The lifetimes of hypernuclei are measured precisely from their decay lengths and kinematics. To reconstruct a 
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 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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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
