Abstract
The WASA-FRS experiment aims to reveal the nature of light \(\Lambda \) hypernuclei with heavy-ion beams. The lifetimes of hypernuclei are measured precisely from their decay lengths and kinematics. To reconstruct a \(\pi ^{-}\) 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 \(\pi ^{-}\) 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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This manuscript has no associated data or the data will not be deposited. [Authors’ comment: This is a simulation study and thus there are no experimental data associated.]
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Acknowledgements
This work was supported by the Special Postdoctoral Researcher Program at RIKEN. The authors thank Yukiko Kurakata of the High Energy Nuclear Physics Laboratory at RIKEN to provide administrative support for the entire project. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Communicated by Takashi Nakamura.
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Ekawa, H., Dou, W., Gao, Y. et al. Development of machine learning analyses with graph neural network for the WASA-FRS experiment. Eur. Phys. J. A 59, 103 (2023). https://doi.org/10.1140/epja/s10050-023-01016-5
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DOI: https://doi.org/10.1140/epja/s10050-023-01016-5