1. Development of machine learning analyses with graph neural network for the WASA-FRS experiment.
- Author
-
Ekawa, H., Dou, W., Gao, Y., He, Y., Kasagi, A., Liu, E., Muneem, A., Nakagawa, M., Rappold, C., Saito, N., Saito, T. R., Taki, M., Tanaka, Y. K., Wang, H., and Yoshida, J.
- Subjects
- *
MACHINE learning , *MONTE Carlo method , *HYPERFRAGMENTS , *MESONS - Abstract
The WASA-FRS experiment aims to reveal the nature of light Λ hypernuclei with heavy-ion beams. The lifetimes of hypernuclei are measured precisely from their decay lengths and kinematics. To reconstruct a π - 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 π - 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%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF