1. Hypernuclear event detection in the nuclear emulsion with Monte Carlo simulation and machine learning
- Author
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Kasagi, A., Dou, W., Drozd, V., Ekawa, H., Escrig, S., Gao, Y., He, Y., Liu, E., Muneem, A., Nakagawa, M., Nakazawa, K., Rappold, C., Saito, N., Saito, T. R., Sugimoto, S., Taki, M., Tanaka, Y. K., Yanai, A., Yoshida, J., Yoshimoto, M., and Wang, H.
- Subjects
Nuclear Experiment ,Physics - Instrumentation and Detectors - Abstract
This study developed a novel method for detecting hypernuclear events recorded in nuclear emulsion sheets using machine learning techniques. The artificial neural network-based object detection model was trained on surrogate images created through Monte Carlo simulations and image-style transformations using generative adversarial networks. The performance of the proposed model was evaluated using $\alpha$-decay events obtained from the J-PARC E07 emulsion data. The model achieved approximately twice the detection efficiency of conventional image processing and reduced the time spent on manual visual inspection by approximately 1/17. The established method was successfully applied to the detection of hypernuclear events. This approach is a state-of-the-art tool for discovering rare events recorded in nuclear emulsion sheets without any real data for training., Comment: 32 pages, 13 figures
- Published
- 2023
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