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Hypernuclear event detection in the nuclear emulsion with Monte Carlo simulation and machine learning
- Publication Year :
- 2023
-
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.<br />Comment: 32 pages, 13 figures
- Subjects :
- Nuclear Experiment
Physics - Instrumentation and Detectors
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2305.00884
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1016/j.nima.2023.168663