تقرير
Hypernuclear event detection in the nuclear emulsion with Monte Carlo simulation and machine learning
| العنوان: | Hypernuclear event detection in the nuclear emulsion with Monte Carlo simulation and machine learning |
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| المؤلفون: | 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., Wang, H. |
| سنة النشر: | 2023 |
| المجموعة: | Nuclear Experiment Physics (Other) |
| مصطلحات موضوعية: | Nuclear Experiment, Physics - Instrumentation and Detectors |
| الوصف: | 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 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1016/j.nima.2023.168663 |
| الوصول الحر: | http://arxiv.org/abs/2305.00884Test |
| رقم الانضمام: | edsarx.2305.00884 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1016/j.nima.2023.168663 |
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