Hypernuclear event detection in the nuclear emulsion with Monte Carlo simulation and machine learning

Author:

A. Kasagi, W. Dou, V. Drozd, H. Ekawa, S. Escrig, Y. Gao, Y. He, E. Liu, A. Muneem, M. Nakagawa, K. Nakazawa, C. Rappold, N. Saito, T. R. Saito, S. Sugimoto, M. Taki, Y. K. Tanaka, A. Yanai, J. Yoshida, M. Yoshimoto, H. Wang

Keyword:

Nuclear Experiment, Nuclear Experiment (nucl-ex), Instrumentation and Detectors (physics.ins-det)

journal:

date:

2023-04-30 16:00:00

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.

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