  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.
