1. A novel machine learning method to detect double-$\Lambda$ hypernuclear events in nuclear emulsions
- Author
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He, Yan, Drozd, Vasyl, Ekawa, Hiroyuki, Escrig, Samuel, Gao, Yiming, Kasagi, Ayumi, Liu, Enqiang, Muneem, Abdul, Nakagawa, Manami, Nakazawa, Kazuma, Rappold, Christophe, Saito, Nami, Saito, Takehiko R., Sugimoto, Shohei, Taki, Masato, Tanaka, Yoshiki K., Wang, He, Yanai, Ayari, Yoshida, Junya, and Zhang, Hongfei
- Subjects
High Energy Physics - Experiment - Abstract
A novel method was developed to detect double-$\Lambda$ hypernuclear events in nuclear emulsions using machine learning techniques. The object detection model, the Mask R-CNN, was trained using images generated by Monte Carlo simulations, image processing, and image-style transformation based on generative adversarial networks. Despite being exclusively trained on $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ events, the model achieved a detection efficiency of 93.9$\%$ for $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ and 81.5$\%$ for $\prescript{5\ }{\Lambda\Lambda}{\rm{H}}$ events in the produced images. In addition, the model demonstrated its ability to detect the Nagara event, which is the only uniquely identified $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ event reported to date. It also exhibited a proper segmentation of the event topology. Furthermore, after analyzing 0.2$\%$ of the entire emulsion data from the J-PARC E07 experiment utilizing the developed approach, six new candidates for double-$\Lambda$ hypernuclear events were detected, suggesting that more than 2000 double-strangeness hypernuclear events were recorded in the entire dataset. This method is sufficiently effective for mining more latent double-$\Lambda$ hypernuclear events recorded in nuclear emulsion sheets by reducing the time required for manual visual inspection by a factor of five hundred.
- Published
- 2024