Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.01657 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913505234386944 |
|---|---|
| author | 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 Zhang, Hongfei |
| author_facet | 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 Zhang, Hongfei |
| contents | A novel method was developed to detect double-$Λ$ 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 $\prescript{6\ }{ΛΛ}{\rm{He}}$ events, the model achieved a detection efficiency of 93.8$\%$ for $\prescript{6\ }{ΛΛ}{\rm{He}}$ and 82.0$\%$ for $\prescript{5\ }{ΛΛ}{\rm{H}}$ events in the produced images. In addition, the model demonstrated its ability to detect the $\prescript{6\ }{ΛΛ}{\rm{He}}$ event named the Nagara event, which is the only uniquely identified double-$Λ$ hypernuclear 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-$Λ$ 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-$Λ$ hypernuclear events recorded in nuclear emulsion sheets by reducing the time required for manual visual inspection by a factor of five hundred. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_01657 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A novel machine learning method to detect double-$Λ$ hypernuclear events in nuclear emulsions 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 Zhang, Hongfei High Energy Physics - Experiment A novel method was developed to detect double-$Λ$ 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 $\prescript{6\ }{ΛΛ}{\rm{He}}$ events, the model achieved a detection efficiency of 93.8$\%$ for $\prescript{6\ }{ΛΛ}{\rm{He}}$ and 82.0$\%$ for $\prescript{5\ }{ΛΛ}{\rm{H}}$ events in the produced images. In addition, the model demonstrated its ability to detect the $\prescript{6\ }{ΛΛ}{\rm{He}}$ event named the Nagara event, which is the only uniquely identified double-$Λ$ hypernuclear 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-$Λ$ 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-$Λ$ hypernuclear events recorded in nuclear emulsion sheets by reducing the time required for manual visual inspection by a factor of five hundred. |
| title | A novel machine learning method to detect double-$Λ$ hypernuclear events in nuclear emulsions |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2409.01657 |