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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2408.04795 |
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| _version_ | 1866929616448389120 |
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| author | Kaneko, Fumihiro Kuno, Yoshitaka Sato, Joe Sato, Ikuya Pieters, Dorian Wu, Chen |
| author_facet | Kaneko, Fumihiro Kuno, Yoshitaka Sato, Joe Sato, Ikuya Pieters, Dorian Wu, Chen |
| contents | We present a pioneering approach to tracking analysis within the COMET Phase-I experiment, which aims to search for the charged lepton flavor violating $μ\to e$ conversion process in a muonic atom, at J-PARC, Japan. This paper specifically introduces the extraction of signal electron trajectories in the COMET Phase-I cylindrical drift chamber (CDC) amidst a high background hit rate, with more than $40\,\%$ occupancy of the total CDC cells, utilizing deep learning techniques of semantic segmentation. Our model achieved remarkable results, with a purity rate of $98\,\%$ and a retention rate of $90\,\%$ for CDC cells with signal hits, surpassing the design-goal performance of $90\,\%$ for both metrics. This study marks the initial application of deep learning to COMET tracking, paving the way for more advanced techniques in future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04795 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Extracting Signal Electron Trajectories in the COMET Phase-I Cylindrical Drift Chamber Using Deep Learning Kaneko, Fumihiro Kuno, Yoshitaka Sato, Joe Sato, Ikuya Pieters, Dorian Wu, Chen High Energy Physics - Experiment High Energy Physics - Phenomenology We present a pioneering approach to tracking analysis within the COMET Phase-I experiment, which aims to search for the charged lepton flavor violating $μ\to e$ conversion process in a muonic atom, at J-PARC, Japan. This paper specifically introduces the extraction of signal electron trajectories in the COMET Phase-I cylindrical drift chamber (CDC) amidst a high background hit rate, with more than $40\,\%$ occupancy of the total CDC cells, utilizing deep learning techniques of semantic segmentation. Our model achieved remarkable results, with a purity rate of $98\,\%$ and a retention rate of $90\,\%$ for CDC cells with signal hits, surpassing the design-goal performance of $90\,\%$ for both metrics. This study marks the initial application of deep learning to COMET tracking, paving the way for more advanced techniques in future research. |
| title | Extracting Signal Electron Trajectories in the COMET Phase-I Cylindrical Drift Chamber Using Deep Learning |
| topic | High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2408.04795 |