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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.26273 |
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| _version_ | 1866912618143285248 |
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| author | Borsato, Martino Laganà, Giovanni Martinelli, Maurizio |
| author_facet | Borsato, Martino Laganà, Giovanni Martinelli, Maurizio |
| contents | Cherenkov rings play a crucial role in identifying charged particles in high-energy physics (HEP) experiments. Most Cherenkov ring pattern reconstruction algorithms currently used in HEP experiments rely on a likelihood fit to the photo-detector response, which often consumes a significant portion of the computing budget for event reconstruction. We present a novel approach to Cherenkov ring reconstruction using YOLO, a computer vision algorithm capable of real-time object identification with a single pass through a neural network. We obtain a reconstruction efficiency above 95% and a pion misidentification rate below 5% across a wide momentum range for all particle species. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26273 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rings of Light, Speed of AI: YOLO for Cherenkov Reconstruction Borsato, Martino Laganà, Giovanni Martinelli, Maurizio High Energy Physics - Experiment Cherenkov rings play a crucial role in identifying charged particles in high-energy physics (HEP) experiments. Most Cherenkov ring pattern reconstruction algorithms currently used in HEP experiments rely on a likelihood fit to the photo-detector response, which often consumes a significant portion of the computing budget for event reconstruction. We present a novel approach to Cherenkov ring reconstruction using YOLO, a computer vision algorithm capable of real-time object identification with a single pass through a neural network. We obtain a reconstruction efficiency above 95% and a pion misidentification rate below 5% across a wide momentum range for all particle species. |
| title | Rings of Light, Speed of AI: YOLO for Cherenkov Reconstruction |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2509.26273 |