Neutrino Reconstruction in TRIDENT Based on Graph Neural Network
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909176018501632 |
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| author | Mo, Cen Zhang, Fuyudi Li, Liang |
| author_facet | Mo, Cen Zhang, Fuyudi Li, Liang |
| contents | TRopIcal DEep-sea Neutrino Telescope (TRIDENT) is a next-generation neutrino telescope to be located in the South China Sea. With a large detector volume and the use of advanced hybrid digital optical modules (hDOMs), TRIDENT aims to discover multiple astrophysical neutrino sources and probe all-flavor neutrino physics. The reconstruction resolution of primary neutrinos is on the critical path to these scientific goals. We have developed a novel reconstruction method based on graph neural network (GNN) for TRIDENT. In this paper, we present the reconstruction performance of the GNN-based approach on both track- and shower-like neutrino events in TRIDENT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15324 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neutrino Reconstruction in TRIDENT Based on Graph Neural Network Mo, Cen Zhang, Fuyudi Li, Liang High Energy Physics - Experiment Artificial Intelligence TRopIcal DEep-sea Neutrino Telescope (TRIDENT) is a next-generation neutrino telescope to be located in the South China Sea. With a large detector volume and the use of advanced hybrid digital optical modules (hDOMs), TRIDENT aims to discover multiple astrophysical neutrino sources and probe all-flavor neutrino physics. The reconstruction resolution of primary neutrinos is on the critical path to these scientific goals. We have developed a novel reconstruction method based on graph neural network (GNN) for TRIDENT. In this paper, we present the reconstruction performance of the GNN-based approach on both track- and shower-like neutrino events in TRIDENT. |
| title | Neutrino Reconstruction in TRIDENT Based on Graph Neural Network |
| topic | High Energy Physics - Experiment Artificial Intelligence |
| url | https://arxiv.org/abs/2401.15324 |