Neutrino Reconstruction in TRIDENT Based on Graph Neural Network

• URL: http://arxiv.org/abs/2401.15324v1
• Date: Sat, 27 Jan 2024 06:57:24 GMT
• Title: Neutrino Reconstruction in TRIDENT Based on Graph Neural Network
• Authors: Cen Mo, Fuyudi Zhang, Liang Li
• Abstract summary: TRopIcal DEep-sea Neutrino Telescope (TRIDENT) is a next-generation neutrino telescope to be located in the South China Sea.
• Score: 6.301679261460378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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