Neutrino Reconstruction in TRIDENT Based on Graph Neural Network

Cen Mo, Fuyudi Zhang, Liang Li
1/27/2024
hep-excs.AI

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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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Cite this paper

@article{mo2024neutrino,
  title  = {Neutrino Reconstruction in TRIDENT Based on Graph Neural Network},
  author = {Cen Mo and Fuyudi Zhang and Liang Li},
  year   = {2024},
  eprint = {2401.15324},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.15324v1}
}

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