Neutrino Reconstruction in TRIDENT Based on Graph Neural Network
This work addresses neutrino reconstruction for astrophysical source discovery and neutrino physics in the TRIDENT telescope, but it appears incremental as it applies an existing GNN method to a new domain.
The paper tackles neutrino reconstruction in the TRIDENT telescope using a graph neural network (GNN) method, achieving improved resolution for track- and shower-like neutrino events, though no specific numbers are provided.
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.