Neutrino Reconstruction in TRIDENT Based on Graph Neural Network
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Abstract (arXiv)
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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References · 5
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
- 2021A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatorycited by 13
- 2022Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCubecited by 8
- 2023Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networkscited by 4
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