Fast and accurate muon reconstruction is crucial for neutrino telescopes to improve experimental sensitivity and enable online triggering. This paper introduces a hybrid-graph neural network (GNN) method tailored for efficient muon track reconstruction, leveraging the robustness of GNNs, alongside traditional physics-based approaches. The "light GNN model" achieves a run-time of 0.19-0.29 ms per event on GPUs, offering a 3 orders of magnitude speedup compared to traditional likelihood-based methods, while maintaining a high reconstruction accuracy. For high-energy muons (10-100 TeV), the median angular error is approximately 0.1{\deg}, with errors in reconstructed Cherenkov photon emission positions being below 3-5 m, depending on the GNN model used. Furthermore, the semi-GNN method offers a mechanism to assess the quality of event reconstruction, enabling the identification and exclusion of poorly reconstructed events. These results establish the GNN-based approach as a promising solution for next-generation neutrino telescope data reconstruction.
@article{arxiv.2505.23425,
title = {Hybrid-graph neural network method for muon fast reconstruction in neutrino telescopes},
author = {Cen Mo and Liang Li},
journal= {arXiv preprint arXiv:2505.23425},
year = {2025}
}