Hybrid-graph neural network method for muon fast reconstruction in neutrino telescopes

Abstract

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°, 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.

References in corpus (9)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Evidence for High-Energy Extraterrestrial Neutrinos at the IceCube Detector
• Neutrino emission from the direction of the blazar TXS 0506+056 prior to the IceCube-170922A alert
• Letter of Intent for KM3NeT 2.0
• Evidence for neutrino emission from the nearby active galaxy NGC 1068
• ParticleNet: Jet Tagging via Particle Clouds
• A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory
• Event reconstruction for KM3NeT/ORCA using convolutional neural networks
• Exploring a PMT+SiPM Hybrid Optical Module for Next Generation Neutrino Telescopes