Neutrino Reconstruction in TRIDENT Based on Graph Neural Network (2401.15324v1)
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.
- R. Abbasi et al., “Evidence for high-energy extraterrestrial neutrinos at the icecube detector,” Science, vol. 342, no. 6161, p. 1242856, 2013.
- R. Abbasi et al., “Neutrino emission from the direction of the blazar txs 0506+056 prior to the icecube-170922a alert,” Science, vol. 361, no. 6398, pp. 147–151, 2018.
- R. Abbasi et al., “Evidence for neutrino emission from the nearby active galaxy ngc 1068,” Science, vol. 378, no. 6619, pp. 538–543, 2022.
- L. Wille and D. Xu, “Astrophysical tau neutrino identification with icecube waveforms,” 2019.
- R. Abbasi et al., “A convolutional neural network based cascade reconstruction for the IceCube neutrino observatory,” Journal of Instrumentation, vol. 16, no. 7, p. P07041, 2021.
- F. J. Yu, J. Lazar, and C. A. Argüelles, “Trigger-level event reconstruction for neutrino telescopes using sparse submanifold convolutional neural networks,” 2023. arXiv:2303.08812.
- R. Abbasi et al., “Graph neural networks for low-energy event classification & reconstruction in IceCube,” Journal of Instrumentation, vol. 17, p. P11003, nov 2022.
- K. Melis, A. Heijboer, and M. de Jong, “KM3NeT/ARCA Event Reconstruction Algorithms,” PoS, vol. ICRC2017, p. 950, 2018.
- S. Aiello et al., “Event reconstruction for KM3net/ORCA using convolutional neural networks,” Journal of Instrumentation, vol. 15, pp. P10005–P10005, oct 2020.
- T. Huege, “Corsika 8 – the next-generation air shower simulation framework,” 2022. arXiv:2208.14240.
- C. Bierlich et al., “A comprehensive guide to the physics and usage of pythia 8.3,” 2022. arXiv:2203.11601.
- S. Blyth, “Opticks : GPU Optical Photon Simulation for Particle Physics using NVIDIA® OptiXTM,” EPJ Web Conf., vol. 214, p. 02027, 2019.
- H. Qu and L. Gouskos, “Jet tagging via particle clouds,” Physical Review D, vol. 101, mar 2020.
- A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32, pp. 8024–8035, Curran Associates, Inc., 2019.
- M. Fey and J. E. Lenssen, “Fast graph representation learning with PyTorch Geometric,” in ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019.
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