GraphNeT: Graph neural networks for neutrino telescope event reconstruction

• URL: http://arxiv.org/abs/2210.12194v1
• Date: Fri, 21 Oct 2022 18:43:50 GMT
• Title: GraphNeT: Graph neural networks for neutrino telescope event reconstruction
• Authors: Andreas S{\o}gaard, Rasmus F. {\O}rs{\o}e, Leon Bozianu, Morten Holm, Kaare Endrup Iversen, Tim Guggenmos, Martin Ha Minh, Philipp Eller and Troels C. Petersen
• Abstract summary: GraphNeT is an open-source python framework to perform reconstruction tasks at neutrino telescopes using graph neural networks (GNNs) GNNs from GraphNeT are flexible enough to be applied to data from all neutrino telescopes, including future projects such as IceCube extensions or P-ONE. This means that GNN-based reconstruction can be used to provide state-of-the-art performance on most reconstruction tasks in neutrino telescopes, at real-time event rates, across experiments and physics analyses, with vast potential impact for neutrino and astro-particle physics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: GraphNeT is an open-source python framework aimed at providing high quality, user friendly, end-to-end functionality to perform reconstruction tasks at neutrino telescopes using graph neural networks (GNNs). GraphNeT makes it fast and easy to train complex models that can provide event reconstruction with state-of-the-art performance, for arbitrary detector configurations, with inference times that are orders of magnitude faster than traditional reconstruction techniques. GNNs from GraphNeT are flexible enough to be applied to data from all neutrino telescopes, including future projects such as IceCube extensions or P-ONE. This means that GNN-based reconstruction can be used to provide state-of-the-art performance on most reconstruction tasks in neutrino telescopes, at real-time event rates, across experiments and physics analyses, with vast potential impact for neutrino and astro-particle physics.

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