Enhancements to the IceCube Extremely High Energy Neutrino Selection using Graph & Transformer Based Neural Networks

KM3NeT has recently reported the detection of a very high-energy neutrino event, while IceCube has previously set upper limits on the differential neutrino flux above 100 PeV but has yet to observe a neutrino event with an energy comparable to that of the KM3NeT detection. To improve diffuse measurements above 10 PeV, we apply machine learning techniques to enhance atmospheric muon background rejection and directional reconstruction. We utilize a Graph Neural Network (GNN) to perform a classification task that distinguishes neutrinos from high-energy atmospheric muons. The method allows for the rejection of early hits from laterally spread, lower-energy muons in cosmic ray showers without relying on directional reconstruction as a prior. Additionally, a Transformer-based Neural Network is implemented for directional reconstruction. Unlike previous likelihood-based rapid reconstruction algorithms that assume a single muon track, this method makes no prior assumptions about event topology of the particle inside the detector. We demonstrate improved background rejection and reconstruction performance using machine learning techniques. Applications to the development of future Extremely High Energy (EHE) selections are also discussed.

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Publication Details

Published
2025-09-24
DOI
https://doi.org/10.22323/1.501.1127
Primary Topic
Astrophysics and Cosmic Phenomena
Type
article
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0.00

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article

Enhancements to the IceCube Extremely High Energy Neutrino Selection using Graph & Transformer Based Neural Networks

L. Schumacher, L. Köpke, Sofia Athanasiadou, Kiara Carloni et al.
Astrophysics and Cosmic Phenomena
article

Enhancements to the IceCube Extremely High Energy Neutrino Selection using Graph & Transformer Based Neural Networks

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article en

Abstract

KM3NeT has recently reported the detection of a very high-energy neutrino event, while IceCube has previously set upper limits on the differential neutrino flux above 100 PeV but has yet to observe a neutrino event with an energy comparable to that of the KM3NeT detection. To improve diffuse measurements above 10 PeV, we apply machine learning techniques to enhance atmospheric muon background rejection and directional reconstruction. We utilize a Graph Neural Network (GNN) to perform a classification task that distinguishes neutrinos from high-energy atmospheric muons. The method allows for the rejection of early hits from laterally spread, lower-energy muons in cosmic ray showers without relying on directional reconstruction as a prior. Additionally, a Transformer-based Neural Network is implemented for directional reconstruction. Unlike previous likelihood-based rapid reconstruction algorithms that assume a single muon track, this method makes no prior assumptions about event topology of the particle inside the detector. We demonstrate improved background rejection and reconstruction performance using machine learning techniques. Applications to the development of future Extremely High Energy (EHE) selections are also discussed.

National Science Foundation, U.S. Department of Energy, University of Wisconsin-Madison, Nvidia, Michigan State University, Villum Fonden, Polarforskningssekretariatet, Marquette University, Western Canada Research Grid, European Commission, National Research Foundation, Deutsche Forschungsgemeinschaft, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Fonds De La Recherche Scientifique - FNRS, Belgian Federal Science Policy Office, Fonds Wetenschappelijk Onderzoek, National Research Foundation of Korea, Knut och Alice Wallenbergs Stiftelse, Vetenskapsrådet, RWTH Aachen University, Chiba University, Helmholtz Alliance for Astroparticle Physics, Alliance de recherche numérique du Canada, College of Engineering, Michigan State University, Natural Sciences and Engineering Research Council of Canada, Deutsches Elektronen-Synchrotron, Japan Society for the Promotion of Science, Marsden Fund, Institute for Global Prominent Research, Chiba University, Office of Polar Programs, Office of Advanced Cyberinfrastructure, Office of Experimental Program to Stimulate Competitive Research
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