Unfolding the Atmospheric Muon Flux with IceCube: Investigating Stopping Muons and High-Energy Prompt Contributions

Atmospheric muons produced in cosmic-ray air showers are classified as conventional muons from pion and kaon decays and prompt muons from heavy hadron decays. Conventional muons dominate at lower energies, and the prompt component becomes dominant at PeV energies and above. Precisely measuring the atmospheric muon flux from a few GeV to several PeV is valuable for advancing our understanding of cosmic-ray interactions and testing hadronic interaction models. Low-energy muons that stop within the IceCube in-ice array provide valuable information about the energy spectrum of muons from a few hundred GeV up to 10 TeV. Machine learning techniques are employed to enhance event reconstruction and selection to provide insights into the conventional and prompt components. This contribution presents the unfolding of the energy spectrum of stopping muons in IceCube as well as the unfolding of high-energy muons to probe the prompt component.

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Published
2025-09-23
DOI
https://doi.org/10.22323/1.501.0281
Primary Topic
Astrophysics and Cosmic Phenomena
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article
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article

Unfolding the Atmospheric Muon Flux with IceCube: Investigating Stopping Muons and High-Energy Prompt Contributions

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

Unfolding the Atmospheric Muon Flux with IceCube: Investigating Stopping Muons and High-Energy Prompt Contributions

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article en

Abstract

Atmospheric muons produced in cosmic-ray air showers are classified as conventional muons from pion and kaon decays and prompt muons from heavy hadron decays. Conventional muons dominate at lower energies, and the prompt component becomes dominant at PeV energies and above. Precisely measuring the atmospheric muon flux from a few GeV to several PeV is valuable for advancing our understanding of cosmic-ray interactions and testing hadronic interaction models. Low-energy muons that stop within the IceCube in-ice array provide valuable information about the energy spectrum of muons from a few hundred GeV up to 10 TeV. Machine learning techniques are employed to enhance event reconstruction and selection to provide insights into the conventional and prompt components. This contribution presents the unfolding of the energy spectrum of stopping muons in IceCube as well as the unfolding of high-energy muons to probe the prompt component.

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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Astrophysics and Cosmic Phenomena
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