Machine learning driven reconstruction of cosmic-ray air showers for next generation radio arrays

Surface radio antenna-based measurements of cosmic-ray air showers present significant computational challenges in accurately reconstructing physics observables, in particular, the depth of shower maximum, X$_{max}$. State-of-the-art template fitting methods rely on extensive simulation libraries, limiting scalability. This work introduces a technique utilizing graph neural networks to reconstruct key air-shower parameters, in particular, direction and shower-core, energy, and X$_{max}$. For training and testing of the networks, we use a CoREAS simulation library made for a future enhancement of IceCube’s surface array with radio antennas. The neural networks provide a scalable framework for large-scale data analysis for next-generation astroparticle observatories, such as IceCube-Gen2.

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Publication Details

Published
2025-09-23
DOI
https://doi.org/10.22323/1.501.0309
Primary Topic
Astrophysics and Cosmic Phenomena
Type
article
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article

Machine learning driven reconstruction of cosmic-ray air showers for next generation radio arrays

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

Machine learning driven reconstruction of cosmic-ray air showers for next generation radio arrays

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article en

Abstract

Surface radio antenna-based measurements of cosmic-ray air showers present significant computational challenges in accurately reconstructing physics observables, in particular, the depth of shower maximum, X$_{max}$. State-of-the-art template fitting methods rely on extensive simulation libraries, limiting scalability. This work introduces a technique utilizing graph neural networks to reconstruct key air-shower parameters, in particular, direction and shower-core, energy, and X$_{max}$. For training and testing of the networks, we use a CoREAS simulation library made for a future enhancement of IceCube’s surface array with radio antennas. The neural networks provide a scalable framework for large-scale data analysis for next-generation astroparticle observatories, such as IceCube-Gen2.

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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