Performance Study of the IceCube Upgrade Camera System

The IceCube Upgrade Camera System is a novel calibration system designed to calibrate the IceCube detector by measuring the optical properties of the Antarctic ice. The system comprises nearly 2,000 cameras and illumination LEDs, which are present on every D-Egg and mDOM—the newly designed optical modules for the IceCube Upgrade. These units, deployed across the IceCube Upgrade volume, will capture transmission and reflection images that can be used to characterize the optical properties of both the refrozen ice within drill holes and the bulk ice between strings. Additionally, the images can aid in determining the positions of the optical modules they are mounted on. To maximize the system’s performance, various image analysis methodologies have been explored, ranging from classical maximum likelihood estimation to AI-based approaches using neural networks. In this study, we present preliminary results on the performance of these methods based on images generated by a simulation tool developed specifically for this system.

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Published
2025-09-24
DOI
https://doi.org/10.22323/1.501.1069
Primary Topic
Radio Wave Propagation Studies
Type
article
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article

Performance Study of the IceCube Upgrade Camera System

L. Schumacher, L. Köpke, Sofia Athanasiadou, Kiara Carloni et al.
Radio Wave Propagation Studies
article

Performance Study of the IceCube Upgrade Camera System

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article en

Abstract

The IceCube Upgrade Camera System is a novel calibration system designed to calibrate the IceCube detector by measuring the optical properties of the Antarctic ice. The system comprises nearly 2,000 cameras and illumination LEDs, which are present on every D-Egg and mDOM—the newly designed optical modules for the IceCube Upgrade. These units, deployed across the IceCube Upgrade volume, will capture transmission and reflection images that can be used to characterize the optical properties of both the refrozen ice within drill holes and the bulk ice between strings. Additionally, the images can aid in determining the positions of the optical modules they are mounted on. To maximize the system’s performance, various image analysis methodologies have been explored, ranging from classical maximum likelihood estimation to AI-based approaches using neural networks. In this study, we present preliminary results on the performance of these methods based on images generated by a simulation tool developed specifically for this system.

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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Radio Wave Propagation Studies
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