Citizen science to enhance sub-GeV neutrino searches in IceCube
Machine learning has become a vital part of analysis in modern neutrino astronomy, and many recent discoveries would not be possible without it. This approach, however, is limited by the quality of available training data. Located at the South Pole, the IceCube Neutrino Observatory is a neutrino detector sensitive to astrophysical neutrinos from GeV to PeV energies, with ongoing efforts to push the sensitivity down to 100 MeV for neutrinos from transient events. IceCube is dominated by massive backgrounds, detecting more than 10 billion atmospheric muons for each astrophysical neutrino, and machine learning is a powerful tool to reduce this large background rate. However, undetected outliers in labelled training data negatively affect the final performance of machine learning algorithms. Citizen scientists can help to quantify and qualify outliers in IceCube data to improve the detection of such outliers. In this contribution, we present the ongoing efforts of utilising citizen science to improve a machine-learning-based event selection targeting sub-GeV astrophysical neutrinos.
Authors
- G. de Wasseige (ORCID: https://orcid.org/0000-0002-1010-5100)
Publication Details
- Published
- 2025-09-23
- DOI
- https://doi.org/10.22323/1.501.1262
- Primary Topic
- Astrophysics and Cosmic Phenomena
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- 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