https://doi.org/10.1140/epjs/s11734-024-01280-6
Review
Machine learning in experimental neutrino physics
Department of Physics, Indian Institute of Technology, 208016, Kanpur, UP, India
Received:
11
April
2024
Accepted:
16
July
2024
Published online:
19
August
2024
Neutrino physics has entered into the era of precision measurements. Over the last two decades, significant efforts have been made to measure precise parameters of the PMNS matrix, which describes neutrino oscillation phenomena. The next generation neutrino experiment will prioritize measuring leptonic CP-violation, potentially revealing the matter–antimatter asymmetry of the universe. Technological advancements will enable faster and more precise measurements. This article describes how neutrino experiments, will utilize machine learning techniques to identify and reconstruct different neutrino event topology in detectors. This approach promises unprecedented measurements of neutrino oscillation parameters.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
