Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines

Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines

Neutrinoless double-beta decay ( $0\nu\beta\beta$ ) is a rare nuclear process that, if observed, will provide insight into the nature of neutrinos and help explain the matter-antimatter asymmetry in the Universe. The large enriched germanium experiment for neutrinoless double-beta decay (LEGEND) wil...

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Bibliographic Details
Main Authors: E León, A Li, M A Bahena Schott, B Bos, M Busch, J R Chapman, G L Duran, J Gruszko, R Henning, E L Martin, J F Wilkerson
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Machine Learning: Science and Technology
Subjects:
data cleaning
germanium detectors
neutrinoless double beta decay
machine learning
artificial intelligence
Online Access:https://doi.org/10.1088/2632-2153/adbb37
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Summary:Neutrinoless double-beta decay ( $0\nu\beta\beta$ ) is a rare nuclear process that, if observed, will provide insight into the nature of neutrinos and help explain the matter-antimatter asymmetry in the Universe. The large enriched germanium experiment for neutrinoless double-beta decay (LEGEND) will operate in two phases to search for $0\nu\beta\beta$ . The first (second) stage will employ 200 (1000) kg of High-Purity Germanium (HPGe) enriched in ^76 Ge to achieve a half-life sensitivity of 10 ^27 (10 ^28 ) years. In this study, we present a semi-supervised data-driven approach to remove non-physical events captured by HPGe detectors powered by a novel artificial intelligence model. We utilize affinity propagation to cluster waveform signals based on their shape and a support vector machine to classify them into different categories. We train, optimize, and test our model on data taken from a natural abundance HPGe detector installed in the Full Chain Test experimental stand at the University of North Carolina at Chapel Hill. We demonstrate that our model yields a maximum sacrifice of physics events of $0.024 ^{+0.004}_{-0.003} \%$ after data cleaning. Our model is being used to accelerate data cleaning development for LEGEND-200 and will serve to improve data cleaning procedures for LEGEND-1000.
ISSN:2632-2153

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