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arXiv · 2410.14701

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

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
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Abstract

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, 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 $0.024 ^{+0.004}_{-0.003} \%$. Our model is being used to accelerate data cleaning development for LEGEND-200 and will serve to improve data cleaning procedures for LEGEND-1000.

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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. 2024-10-05. Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines. https://doi.org/10.1088/2632-2153/adbb37

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