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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 in⁷⁶Ge to achieve a half-life sensitivity of 10²⁷(10²⁸) 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 ${0.024}_{-0.003}^{+0.004}\mathrm{\%}$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. 

76Ge can ββ decay into three possible excited states of 76Se, with the emission of two or, if the neutrino is Majorana, zero neutrinos. None of these six transitions have yet been observed. The Majorana Demonstrator was designed to study ββ decay of 76Ge using a low background array of high purity germanium detectors. With 98.2 kg-y of isotopic exposure, the Demonstrator sets the strongest half-life limits to date for all six transition modes. For 2νββ to the 0+ state of 76Se, this search has begun to probe for the first time half-life values predicted using modern many-body nuclear theory techniques, setting a limit of T_1/2 > 1.5e24 y (90% CL).