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

Abstract

Neutrinoless double-beta decay ($0\hat{I}½{\hat{I}}^2{\hat{I}}^2$) 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 $0\hat{I}½{\hat{I}}^2{\hat{I}}^2$. 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 $0.024_{-0.003}^{+0.004}\%$. Our model is being used to accelerate data cleaning development for LEGEND-200 and will serve to improve data cleaning procedures for LEGEND-1000.

17 pages, 13 figures