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This content will become publicly available on March 17, 2026

Title: Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines
Abstract Neutrinoless double-beta decay ( 0 ν β β ) 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 ν β β . 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 % 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.  more » « less
Award ID(s):
1812374
PAR ID:
10610890
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ;
Publisher / Repository:
IOP Publishing
Date Published:
Journal Name:
Machine Learning: Science and Technology
Volume:
6
Issue:
1
ISSN:
2632-2153
Page Range / eLocation ID:
015064
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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