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DOE OSTI · 3001827

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

León, E. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States); Analysis Group, Boston, MA (United States)] (ORCID:0000000200735512)·Li, A. [Univ. of California, San Diego, CA (United States)] (ORCID:0000000248449339)·Bahena Schott, M. A. [University of North Carolina, Chapel Hill, NC (United States)] (ORCID:0000000178928691)·Bos, B. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States)] (ORCID:0009000858281745)·Busch, M. [Duke Univ., Durham, NC (United States). Triangle Universities Nuclear Laboratory] (ORCID:0009000293363937)·Chapman, J. R. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States)] (ORCID:0009000498152981)·Duran, G. L. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States)] (ORCID:000900013047478X)·Gruszko, J. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States)] (ORCID:0000000237772237)·Henning, R. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States)] (ORCID:0000000186512960)·Martin, E. L. [Duke Univ., Durham, NC (United States). Triangle Universities Nuclear Laboratory] (ORCID:0000000250081596)·Wilkerson, J. F. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States)] (ORCID:0000000203420217)
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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, 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.

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artificial intelligencedata cleaninggermanium detectorsmachine learningneutrinoless double beta decay

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León, E. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States); Analysis Group, Boston, MA (United States)] (ORCID:0000000200735512), Li, A. [Univ. of California, San Diego, CA (United States)] (ORCID:0000000248449339), Bahena Schott, M. A. [University of North Carolina, Chapel Hill, NC (United States)] (ORCID:0000000178928691), Bos, B. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States)] (ORCID:0009000858281745), Busch, M. [Duke Univ., Durham, NC (United States). Triangle Universities Nuclear Laboratory] (ORCID:0009000293363937), Chapman, J. R. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States)] (ORCID:0009000498152981), Duran, G. L. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States)] (ORCID:000900013047478X), Gruszko, J. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States)] (ORCID:0000000237772237), Henning, R. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States)] (ORCID:0000000186512960), Martin, E. L. [Duke Univ., Durham, NC (United States). Triangle Universities Nuclear Laboratory] (ORCID:0000000250081596), Wilkerson, J. F. [University of North Carolina, Chapel Hill, NC (United States); Triangle Universities Nuclear Laboratory (TUNL), Durham, NC (United States)] (ORCID:0000000203420217). 2025-03-17. 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%2Fadbb37

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