Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908327479345152 |
|---|---|
| author | León, E. Li, A. Schott, M. A. Bahena Bos, B. Busch, M. Chapman, J. R. Duran, G. L. Gruszko, J. Henning, R. Martin, E. L. Wilkerson, J. F. |
| author_facet | León, E. Li, A. Schott, M. A. Bahena Bos, B. Busch, M. Chapman, J. R. Duran, G. L. Gruszko, J. Henning, R. Martin, E. L. Wilkerson, J. F. |
| contents | Neutrinoless double-beta decay ($0νββ$) 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 $0νββ$. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14701 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines León, E. Li, A. Schott, M. A. Bahena Bos, B. Busch, M. Chapman, J. R. Duran, G. L. Gruszko, J. Henning, R. Martin, E. L. Wilkerson, J. F. Data Analysis, Statistics and Probability Nuclear Experiment Instrumentation and Detectors Neutrinoless double-beta decay ($0νββ$) 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 $0νββ$. 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. |
| title | Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines |
| topic | Data Analysis, Statistics and Probability Nuclear Experiment Instrumentation and Detectors |
| url | https://arxiv.org/abs/2410.14701 |