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Learning new physics efficiently with nonparametric methods
We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original p...
| Autores principales: | , , , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1140/epjc/s10052-022-10830-y http://cds.cern.ch/record/2807356 |