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Learning New Physics from an Imperfect Machine
We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties as nuisance parameters for hypothesis testing...
| Autores principales: | , , , , |
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| Lenguaje: | eng |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1140/epjc/s10052-022-10226-y http://cds.cern.ch/record/2791774 |