Learning New Physics from an Imperfect Machine

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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...

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Detalles Bibliográficos
Autores principales: d'Agnolo, Raffaele Tito, Grosso, Gaia, Pierini, Maurizio, Wulzer, Andrea, Zanetti, Marco
Lenguaje:eng
Publicado: 2021
Materias:
hep-ph
Particle Physics - Phenomenology
Acceso en línea:https://dx.doi.org/10.1140/epjc/s10052-022-10226-y
http://cds.cern.ch/record/2791774
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