Exploring SMEFT in VH with Machine Learning

Abstract

In this paper we study the use of Machine Learning techniques to exploit kinematic information in VH, the production of a Higgs in association with a massive vector boson. We parametrise the effect of new physics in terms of the SMEFT framework. We find that the use of a shallow neural network allows us to dramatically increase the sensitivity to deviations in VH respect to previous estimates. We also discuss the relation between the usual measures of performance in Machine Learning, such as AUC or accuracy, with the more adept measure of Asimov significance. This relation is particularly relevant when parametrising systematic uncertainties. Our results show the potential of incorporating Machine Learning techniques to the SMEFT studies using the current datasets.

7 pages, 5 figures

References in corpus (6)

• FeynRules - Feynman rules made easy
• Complete Higgs Sector Constraints on Dimension-6 Operators
• The Gauge-Higgs Legacy of the LHC Run II
• Electroweak Sector Under Scrutiny: A Combined Analysis of LHC and Electroweak Precision Data
• Jet substructure and probes of CP violation in Vh production
• Constraints on models for the Higgs boson with exotic spin and parity in $\mathbf{VH}\to \mathbf{Vb}\bar{\mathbf{b}}$ final states

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