Exploring SMEFT in VH with Machine Learning

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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.