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• W2912351279 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." @default.
• W2912351279 created "2019-02-21" @default.
• W2912351279 creator A5023269417 @default.
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• W2912351279 date "2019-08-30" @default.
• W2912351279 modified "2023-09-27" @default.
• W2912351279 title "Exploring the standard model EFT in <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline><mml:mi>V</mml:mi><mml:mi>H</mml:mi></mml:math> production with machine learning" @default.
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• W2912351279 doi "https://doi.org/10.1103/physrevd.100.035040" @default.
• W2912351279 hasPublicationYear "2019" @default.
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