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Talk
Title	Exploring SMEFT in VH channel with Machine Learning
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Author(s)	Khosa, Charanjit Kaur (speaker)
Corporate author(s)	CERN. Geneva
Imprint	2019-04-16. - 0:23:18.
Series	(LPCC Workshops) (3rd IML Machine Learning Workshop)
Lecture note	on 2019-04-16T15:00:00
Subject category	LPCC Workshops
Abstract	We use Machine Learning(ML) techniques to exploit kinematic information in VH, the production of a Higgs in association with a massive vector boson. We parametrize 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 parametrizing systematic uncertainties. Our results show the potential of incorporating Machine Learning techniques to the SMEFT studies using the current datasets.
Copyright/License	© 2019-2024 CERN
Submitted by	paul.seyfert@cern.ch