# Exploring SMEFT in VH with Machine Learning

## TL;DR

This paper demonstrates that shallow neural networks significantly enhance sensitivity to new physics effects in VH Higgs production within the SMEFT framework, highlighting the synergy between machine learning and particle physics analyses.

## Contribution

It introduces the application of shallow neural networks to SMEFT analyses in VH production, improving sensitivity and relating ML performance metrics to physics significance measures.

## Key findings

- Neural networks increase sensitivity to SMEFT deviations in VH production.
- A relation between AUC, accuracy, and Asimov significance is established.
- Results show potential for ML techniques in current SMEFT datasets.

## 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.