Legacy Analysis of Standard Model Higgs boson in the H → W W ∗ → ℓ− ν ℓ′+ν decay channel from pp collisions at √s = 13 TeV with the ATLAS detector at the LHC

Abstract

The 2012 discovery of a Standard Model-like Higgs boson at CERN’s Large Hadron Collider (LHC) marked a pivotal moment in high-energy physics. In the years that have followed, the Higgs boson’s properties and role within the Standard Model have been refined through increasingly precise measurements of this particle in a variety of final statesand kinematic regimes. This thesis contributes to this ongoing exploration by presenting a legacy analysis of the Standard Model Higgs boson production cross sections via gluon-gluon fusion, focusing on the previously unexplored 2-jet same-flavor leptonic final state, utilizing the H → W W ∗ decay mode. The analysis leverages data from proton-proton collisions at√s = 13 TeV, collected by the ATLAS detector during the LHC’s Run 2, comprising an integrated luminosity of 139 fb−1. This work implements Deep Neural Network techniques to enhance signal identification and background suppression, rather than using mT as the discriminant variable. The innovative DNN-based approach presented here aims to deepen the understanding of the Higgs sector, optimize the ggF production process analysis, and provide new insights into the interactions of the Higgs boson with the Standard Model. Employing DNN as the discriminant variable in the ggF channels significantly improved background suppression. The inclusion of the ggF 2-jet same-flavor leptonic state improved the cross-section sensitivity by 15% in the ggF inclusive study and by 25% in the ggF 2jet channel.