Categorisation of dark photon jets using machine learning techniques
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Date
2023
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Abstract
This thesis presents a search for Dark Photons decaying into two Hidden Lightest Stable Particles (HLSP) and fermions or light hadrons using ATLAS experiment data from the LHC at a center-of-mass energy of 13 TeV, with an integrated luminosity of 139.0 fb^-1. This study looks to discriminate the dark photon signal produced by a vector-boson-fusion Higgs from all backgrounds using various machine learning techniques. Among the methods tested, XGBoost emerged as the most effective, achieving a MC simulated significance of 5.88 standard deviations. This marked a substantial 22.5% improvement compared to the standard VBF analysis.
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Tesis (Master in theoretical physics)--Pontificia Universidad Católica de Chile, 2023.