Phenomenology of Scalar Particles Assisted by Machine Learning

Abstract

In this thesis, we explore the phenomenology of scalar particles within Beyond Standard Model (BSM) frameworks, using Machine Learning techniques to enhance sensitivity and discovery potential at current and future collider experiments, the Large Hadron Collider (LHC) and the High-Luminosity LHC (HL-LHC). Specifically, we study scalar extensions of the Standard Model (SM) such as the Two Higgs Doublet Model Type-III (2HDM-III) and the Froggatt-Nielsen Flavon model.

We perform a detailed collider analysis focusing on charged Higgs boson pair production within the 2HDM-III, examining final states involving muons, neutrinos and quark jets. Our studies identify parameter regions consistent with recent experimental anomalies reported by the A Toroidal LHC Apparatus (ATLAS) collaboration, particularly in charged Higgs decays involving charm-bottom quark transitions, and suggest concrete scenarios for achieving statistically significant signals of 5 at future luminosities.

In the context of the Flavon model, we analyse potential signatures of a new scalar called Flavon decaying into a Higgs boson and a pair of bottom quarks, followed by the channels where the Higgs decays into a pair of bottom quarks or a pair of photons. Additionally, we analyse Lepton-Flavour-Violating (LFV) processes, both of them achieving discovery level significances of up to at the HL-LHC.

Using multivariate analysis techniques, specifically Boosted Decision Trees (BDTs), we demonstrate a significant improvement in signal discrimination. Throughout this thesis, Machine Learning methodologies have been integral, notably enhancing the signal from background separation and significantly improving the robustness of phenomenological predictions. The methods and analyses presented here contribute to clarifying the flavour structure mysteries of the SM and offer actionable targets for future experimental searches.