A Search for Additional Heavy Higgs Bosons Decaying to Top Quarks in the Full LHC Run-2 Dataset

Supervisors: Dr. Katharina Behr (UHH), Prof. Kerstin Tackmann (DESY, UHH), Prof. Peter Schleper (UHH)

The majority of LHC searches for new particles and interactions relies on the assumption that a new particle would manifest itself as a localised peak on top of a smoothly falling distribution of background events. In the absence of such localised deviations in the LHC data, it is reasonable to search for new physics by investigating the presence of strong interference effects between the new signal and the dominant background processes.
New pseudoscalar and scalar states coupling strongly to top-antitop pairs are predicted by a class of models in which the Higgs sector is extended to include a second Higgs doublet. These models are motivated by many theories beyond the Standard Model (SM), such as supersymmetry and axion models. If the mass of the new states is higher than 500 GeV and the ratio of the vacuum expectation values of the two Higgs fields is small (less than 3), these states decay predominantly into topantitop pairs. This process would interfere strongly with top-antitop quark production via SM processes, producing a specific peak-dip structure, and therefore cannot be identified easily with traditional analysis techniques.
This thesis project aims at the search for heavy pseudoscalar and scalar Higgs bosons decaying into a top quark pair, using the full Run-2 dataset in the ATLAS experiment. New analysis tools will be developed such as novel reconstruction and identification approaches for semi-boosted top quarks, to improve the sensitivity to narrow interference patterns and to heavy Higgs boson with masses above 600 GeV, a mass region completely unexplored to date.
An accompanying technical project will aim to improve the reconstruction of charged particle trajectories in the high-intensity collisions during the upcoming LHC Run-3 and the High-Luminosity LHC from 2026. The effects of radiation damage and tuning on the detector will be taken into consideration and advanced analysis techniques such as machine learning will be explored.