Doctor of Philosophy in Physics (PhD)
Searching for evidence of Heavy Neutral Leptons in the LHC with the ATLAS Experiment
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The standard model of particle physics (SM) describes all the fundamental particles and their interactions. It is a very successful theory; however, many experimental observations - such as the origin of neutrino mass, particle origin of dark matter, e.t.c. - are either not consistent or not explained by the SM. So, it is inevitable that there has to be a (physics) model beyond the SM which will consistently explain all these observations, not covered by the SM. Several of such extensions predict new heavy particles that can interact with SM particles. This dissertation presents searches for the resonant production of such high-mass particles in dielectron and top-antitop final states. These searches use proton-proton collision data at the center-of-mass energy of 13 TeV collected by the ATLAS detector at the Large Hadron Collider (LHC) between 2015 and 2018. Electrons are stable and easy to reconstruct, but top-quarks decay instantaneously. Two dominant top-decay final states, all-hadronic and semi-leptonic, are studied in this dissertation. The combined mass distributions of all the final-state particles are used to perform model-dependent and model-independent statistical searches. No evidence for the existence of new particles is found in any of the explored final states. Hence, upper limits on production cross-section times branching ratio and lower limits on the mass of heavy Z' particles, predicted by the BSM models, are placed at a 95% confidence level. The dilepton resonance search excludes Z' boson below 3.6 TeV. The resonance search in the boosted all-hadronic top-antitop final state excludes Z' bosons with a mass lower than 4.1 TeV. Whereas in the semi-leptonic search, the same signal is expected to be excluded up to 3.6 TeV.The dissertation also presents a new algorithm for splitting the merged charge clusters in the ATLAS pixel detector, based on a Mixture Density Network (MDN). The performance of this new algorithm is found to be better than the existing algorithm. As a result, the MDN-based algorithm is expected to be used as a default algorithm in ATLAS during the next data collection period, which will start in 2022.
The precise understanding of elementary particle properties and theory parameters predicted by the Standard Model of Particle Physics (SM) as well as the revelation of new physics phenomena beyond the scope of that successful theory are at the heart of modern fundamental particle physics research. The Large Hadron Collider (LHC) and modern particle detectors provide the key to probing nature at energy scales never achieved in an experimental controlled setup before. The assumption that the SM describes nature only up to a certain energy scale Λ can be relaxed if new particles are present. This helps in particular with the so called "fine-tuning" problem which requires large corrections -- in the SM -- to the bare mass of the Higgs boson in order to be consistent with the observed mass. A possible solution to this problem is the existence of partner particles of the heaviest known fundamental particle, the top-quark. The new partner particles are expected to be up to ten times heavier. Popular examples of theories predicting heavier top-quark partners are supersymmetric theories and theories that add an additional quark sector to the SM which might be a result of an additional spontaneously broken global symmetry. This dissertation documents two searches for heavy top-quark partners, namely vector-like quarks (VLQs), based on the proton proton pp collision data collected in 2015 and 2016, corresponding to an integrated luminosity of 36.1 fb-¹ at a center of mass energy of 13 TeV. It also elaborates on the work that contributed to a successful data taking campaign related to the alignment of the inner most part of the ATLAS detector with emphasis on the identification and mitigation of track parameter biases.No signs for VLQs were found. The strongest lower mass limits on the pair-produced VLQs decaying into W bosons and top- or bottom-quarks are set to 1.35 TeV at the 95% Confidence Interval exceeding the one TeV scale for the first time. In addition, the analyses were re-interpreted for other expected VLQ decay signatures.
With the mass of the discovered Higgs-like boson being 125 GeV, this leads to a primary Higgs decay mode to two bottom (b) jets. A precise measurement of top-pair (tt̄) production in conjunction with two additional b-jets is essential to reduce the background uncertainty on the tt̄ + Higgs production cross-section, a direct probe of the Higgs to Yukawa coupling. This thesis attempts to improve on the statistical sensitivity of tt̄ production in conjunction with two additional heavy-flavour jets, using expected sensitivities from 20.3 fb-¹ of pp collision data at √s = 8TeV, collected by the ATLAS detector at the Large Hadron Collider in 2012. This thesis compares multiple multivariate analysis techniques, boosted decision trees and artificial neural networks, in both binary and multi-class classification cases. An overall improvement in precision was seen, from 19.7% uncertainty on the baseline tt̄ + bb̄ measurement based on a fit to the best single variable, to 16.1% uncertainty with the very best multi-class neural network algorithm. This represents a relative improvement of nearly 20% and could thus reduce luminosity needed for a precision measurement of this process.