This PhD thesis focuses on the search for flavor-changing neutral currents in the decay of a top quark to an up-type quark (q = u, c) and the Standard Model Higgs boson, where the Higgs boson decays to bb. Further, the thesis presents the combination of this search for top quark pair events with other ATLAS searches – in the course of which the most restrictive bounds to date on tqH interactions were obtained. Following on from the discovery of the Higgs boson, it is particularly important to measure the Yukawa couplings of the Standard Model fermions; these parameters may provide crucial insights to help solve the flavor puzzle and may help reveal the presence of new physics before it is directly observed in experiments.

The top quark is by far the heaviest known fundamental particle with a mass nearing that of a gold atom. Because of this strikingly high mass, the top quark has several unique properties and might play an important role in electroweak symmetry breaking—the mechanism that gives all elementary particles mass. Creating top quarks requires access to very high energy collisions, and at present only the Tevatron collider at Fermilab is capable of reaching these energies. Until now, top quarks have only been observed produced in pairs via the strong interaction. At hadron colliders, it should also be possible to produce single top quarks via the electroweak interaction. Studies of single top quark production provide opportunities to measure the top quark spin, how top quarks mix with other quarks, and to look for new physics beyond the standard model. Because of these interesting properties, scientists have been looking for single top quarks for more than 15 years. This thesis presents the first discovery of single top quark production. It documents one of the flagship measurements of the D0 experiment, a collaboration of more than 600 physicists from around the world. It describes first observation of a physical process known as “single top quark production”, which had been sought for more than 10 years before its eventual discovery in 2009. Further, his thesis describes, in detail, the innovative approach Dr. Gillberg took to this analysis. Through the use of Boosted Decision Trees, a machine-learning technique, he observed the tiny single top signal within an otherwise overwhelming background. This Doctoral Thesis has been accepted by Simon Fraser University, Burnaby, BC, Canada.

This will be a required acquisition text for academic libraries. More than ten years after its discovery, still relatively little is known about the top quark, the heaviest known elementary particle. This extensive survey summarizes and reviews top-quark physics based on the precision measurements at the Fermilab Tevatron Collider, as well as examining in detail the sensitivity of these experiments to new physics. Finally, the author provides an overview of top quark physics at the Large Hadron Collider.

It is known that the LHC has a considerable discovery potential because of its large centre-of-mass energy (vs =14 TeV) and the high design luminosity. In addition, the two experiments ATLAS and CMS perform precision measurements for numerous models in physics. The increasing experimental precision demands an even higher level of accuracy on the theoretical side. For a more precise prediction of outcomes, one has to consider the corrections obtained typically from Quantum Chromodynamics (QCD). The calculation of these corrections in the high energy regime is described by perturbation theory. In the present study, multi-loop calculations in QCD, including in particular two-loop corrections for single top quark production, are considered. There are several phenomenological motivations to study single top quark production: Firstly, the process is sensitive to the electroweak Wtb-vertex; moreover, non-standard couplings can hint at physics beyond the Standard Model. Secondly, the t-channel cross section measurement provides information on the b-quark Parton Distribution Functions (PDF). Finally, single top quark production enables us to directly measure the Cabibbo-Kobayashi-Maskawa(CKM) matrix element Vtb. The next-to-next-to-leading-order (NNLO) calculation of the single top quark production has many building blocks. In this study, two blocks will be presented: one-loop corrections squared and two-loop corrections interfered with Born. Initially, the one-loop squared contribution at NNLO for single top quark production will be calculated. Before we begin with the calculation of the two-loop corrections to single top quark production, we calculate the QCD form factors of heavy quarks at NNLO, along with the axial vector coupling as a first independent check. A comparison with the relevant literature suggests that this approach is in line with generally accepted procedure. This consistency check provides a proof of the validity of our setup. In the next step, the two-loop corrections to single top quark production will be calculated. After reducing all occurring tensor integrals to scalar integrals, we apply the integration by parts method (IBP) to find the master integrals. This step is a major challenge compared to all similar calculations because of the number of variables in the problem (two Mandelstam variables s and t, the dimension d and the mass of the top quark mt as well as the mass of the W boson mw). Finally, the calculation of the three kinds of topologies – vertex corrections, double boxes and non-planar double boxes – in the two-loop contribution at NNLO calculation will be presented.

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Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.