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PhD Thesis: Top quark and heavy vector boson associated production at the ATLAS experiment
  Thesis defense

Thursday 02 November 2017
from 09:30 to 13:30
at FA32
Speaker : Olga Bessidskaia Bylund (Stockholm University, Department of Physics)
Abstract : The Standard Model (SM) of particle physics describes the elementary particles that constitute matter and their interactions. The predictions of the SM have been confirmed by numerous experimental results. However, several questions of particle phenomena in the Universe remain unaddressed by the Standard Model, which suggests that the SM can be extended to a more complete theory. One approach to search for extensions of the SM is to test the predictions of the Standard Model in high precision measurements and see whether the results falsify the SM. For this reason, production of the ttZ and ttW processes at the ATLAS experiment at CERN is studied. It is investigated whether the SM gives correct predictions for these processes and how much room there is for contributions from new physics that give similar final states. Three measurements of ttZ and ttW production are performed. The first measurement is performed at 8 TeV collision energy. The next measurement uses data collected in 2015 at 13 TeV collision energy, when the production cross sections for these processes are considerably larger. The third measurement uses ten times as much data at 13 TeV collision energy. This analysis is not public at the time of writing, so only preliminary results for the expected sensitivity are presented. The new physics affecting ttZ production is parametrised in the model-independent framework of Effective Field Theory. Five effective operators that can affect ttZ production are studied and their coefficients are constrained in a fit to simulated data for the third measurement. The major background process tWZ is modelled at NLO in QCD. In order to avoid overlaps with ttZ, the Diagram Removal (DR) method is employed in two versions: one where the quantum interference is neglected (DR1) and another where it is modelled (DR2). The differences between the two predictions are explored and enter the measurement as a modelling uncertainty.

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