School of Physics - Theses

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    Measurements of the ATLAS tau trigger reconstruction and identification efficiencies using 2016 data from \(pp\) collisions at\(\sqrt s \) = 13 T\(e\)V
    Mason, Lara ( 2017)
    This thesis presents the performance of the tau trigger algorithm used by the ATLAS experiment to select hadronically decaying tau leptons in the LHC Run 2. Using the 33.3 \(f{b^{ - 1}}\) of \(pp\) collisions data recorded in 2016 at\(\sqrt s \) = 13 T\(e\)V, the performance of this algorithm is studied using a `tag-and-probe' based analysis in order to select Z boson decays to tau leptons, where one tau decays hadronically and the other leptonically. The reconstruction and identification efficiencies of the tau trigger algorithm are measured, and good performance is observed. The efficiency of the tau trigger in data is compared with that in simulation, and is parametrised as a function of the tau decay topology, its kinematics, and the average number of interactions per bunch crossing. The selection efficiency at each step of the high level trigger is measured, using dedicated intermediary triggers, and good agreement between data and simulation is observed. Using the comparison between reconstruction and identification efficiencies in data and simulation, correction factors for simulated events are measured, which are utilised by the entire ATLAS collaboration.
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    B0→K0π0 and direct CP violation at Belle
    Hawthorne-Gonzalvez, Anton ( 2017)
    Rare B-meson decays such as the B0 → Ksπ0 which proceed without a charm quark provide a probe for physics beyond the standard model. This decay proceeds mainly via the b → s penguin transition, with the b → u transition being colour suppressed, allowing CP-violating effects to be observable. The asymmetric e+e− KEKB collider and the Belle detector provide the large luminosity and data collection required to observe these rare B decays. Methods to reduce the large qq backgrounds are investigated. The use of optimised neural networks using TensorFlow shows a significant improvement compared to the commonly used NeuroBayes software. Techniques for reducing correlations between variables introduced by TensorFlow are also investigated, proving that the use of adversarial neural networks can provide an improved background suppression as compared to NeuroBayes, whilst minimising correlations introduced by the neural network. An improved method of measuring the direct CP violation is introduced. Using Monte Carlo data with sample sizes corresponding to the full Belle datatset of (771.581 ± 10.566) × 106 BB events, the statistical uncertainty in ACP using this method is reduced from the latest Belle result of 0.13 to 0.1035 ± 0.0032. This method would also provide an up to date measurement on B(B0 → K0π0).