One of the primary goals of the CERN Large Hadron Collider is to search for new physics. Many such searches have been carried out, in particular searches for supersymmetry, yet no new physics beyond the Standard Model has been found. With a large number of free parameters introduced by frameworks such as supersymmetry, it can be difficult to interpret the null results of searches. The first analysis presented in this thesis attempts to tackle this difficulty head-on, and gives a summary of the constraints from the Run-1 ATLAS searches. A combination of 22 searches were used, with integrated luminosities of up to 20.3 inverse femtobarns of 7 and 8 TeV data. The results are interpreted in the context of the 19-dimensional phenomenological MSSM, and are presented in terms of the masses of supersymmetric particles. Constraints from dark matter, heavy flavour and precision electroweak measurements were incorporated, and results are also interpreted in terms of these observables. Properties of models missed by the Run-1 searches are also shown. The second analysis presented in this thesis documents a direct search for new physics, using 18.2 inverse femtobarns of 13 TeV data collected during 2015 and 2016 by the ATLAS detector. The search targets final states with large jet multiplicity (at least 7 to at least 10 jets), which can arise from the pair production of gluinos decaying via a cascade. Further requirements are imposed on the sum of masses of reclustered large-radius jets. No evidence for new physics is found, and the results are interpreted in both a model-independent way and in terms of two simplified supersymmetric models, one of which was inspired by the results of the first study. Limits on the gluino mass of up to 1600 GeV are set at the 95 % confidence level, extending previous limits.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:729151 |
| Date | January 2017 |
| Creators | Fawcett, William James |
| Contributors | Barr, Alan |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://ora.ox.ac.uk/objects/uuid:562ef583-9686-4895-adb0-801d1abb291d |
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