Padley, Brian P2017-07-312017-07-312016-122016-07-08December 2Adair, Antony H. "A Machine Learning Based Search for Supersymmetry in All Hadronic Decays of the sTop Particle." (2016) Diss., Rice University. <a href="https://hdl.handle.net/1911/95593">https://hdl.handle.net/1911/95593</a>.https://hdl.handle.net/1911/95593A search for signs of supersymmetry by means of all hadronic decays of the scalar top quark is presented. The data sample of proton-proton collisions used corresponds to an integrated luminosity of 19.6/fb collected at √s = 8 TeV with the CMS detector at the LHC. The investigation features machine learning based background suppression and prediction techniques, developed through an analogous 18.9/fb study. The data is found to be in agreement with the predicted backgrounds and no evidence of supersymmetry is observed. Exclusion limits are set, but found to be in general agreement with the previous study.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.SupersymmetrysTopTopStandard ModelParticle physicsSUSYHEPElementary ParticlesBoosted Decision TreesMVABDTThesis2017-07-31