A Machine Learning Based Search for Supersymmetry in All Hadronic Decays of the sTop Particle

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dc.contributor.advisorPadley, Brian P
dc.creatorAdair, Antony H
dc.date.accessioned2017-07-31T16:56:36Z
dc.date.available2017-07-31T16:56:36Z
dc.date.created2016-12
dc.date.issued2016-07-08
dc.date.submittedDecember 2016
dc.date.updated2017-07-31T16:56:37Z
dc.description.abstractA 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.
dc.format.mimetypeapplication/pdf
dc.identifier.citationAdair, 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>.
dc.identifier.urihttps://hdl.handle.net/1911/95593
dc.language.isoeng
dc.rightsCopyright 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.
dc.subjectSupersymmetry
dc.subjectsTop
dc.subjectTop
dc.subjectStandard Model
dc.subjectParticle physics
dc.subjectSUSY
dc.subjectHEP
dc.subjectElementary Particles
dc.subjectBoosted Decision Trees
dc.subjectMVA
dc.subjectBDT
dc.titleA Machine Learning Based Search for Supersymmetry in All Hadronic Decays of the sTop Particle
dc.typeThesis
dc.type.materialText
thesis.degree.departmentPhysics and Astronomy
thesis.degree.disciplineNatural Sciences
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.majorPhysics
thesis.degree.nameDoctor of Philosophy
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