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

Simple item page
dc.contributor.advisorPadley, Brian Pen_US
dc.creatorAdair, Antony Hen_US
dc.date.accessioned2017-07-31T16:56:36Zen_US
dc.date.available2017-07-31T16:56:36Zen_US
dc.date.created2016-12en_US
dc.date.issued2016-07-08en_US
dc.date.submittedDecember 2016en_US
dc.date.updated2017-07-31T16:56:37Zen_US
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.en_US
dc.format.mimetypeapplication/pdfen_US
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>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/95593en_US
dc.language.isoengen_US
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.en_US
dc.subjectSupersymmetryen_US
dc.subjectsTopen_US
dc.subjectTopen_US
dc.subjectStandard Modelen_US
dc.subjectParticle physicsen_US
dc.subjectSUSYen_US
dc.subjectHEPen_US
dc.subjectElementary Particlesen_US
dc.subjectBoosted Decision Treesen_US
dc.subjectMVAen_US
dc.subjectBDTen_US
dc.titleA Machine Learning Based Search for Supersymmetry in All Hadronic Decays of the sTop Particleen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentPhysics and Astronomyen_US
thesis.degree.disciplineNatural Sciencesen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.majorPhysicsen_US
thesis.degree.nameDoctor of Philosophyen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ADAIR-DOCUMENT-2016.pdf
Size:
11.83 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
5.84 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
LICENSE.txt
Size:
2.61 KB
Format:
Plain Text
Description:
Download
Collections
Rice University Theses and Dissertations