UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

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Author: search.filters.author.Kirn, Malina AureliaSubject: search.filters.subject.grid computing

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    Using machine learning to measure the cross section of top quark pairs in the muon+jets channel at the Compact Muon Solenoid
    (2011) Kirn, Malina Aurelia; Hadley, Nicholas; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The cross section for pp to top-antitop production at a center of mass energy of 7 TeV is measured using a data sample with integrated luminosity 36.1 inverse pb collected by the CMS detector at the LHC. The analysis is performed on a computing grid. Events with an isolated muon and three hadronic jets are analyzed using a multivariate machine learning algorithm. Kinematic variables and b tags are provided as input to the algorithm; output from the algorithm is used in a maximum likelihood fit to determine top-antitop event yield. The measured cross section is 151 +/- 15(stat.) +35/-28(syst.) +/- 6(lumi.) pb.