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Title: Using machine learning to measure the cross section of top quark pairs in the muon+jets channel at the Compact Muon Solenoid
Authors: Kirn, Malina Aurelia
Advisors: Hadley, Nicholas
Department/Program: Applied Mathematics and Scientific Computation
Type: Dissertation
Sponsors: Digital Repository at the University of Maryland
University of Maryland (College Park, Md.)
Subjects: Particle physics
Computer science
Keywords: Compact Muon Solenoid
cross section
grid computing
neural network
top quark
Issue Date: 2011
Abstract: 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.
Appears in Collections:Computer Science Theses and Dissertations
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UMD Theses and Dissertations

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