Using machine learning to measure the cross section of top quark pairs in the muon+jets channel at the Compact Muon Solenoid
| dc.contributor.advisor | Hadley, Nicholas | en_US |
| dc.contributor.author | Kirn, Malina Aurelia | en_US |
| dc.contributor.department | Applied Mathematics and Scientific Computation | en_US |
| dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
| dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
| dc.date.accessioned | 2012-02-17T06:37:39Z | |
| dc.date.available | 2012-02-17T06:37:39Z | |
| dc.date.issued | 2011 | en_US |
| dc.description.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. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1903/12223 | |
| dc.subject.pqcontrolled | Particle physics | en_US |
| dc.subject.pqcontrolled | Computer science | en_US |
| dc.subject.pquncontrolled | Compact Muon Solenoid | en_US |
| dc.subject.pquncontrolled | cross section | en_US |
| dc.subject.pquncontrolled | grid computing | en_US |
| dc.subject.pquncontrolled | neural network | en_US |
| dc.subject.pquncontrolled | top quark | en_US |
| dc.title | Using machine learning to measure the cross section of top quark pairs in the muon+jets channel at the Compact Muon Solenoid | en_US |
| dc.type | Dissertation | en_US |
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