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dc.contributor.advisorHass, Andrew C.
dc.contributor.advisorJohnson, Loretta M.
dc.contributor.authorDeWilde, Burton
dc.date.accessioned2007-02-22T14:51:15Z
dc.date.available2007-02-22T14:51:15Z
dc.date.copyright2006-01-01
dc.date.created2007-02-22
dc.date.issued2006-01-01
dc.identifier.urihttp://hdl.handle.net/10920/86
dc.descriptionviii, 54 p.
dc.description.abstractThe theory and experiment associated with the Standard Model Higgs boson is reviewed. A search is underway at Fermilab’s Tevatron accelerator for Higgs production in the pp → ZH → μ+μ−bb channel at 1.96 TeV. This study employs an artificial neural network trained on simulated data in order to quantify the expected difference between a small Higgs signal and a much larger background. The network is optimized with respect to variable set, training method, and number of training epochs and hidden neurons. The optimal network achieves greater signal/background discrimination than a conventional analysis of the data, thereby reducing the total number of events required for the detection of the Higgs boson.en
dc.description.sponsorshipColumbia University
dc.description.sponsorshipDepartment of Physics, Columbia University
dc.format.extent1,463,387 bytes
dc.format.mediumimage/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.publisherKalamazoo, Mich. : Kalamazoo College.
dc.relation.ispartofseriesSenior Individualized Projects. Physics.
dc.rightsU.S. copyright laws protect this material. Commercial use or distribution of this material is not permitted without prior written permission of the copyright holder.
dc.subjectparticle physicsen
dc.subjecthiggs bosonen
dc.subjectTevatronen
dc.subjectD0en
dc.subjectneural networken
dc.subjecthigh energy physicsen
dc.subjectFermilaben
dc.subject.lcshParticles (Nuclear physics)
dc.subject.lcshHiggs bosons
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshFermi National Accelerator Laboratory
dc.titleA Search for the Standard Model Higgs Boson in pp → ZH → μ+μ− b b at √s = 1.96 TeVen
dc.typeThesisen


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