Title
Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
article
Publication
Lancaster, Pa ,
Subject
Physics
Source (journal)
Physical review : D : particles and fields. - Lancaster, Pa, 1970 - 2003
Volume/pages
87(2013) :7 , p. 1-21
ISSN
0556-2821
Article Reference
072001
Carrier
E-only publicatie
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
In this paper, a search for supersymmetry (SUSY) is presented in events with two opposite-sign isolated leptons in the final state, accompanied by hadronic jets and missing transverse energy. An artificial neural network is employed to discriminate possible SUSY signals from a standard model background. The analysis uses a data sample collected with the CMS detector during the 2011 LHC run, corresponding to an integrated luminosity of 4: 98 fb(-1) of proton-proton collisions at the center-of-mass energy of 7 TeV. Compared to other CMS analyses, this one uses relaxed criteria on missing transverse energy (E-T > 40 GeV) and total hadronic transverse energy (HT > 120 GeV), thus probing different regions of parameter space. Agreement is found between standard model expectation and observations, yielding limits in the context of the constrained minimal supersymmetric standard model and on a set of simplified models. DOI: 10.1103/PhysRevD.87.072001
Full text (open access)
https://repository.uantwerpen.be/docman/irua/2f7f61/3843.pdf
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