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Description
  • 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
  • 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 (en)
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 (en)
skos:prefLabel
  • 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 (en)
skos:notation
  • RIV/00216208:11320/13:10139340!RIV14-MSM-11320___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • I, P(LA08015)
http://linked.open...iv/cisloPeriodika
  • 7
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 104288
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11320/13:10139340
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • jets; physics; root-s=7 tev; pp collisions; standard model (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • US - Spojené státy americké
http://linked.open...ontrolniKodProRIV
  • [AD513A0E90A7]
http://linked.open...i/riv/nazevZdroje
  • Physical Review D - Particles, Fields, Gravitation and Cosmology
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 87
http://linked.open...iv/tvurceVysledku
  • Adam, W.
  • Chatrchyan, S.
  • Finger, Michael
  • Finger, Miroslav
  • Khachatryan, V.
  • Bergauer, T.
  • Dragicevic, M.
  • Eroe, J.
  • Fabjan, C.
  • Friedl, M.
  • Fruehwirth, R.
  • Ghete, V. M.
  • Hoermann, N.
  • Hrubec, J.
  • Sirunyan, A. M.
  • Tumasyan, A.
  • Aguilo, E.
http://linked.open...ain/vavai/riv/wos
  • 000316954200001
issn
  • 1550-7998
number of pages
http://bibframe.org/vocab/doi
  • 10.1103/PhysRevD.87.072001
http://localhost/t...ganizacniJednotka
  • 11320
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