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Article
Report number arXiv:1604.02997 ; CERN-EP-2016-019 ; CERN-EP-2016-019
Title Measurement of fiducial differential cross sections of gluon-fusion production of Higgs bosons decaying to WWeνμν with the ATLAS detector at s=8 TeV
Related titleMeasurement of fiducial differential cross sections of gluon-fusion production of Higgs bosons decaying to WWeνμν with the ATLAS detector at s=8 TeV
Author(s) ATLAS Collaboration  Afficher les 2859 auteurs
Publication 2016-08-17
Imprint 11 Apr 2016
Number of pages 61
Note 44 pages plus author list + cover pages (61 pages total), 8 figures, 20 tables, submitted to JHEP. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/HIGG-2015-04/
In: JHEP 08 (2016) 104
DOI 10.1007/JHEP08(2016)104
Subject category Particle Physics - Experiment
Accelerator/Facility, Experiment CERN LHC ; ATLAS
Free keywords Higgs physics ; global features ; particle and resonance production ; experimental results
Abstract This paper describes a measurement of fiducial and differential cross sections of gluon-fusion Higgs boson production in the HWWeνμν channel, using 20.3 fb1 of proton--proton collision data. The data were produced at a centre-of-mass energy of s=8 TeV at the CERN Large Hadron Collider and recorded by the ATLAS detector in 2012.  Cross sections are measured from the observed HWWeνμν signal yield in categories distinguished by the number of associated jets. The total cross section is measured in a fiducial region defined by the kinematic properties of the charged leptons and neutrinos. Differential cross sections are reported as a function of the number of jets, the Higgs boson transverse momentum, the dilepton rapidity, and the transverse momentum of the leading jet.  The jet-veto efficiency, or fraction of events with no jets above a given transverse momentum threshold, is also reported.  All measurements are compared to QCD predictions from Monte Carlo generators and fixed-order calculations, and are in agreement with the Standard Model predictions.
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