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Title
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Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV
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Author
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Institution/Organisation
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CMS Collaboration
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Abstract
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| Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated t (t) over bar events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The b jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV). |
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Language
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English
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Source (journal)
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Journal of instrumentation. - Bristol, 2006, currens
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Publication
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Bristol
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Institute of Physics
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2018
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ISSN
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1748-0221
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DOI
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10.1088/1748-0221/13/05/P05011
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Volume/pages
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13
(2018)
, 117 p.
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Article Reference
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P05011
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ISI
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000431716900005
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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