Abstract
Many measurements at the LHC require efficient
identification of heavy-flavour jets, i.e. jets originating from
bottom (b) or charm (c) quarks. An overview of the algorithms used
to identify c jets is described and a novel method to calibrate them
is presented. This new method adjusts the entire distributions of
the outputs obtained when the algorithms are applied to jets of
different flavours. It is based on an iterative approach exploiting
three distinct control regions that are enriched with either b jets,
c jets, or light-flavour and gluon jets. Results are presented in
the form of correction factors evaluated using proton-proton
collision data with an integrated luminosity of 41.5 fb-1 at
√s = 13 TeV, collected by the CMS experiment in 2017. The
closure of the method is tested by applying the measured correction
factors on simulated data sets and checking the agreement between
the adjusted simulation and collision data. Furthermore, a
validation is performed by testing the method on pseudodata, which
emulate various mismodelling conditions. The calibrated results
enable the use of the full distributions of heavy-flavour
identification algorithm outputs, e.g. as inputs to
machine-learning models. Thus, they are expected to increase the
sensitivity of future physics analyses.
