  The identification of prompt and isolated muons, as well as muons from
heavy-flavour hadron decays, is an important task. We developed two
multivariate techniques to provide highly efficient identification for muons
with transverse momentum greater than 10 GeV. One provides a continuous
variable as an alternative to a cut-based identification selection and offers a
better discrimination power against misidentified muons. The other one selects
prompt and isolated muons by using isolation requirements to reduce the
contamination from nonprompt muons arising in heavy-flavour hadron decays. Both
algorithms are developed using 59.7 fb$^{-1}$ of proton-proton collisions data
at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV collected in 2018 with the
CMS experiment at the CERN LHC.
