R}\xspace}$ between the candidate and , particle type, track quality information, and [$d_{xy}$]{}, [$d_{z}$]{}and their uncertainties, $\sigma({\ensuremath{d_{xy}}\xspace})$ and $\sigma({\ensuremath{d_{z}}\xspace})$. A convolutional DNN [@Lecun98gradient-basedlearning] is trained with simulated signal and background events. Signal candidates are those that are matched to generator-level leptons from a mixture of processes that give rise to genuine leptons. Background candidates that fail the matching are taken from simulated [$\PW$+jets]{}and QCD multijet events. The DeepPF discriminator value is obtained by averaging the DNN output with the nominal MVA-based discriminant described above. The working point for DeepPF isolation is chosen to maintain a constant efficiency of $\approx$50%, 56%, and 56% as a function of for the three respective decay modes: one charged hadron, one charged hadron with neutral pions, and three charged hadrons. Since the candidate distribution in signal events depends on the and masses, this choice of discriminator and working points allows us to maintain high efficiency for pair production signals under a large range of mass hypotheses. The overall misidentification rate for jets not originating from leptons ranges from 0.15% to 0.4% depending on and decay mode.

Significant contributions to the SM background originate from Drell–Yan+jets (DY+jets), [$\PW$+jets]{}, , and diboson processes, as well as from 