Development and performance of a transformer-based lepton isolation algorithm with the ATLAS experiment

Abstract

The Prompt Lepton Isolation Tagger (PLIT) is a transformer-based algorithm designed to distinguish prompt leptons (electrons and muons) produced in the primary decays of electroweak gauge bosons from non-prompt leptons produced in hadron decays and from misidentified leptons. It is primarily used in measurements targeting multilepton final states with important backgrounds from non-prompt and misidentified leptons. PLIT exploits information from charged-particle tracks associated with the lepton candidate, together with lepton properties, using a transformer neural network to identify correlations between the tracks and improve the discrimination. Its performance is evaluated using simulated top-quark pair production events and compared with that of previously used lepton isolation algorithms, demonstrating improved prompt lepton efficiency while maintaining high rejection of non-prompt leptons. This article presents the development, performance, and calibration of PLIT for electrons and muons in proton--proton collisions at a centre-of-mass energy of $\sqrt{s}$=13.6 TeV recorded by the ATLAS experiment at the Large Hadron Collider. Operating points are defined for both electrons and muons, and their efficiencies are calibrated using $Z\to l^+l^-$ events in Run 3 data, providing data-to-simulation correction factors for physics analyses.