Machine Learning for Muon Identifcation at LHCb

Particle identifcation is a key ingredient of most of LHCb results. Muon identifcation in particular is used at every stage of the LHCb trigger. The objective of the muon identifcation is to distinguish muons from charged hadrons under strict timing constraints. For this task, we use a state-of-the-art gradient boosting algorithm trained with real background-subtracted data. In this proceedings we present the algorithm along with the evaluation of its performance on signal and background rejection.