Boosted Decision Trees in the Level-1 Muon Endcap Trigger at CMS

The first implementation of a Machine Learning Algorithm inside a Level-1 trigger system at the LHC is presented. The Endcap Muon Track Finder (EMTF) at CMS uses Boosted Decision Trees (BDTs) to infer the momentum of muons in the forward region of the detector, based on 25 different variables. Combinations of these variables representing $2^{30}$ distinct patterns are evaluated offline using regression BDTs. The predictions for the $2^{30}$ input variable combinations are stored in a 1.2 GB look-up table in the EMTF hardware. The BDTs take advantage of complex correlations between variables, the inhomogeneous magnetic field, and non-linear effects -- like inelastic scattering -- to distinguish high momentum signal muons from the overwhelming low-momentum background. The new momentum algorithm reduced the background rate by a factor of three with respect to the previous analytic algorithm, with further improvements foreseen in the coming year