Learning to Isolate Muons

Distinguishing between prompt muons produced in heavy boson decay and muons produced in association with heavy-flavor jet production is an important task in analysis of collider physics data. We explore whether there is information available in calorimeter deposits that is not captured by the standard approach of isolation cones. We find that convolutional networks and particle-flow networks accessing the calorimeter cells surpass the performance of isolation cones, suggesting that the radial energy distribution and the angular structure of the calorimeter deposits surrounding the muon contain unused discrimination power. We assemble a small set of high-level observables which summarize the calorimeter information and partially close the performance gap with networks which analyze the calorimeter cells directly. These observables are theoretically well-defined and can be applied to studies of collider data. The remaining performance gap suggests the need for a new class of calorimeter-based observables.