  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.
