  We use unlabeled collision data and weakly-supervised learning to train
models which can distinguish prompt muons from non-prompt muons using patterns
of low-level particle activity in the vicinity of the muon, and interpret the
models in the space of energy flow polynomials. Particle activity associated
with muons is a valuable tool for identifying prompt muons, those due to heavy
boson decay, from muons produced in the decay of heavy flavor jets. The
high-dimensional information is typically reduced to a single scalar quantity,
isolation, but previous work in simulated samples suggests that valuable
discriminating information is lost in this reduction. We extend these studies
in LHC collisions recorded by the CMS experiment, where true class labels are
not available, requiring the use of the invariant mass spectrum to obtain
macroscopic sample information. This allows us to employ Classification Without
Labels (CWoLa), a weakly supervised learning technique, to train models. Our
results confirm that isolation does not describe events as well as the full
low-level calorimeter information, and we are able to identify single energy
flow polynomials capable of closing the performance gap. These polynomials are
not the same ones derived from simulation, highlighting the importance of
training directly on data.
