  We train a network to identify jets with fractional dark decay (semi-visible
jets) using the pattern of their low-level jet constituents, and explore the
nature of the information used by the network by mapping it to a space of jet
substructure observables. Semi-visible jets arise from dark matter particles
which decay into a mixture of dark sector (invisible) and Standard Model
(visible) particles. Such objects are challenging to identify due to the
complex nature of jets and the alignment of the momentum imbalance from the
dark particles with the jet axis, but such jets do not yet benefit from the
construction of dedicated theoretically-motivated jet substructure observables.
A deep network operating on jet constituents is used as a probe of the
available information and indicates that classification power not captured by
current high-level observables arises primarily from low-$p_\textrm{T}$ jet
constituents.
