  In the collider phenomenology of extensions of the Standard Model with
partner particles, cascade decays occur generically, and they can be
challenging to discover when the spectrum of new particles is compressed and
the signal cross section is low. Achieving discovery-level significance and
measuring the properties of the new particles appearing as intermediate states
in the cascade decays is a longstanding problem, with analysis techniques for
some decay topologies already optimized. We focus our attention on a benchmark
decay topology with four final state particles where there is room for
improvement, and where multidimensional analysis techniques have been shown to
be effective in the past. Using machine learning techniques, we identify the
optimal kinematic observables for discovery, spin determination and mass
measurement. In agreement with past work, we confirm that the kinematic
observable $\Delta_4$ is highly effective. We quantify the achievable accuracy
for spin determination and for the precision for mass measurements as a
function of the signal size.
