  We present a survey of a comprehensive set of jet substructure observables
commonly used to study the modifications of jets resulting from interactions
with the Quark Gluon Plasma in Heavy Ion Collisions. The \jewel{} event
generator is used to produce simulated samples of quenched and unquenched jets.
Three distinct analyses using Machine Learning techniques on the jet
substructure observables have been performed to identify both linear and
non-linear relations between the observables, and to distinguish the Quenched
and Unquenched jet samples. We find that most of the observables are highly
correlated, and that their information content can be captured by a small set
of observables. We also find that the correlations between observables are
resilient to quenching effects and that specific pairs of observables exhaust
the full sensitivity to quenching effects. The code, the datasets, and
instructions on how to reproduce this work are also provided.
