  Jets produced in high-energy heavy-ion collisions are modified compared to
those in proton-proton collisions due to their interaction with the deconfined,
strongly-coupled quark-gluon plasma (QGP). In this work, we employ machine
learning techniques to identify important features that distinguish jets
produced in heavy-ion collisions from jets produced in proton-proton
collisions. We formulate the problem using binary classification and focus on
leveraging machine learning in ways that inform theoretical calculations of jet
modification: (i) we quantify the information content in terms of Infrared
Collinear (IRC)-safety and in terms of hard vs. soft emissions, (ii) we
identify optimally discriminating observables that are in principle calculable
in perturbative QCD, and (iii) we assess the information loss due to the
heavy-ion underlying event and background subtraction algorithms. We illustrate
our methodology using Monte Carlo event generators, where we find that
important information about jet quenching is contained not only in hard
splittings but also in soft emissions and IRC-unsafe physics inside the jet.
This information appears to be significantly reduced by the presence of the
underlying event. We discuss the implications of this for the prospect of using
jet quenching to extract properties of the QGP. Since the training labels are
exactly known, this methodology can be used directly on experimental data
without reliance on modeling. We outline a proposal for how such an
experimental analysis can be carried out, and how it can guide future
measurements.
