  Jet interactions in a hot QCD medium created in heavy-ion collisions are
conventionally assessed by measuring the modification of the distributions of
jet observables with respect to the proton-proton baseline. However, the
steeply falling production spectrum introduces a strong bias toward small
energy losses that obfuscates a direct interpretation of the impact of medium
effects in the measured jet ensemble. Modern machine learning techniques offer
the potential to tackle this issue on a jet-by-jet basis. In this paper, we
employ a convolutional neural network (CNN) to diagnose such modifications from
jet images where the training and validation is performed using the hybrid
strong/weak coupling model. By analyzing measured jets in heavy-ion collisions,
we extract the original jet transverse momentum, i.e., the transverse momentum
of an identical jet that did not pass through a medium, in terms of an energy
loss ratio. Despite many sources of fluctuations, we achieve good performance
and put emphasis on the interpretability of our results. We observe that the
angular distribution of soft particles in the jet cone and their relative
contribution to the total jet energy contain significant discriminating power,
which can be exploited to tailor observables that provide a good estimate of
the energy loss ratio. With a well-predicted energy loss ratio, we study a set
of jet observables to estimate their sensitivity to bias effects and reveal
their medium modifications when compared to a more equivalent jet population,
i.e., a set of jets with similar initial energy. Finally, we also show the
potential of deep learning techniques in the analysis of the geometrical
aspects of jet quenching such as the in-medium traversed length or the position
of the hard scattering in the transverse plane, opening up new possibilities
for tomographic studies.
