  Deep learning techniques have the power to identify the degree of
modification of high energy jets traversing deconfined QCD matter on a
jet-by-jet basis. Such knowledge allows us to study jets based on their
initial, rather than final energy. We show how this new technique provides
unique access to the genuine configuration profile of jets over the transverse
plane of the nuclear collision, both with respect to their production point and
their orientation. Effectively removing the selection biases induced by
final-state interactions, one can in this way analyse the potential azimuthal
anisotropies of jet production associated to initial-state effects.
Additionally, we demonstrate the capability of our new method to locate with
precision the production point of a dijet pair in the nuclear overlap region,
in what constitutes an important step forward towards the long term quest of
using jets as tomographic probes of the quark-gluon plasma.
