  Having access to the parton-level kinematics is important for understanding
the internal dynamics of particle collisions. Here, we present new results
aiming to an efficient reconstruction of parton collisions using
machine-learning techniques. By simulating the collider events, we related
experimentally-accessible quantities with the momentum fractions of the
involved partons. We used photon-hadron production to exploit the cleanliness
of the photon signal, including up to NLO QCD-QED corrections. Neural networks
led to an outstanding reconstruction efficiency, suggesting a powerful strategy
for unveiling the behaviour of the fundamental bricks of matter in high-energy
collisions.
