Dr German Sborlini (Universidad de Salamanca)
Having access to the parton-level kinematics is important for understanding the internal dynamics of particle collisions. In this talk, we present new results aiming to an efficient reconstruction of parton kinematics using machine-learning techniques. By simulating the collisions, we related experimentally-accessible quantities with the momentum fractions of the colliding 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.
Dr German Sborlini (Universidad de Salamanca) Roger Hernandez-Pinto (Universidad Autonoma de Sinaloa) David Renteria-Estrada (Universidad de Sinaloa) Maria Zurita (Brookhaven National Laboratory)