Jul 6 – 13, 2022
Bologna, Italy
Europe/Rome timezone

Reconstructing parton collisions with machine learning techniques

Jul 8, 2022, 7:05 PM
1h 25m
Bologna, Italy

Bologna, Italy

Palazzo della Cultura e dei Congressi
Poster Top quark and EW Physics Poster Session


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.

In-person participation Yes

Primary authors

Dr German Sborlini (Universidad de Salamanca) Roger Hernandez-Pinto (Universidad Autonoma de Sinaloa) David Renteria-Estrada (Universidad de Sinaloa) Maria Zurita (Brookhaven National Laboratory)

Presentation materials