Speaker
Dr
German Sborlini
(Universidad de Salamanca)
Description
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 |
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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)
