21-25 March 2022
Salon de actos del IATA
Europe/Madrid timezone

Neural networks for reconstruction of the underlying kinematics in high energy collisions

23 Mar 2022, 17:03


David Francisco Rentería Estrada (Universidad Autónoma de Sinaloa)


The parton-level kinematics plays a crucial role for understanding the internal structure of hadrons and improving the precision of the calculations. To better understand the kinematics at the partonic level, we study the production of one hadron and a direct photon, including up to Next-to-Leading Order Quantum Chromodynamics and Leading-Order Quantum Electrodynamics corrections. Using a code based on Monte-Carlo integration, we simulate the collisions and analyze the events to determine the correlations among measurable and partonic quantities. Then, we use these results to apply Machine Learning algorithms that allow us to find the momentum fractions of the partons involved in the process, in terms of suitable combinations of the final state momenta.

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