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 Physics at Virginia

"Neural Network Generalized Parton Distributions"


Adil Umar Khawaja , University of Virginia
[Host: Chris Neu & Simonetta Liuti]
ABSTRACT:

Generalized parton distributions (GPDs) are a key construct for understanding the spatial distribution of quarks and gluons inside nucleons. These distributions are accessed through deeply virtual exclusive processes, such as deeply virtual Compton scattering (DVCS) and deeply virtual meson production (DVMP). We present a spectator model-based parameterization of twist 2, chiral-even, GPDs in the quark, anti-quark, and the gluon sectors. Our model parameters are constrained using high precision electron-nucleon elastic scattering data, deep inelastic scattering data, and recent lattice QCD moment data. The parametrization is used to calculate Compton form factors (CFFs) which allow us to make predictions for DVCS experiments in the kinematic regions currently accessed at Jefferson Lab. Predictions for kinematic regions that will be accessible by the electron ion collider (EIC) are also presented. We also discuss preliminary results on an approach to learn GPDs with a neural network.

Nuclear Physics Seminar
Tuesday, April 29, 2025
3:30 PM
Physics, Room 220

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