Neural network generated parametrizations of deeply virtual Compton form factors

  title={Neural network generated parametrizations of deeply virtual Compton form factors},
  author={Kre{\vs}imir Kumeri{\vc}ki and Dieter M{\"u}ller and Andreas Sch{\"a}fer},
  journal={Journal of High Energy Physics},
We have generated a parametrization of the Compton form factor (CFF) $ \mathcal{H} $ based on data from deeply virtual Compton scattering (DVCS) using neural networks. This approach offers an essentially model-independent fitting procedure, which provides realistic uncertainties. Furthermore, it facilitates propagation of uncertainties from experimental data to CFFs. We assumed dominance of the CFF $ \mathcal{H} $ and used HERMES data on DVCS off unpolarized protons. We predict the beam charge… 

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