# Neural network generated parametrizations of deeply virtual Compton form factors

@article{Kumeriki2011NeuralNG,
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},
year={2011},
volume={2011},
pages={1-17}
}
• Published 14 June 2011
• Physics
• 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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