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arXiv · 1106.2808

Neural network generated parametrizations of deeply virtual Compton form factors

Kresimir Kumericki·Dieter Mueller·Andreas Schafer
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Abstract

We have generated a parametrization of the Compton form factor (CFF) 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 H and used HERMES data on DVCS off unpolarized protons. We predict the beam charge-spin asymmetry for a proton at the kinematics of the COMPASS II experiment.

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Kresimir Kumericki, Dieter Mueller, Andreas Schafer. 2011-06-14. Neural network generated parametrizations of deeply virtual Compton form factors. https://doi.org/10.1007/jhep07(2011)073

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