Neural network generated parametrizations of deeply virtual Compton form factors
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.
- Publication:
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Journal of High Energy Physics
- Pub Date:
- July 2011
- DOI:
- arXiv:
- arXiv:1106.2808
- Bibcode:
- 2011JHEP...07..073K
- Keywords:
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- QCD Phenomenology;
- High Energy Physics - Phenomenology;
- High Energy Physics - Experiment
- E-Print:
- 16 pages, 5 figures