Neural network generated parametrizations of deeply virtual Compton form factors
High Energy Physics - Phenomenology
2015-05-28 v1 High Energy Physics - Experiment
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.
Cite
@article{arxiv.1106.2808,
title = {Neural network generated parametrizations of deeply virtual Compton form factors},
author = {Kresimir Kumericki and Dieter Mueller and Andreas Schafer},
journal= {arXiv preprint arXiv:1106.2808},
year = {2015}
}
Comments
16 pages, 5 figures