Kumericki, Kresimir and Mueller, Dieter and Schaefer, Andreas (2011) Neural network generated parametrizations of deeply virtual Compton form factors. JOURNAL OF HIGH ENERGY PHYSICS (7): 073. ISSN 1029-8479,
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | GENERALIZED PARTON DISTRIBUTIONS; SCATTERING; NUCLEON; QUARK; QCD; QCD Phenomenology |
| Subjects: | 500 Science > 530 Physics |
| Divisions: | Physics > Institute of Theroretical Physics > Chair Professor Schäfer > Group Andreas Schäfer |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 08 Jun 2020 06:20 |
| Last Modified: | 08 Jun 2020 06:20 |
| URI: | https://pred.uni-regensburg.de/id/eprint/20562 |
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