Neural network generated parametrizations of deeply virtual Compton form factors (CROSBI ID 173623)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Kumerički, Krešimir ; Müller, Dieter ; Schäfer, Andreas
engleski
Neural network generated parametrizations of deeply virtual Compton form factors
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.
deeply virtual Compton scattering; neural networks
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Podaci o izdanju
1107 (7)
2011.
073-1-073-16
objavljeno
1126-6708
10.1007/JHEP07(2011)073