Neural network generated parametrizations of deeply virtual Compton form factors

Details Neural network generated parametrizations of deeply virtual Compton form factors Neural network generated parametrizations of deeply virtual Compton form factors

2011-06-14

arxiv.org/abs/1106.2808v1

Physics

High Energy Physics

High Energy Physics - Phenomenology

16 pages, 5 figures

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Neural network generated parametrizations of deeply virtual Compton form factors does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Neural network generated parametrizations of deeply virtual Compton form factors, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Neural network generated parametrizations of deeply virtual Compton form factors will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-656480