Neural network generated parametrizations of deeply virtual Compton form factors

@article{Kumeriki2011NeuralNG,
  title={Neural network generated parametrizations of deeply virtual Compton form factors},
  author={Kre{\vs}imir Kumeri{\vc}ki and Dieter M{\"u}ller and Andreas Sch{\"a}fer},
  journal={Journal of High Energy Physics},
  year={2011},
  volume={2011},
  pages={1-17}
}
We have generated a parametrization of the Compton form factor (CFF) $ \mathcal{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 $ \mathcal{H} $ and used HERMES data on DVCS off unpolarized protons. We predict the beam charge… 

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References

SHOWING 1-10 OF 43 REFERENCES

Neural network parameterizations of electromagnetic nucleon form-factors

The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks and the Bayesian approach for the neural networks is adapted for χ2 error-like function and applied to the data analysis.

Neural network parametrization of deep inelastic structure functions

We construct a parametrization of deep-inelastic structure functions which retains information on experimental errors and correlations, and which does not introduce any theoretical bias while

A fitter code for Deep Virtual Compton Scattering and Generalized Parton Distributions

We have developed a fitting code based on the leading-twist handbag Deep Virtual Compton Scattering (DVCS) amplitude in order to extract Generalized Parton Distribution (GPD) information from DVCS

Unbiased determination of the proton structure function F 2 p with faithful uncertainty estimation

We construct a parametrization of the deep-inelastic structure function of the proton F2(x,Q2) based on all available experimental information from charged lepton deep-inelastic scattering

Extraction of the Compton form factor H from deeply virtual Compton scattering measurements at Jefferson Lab

In the framework of generalized parton distributions, we study the helicity-dependent and independent cross sections measured in Hall A and the beam spin asymmetries measured in Hall B at Jefferson

Generalized Parton Distributions from Deeply Virtual Compton Scattering at HERMES

The HERMES Collaboration has recently published a set of (correlated) beam charge, beam spin and target spin asymmetries for the Deeply Virtual Compton Scattering (DVCS) process. This reaction allows

Deeply Virtual Compton Scattering

We study in QCD the physics of deeply virtual Compton scattering (DVCS){emdash}the virtual Compton process in the large s and small t kinematic region. We show that DVCS can probe a new type of {ital

The neural network approach to parton distribution functions

  • J. Rojo
  • Computer Science, Physics
  • 2006
This work presents in detail the approach to parametrize experimental data, based on a combination of Monte Carlo methods and neural networks, applied to the parametrization of parton distributions.

Extraction of the Compton Form Factor H from DVCS measurements at Jefferson Lab

In the framework of Generalised Parton Distributions, we study the helicitydependent and independent cross sections measured in Hall A and the beam spin asymmetries measured in Hall B at Jefferson

Generalized parton distributions from deep virtual compton scattering at CLAS