Journal of High Energy Physics

, 2010:53 | Cite as

Neural network parameterizations of electromagnetic nucleon form-factors

  • Krzysztof M. Graczyk
  • Piotr Płonski
  • Robert Sulej
Open Access
Article

Abstract

The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties. The Bayesian approach for the neural networks is adapted for χ2 error-like function and applied to the data analysis. The sequence of the feed forward neural networks with one hidden layer of units is considered. The given neural network represents a particular form-factor parametrization. The so-called evidence (the measure of how much the data favor given statistical model) is computed with the Bayesian framework and it is used to determine the best form factor parametrization.

Keywords

Lepton-Nucleon Scattering 

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© The Author(s) 2010

Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Krzysztof M. Graczyk
    • 1
  • Piotr Płonski
    • 2
  • Robert Sulej
    • 3
  1. 1.Institute of Theoretical PhysicsWrocław UniversityWrocławPoland
  2. 2.Institute of RadioelectronicsWarsaw University of TechnologyWarsawPoland
  3. 3.A. Soltan Institute for Nuclear StudiesWarsawPoland

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