Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

Details Neural Network Parameterizations of Electromagnetic Nucleon Form Factors Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

2010-06-02

arxiv.org/abs/1006.0342v2

Physics

High Energy Physics

High Energy Physics - Phenomenology

The revised version is divided into 4 sections. The discussion of the prior assumptions is added. The manuscript contains 4 ne

Scientific paper

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 chi2 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.

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