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arXiv · 1006.0342

Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

Krzysztof M. Graczyk·Piotr Plonski·Robert Sulej
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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 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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Krzysztof M. Graczyk, Piotr Plonski, Robert Sulej. 2010-08-25. Neural Network Parameterizations of Electromagnetic Nucleon Form Factors. https://doi.org/10.1007/jhep09(2010)053

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