Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

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.

The revised version is divided into 4 sections. The discussion of the prior assumptions is added. The manuscript contains 4 new figures and 2 new tables (32 pages, 15 figures, 2 tables)

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• Two-Photon Exchange Effect Studied with Neural Networks
• Extraction of Axial Mass and Strangeness Values from the MiniBooNE Neutral Current Elastic Cross Section Measurement
• Applications of Neural Networks in Hadron Physics
• Bayesian Reasoning for Physics Informed Neural Networks
• Semileptonic decays of heavy mesons with artificial neural networks
• Continuous simulation of hypothetical physics processes with multiple free parameters