Applications of Neural Networks in Hadron Physics

Abstract

The Bayesian approach for the feed-forward neural networks is reviewed. Its potential for usage in hadron physics is discussed. As an example of the application the study of the the two-photon exchange effect is presented. We focus on the model comparison, the estimation of the systematic uncertainties due to the choice of the model, and the over-fitting. As an illustration the predictions of the cross sections ratio $d\sigma (e^{+}p\to e^{+}p)/d\sigma (e^{-}p\to e^{-}p)$ are given together with the estimate of the uncertainty due to the parametrization choice.

16 pages, 9 figures, Invited contribution to the Journal of Physics G: Nuclear and Particle Physics focus section entitled "Enhancing the interaction between nuclear experiment and theory through information and statistics", in press

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• Uncertainty propagation within the UNEDF models
• Bayesian Reasoning for Physics Informed Neural Networks
• Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data