Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

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.Comment: 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

The Spin Structure of the Nucleon

We present an overview of recent experimental and theoretical advances in our understanding of the spin structure of protons and neutrons.Comment: 84 pages, 29 figure

Measurement of the Generalized Polarizabilities of the Proton in Virtual Scattering at Q2=0.92 and 1.76 GeV2: I. Low Energy Expansion Analysis

Virtual Compton Scattering is studied at the Thomas Jefferson National Accelerator Facility at low Center-of-Mass energies, below pion threshold. Following the Low Energy Theorem for the $ep \to ep \gamma$ process, we obtain values for the two structure functions Pll-Ptt/epsilon and Plt at four-momentum transfer squared Q2=0.92 and 1.76 GeV2.Comment: 4 pages, 2 figures, to be submitted to PRL. Figs 1 and 2, lettering enlarge

Exploring the latest quark-meson coupling model for finite nuclei

International audienceThe quark-meson coupling (QMC) model describes atomic nuclei on the basis of the quark structure of nucleons and their self-consistent change as they interact with each other in the nuclear medium. The model has been successfully applied to even-even nuclei across the entire nuclear chart and results were comparable to other existing models despite having fewer adjustable parameters. Nuclear matter properties derived from the model are also within the widely used range of values. In this paper, we explore the latest version of the model, QMCπ-II. We put some emphasis on QMC predictions for neutron skin thickness which will be the subject for experiments in the near future. QMCπ-II predicts a value of around 0.15 and 0.16 fm for 48Ca and 208Pb, respectively, with the slope of symmetry energy at around 40 MeV