Separation of Quark Flavors using DVCS Data

TL;DR

This paper employs neural networks with dispersion relation constraints to analyze DVCS data, successfully extracting quark flavor contributions and advancing the understanding of nucleon structure.

Contribution

It introduces a neural network approach constrained by dispersion relations to separate quark flavor contributions in DVCS data, achieving new insights into nucleon structure.

Findings

Determined six of eight leading Compton form factors in the valence region.

Separated up and down quark contributions using neutron DVCS data.

Paved the way for three-dimensional nucleon imaging.

Abstract

Using the available data on deeply virtual Compton scattering (DVCS) off protons and utilizing neural networks enhanced by the dispersion relation constraint, we determine six out of eight leading Compton form factors in the valence quark kinematic region. Furthermore, adding recent data on DVCS off neutrons, we separate contributions of up and down quarks to the dominant form factor, thus paving the way towards a three-dimensional picture of the nucleon.