Speaker
Description
Mapping the 3D structure of the proton in terms of its spinning quark and gluon constituents is one of the main goals in current hadronic physics. Generalized parton distributions can provide part of the solution, through Fourier transformation of the single particle spatial density of quarks and gluons with a given longitudinal momentum fraction, x, while a fuller dynamical picture of the proton’s interior can be also captured by introducing two-particle spatial density distributions. The latter yield the relative position of quarks and gluons with respect to one another in the transverse plane, providing a measure of the amount of correlations in the particles’ motion. I will illustrate a pathway to the extraction of these quantities from data, that leverages explainable AI to advance our understanding
the observables for deeply virtual exclusive experiments in terms of their fundamental quark and gluon structure.
