Phenomenological assessment of proton mechanical properties from deeply virtual Compton scattering

TL;DR

This paper reviews the current understanding of the proton's mechanical properties derived from deeply virtual Compton scattering data, highlighting the challenges and potential of future measurements at upcoming electron-ion colliders.

Contribution

It provides a phenomenological analysis of the proton's pressure distribution using neural network fits to existing data, assessing systematic assumptions and future prospects.

Findings

Current data limitations hinder precise pressure distribution extraction.

Neural network fits help evaluate systematic uncertainties.

Future colliders will enable more accurate measurements.

Abstract

A unique feature of generalised parton distributions is their relation to the QCD energy-momentum tensor. In particular, they provide access to the mechanical properties of the proton i.e. the distributions of pressure and shear stress induced by its quark and gluon structure. In principle the pressure distribution can be experimentally determined in a model-independent way from a dispersive analysis of deeply virtual Compton scattering data through the measurement of the subtraction constant. In practice the kinematic coverage and accuracy of existing experimental data make this endeavour a challenge. Elaborating on recent global fits of deeply virtual Compton scattering measurements using artificial neural networks, our analysis presents the current knowledge on this subtraction constant and assesses the impact of the most frequent systematic assumptions made in this field of research. This study will pave the way for future works when more precise data will become available, e.g. obtained in the foreseen electron-ion colliders EIC and EIcC.