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Phenomenological assessment of proton mechanical properties from deeply virtual Compton scattering
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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.
Forward citations
Cited by 2 Pith papers
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Reconstruction of Gravitational Form Factors using Generative Machine Learning
A diffusion model trained on synthetic physics-motivated curves reconstructs the proton's A(t), J(t), D(t) from sparse data, extracting c8=-4.6±0.8, c9=-0.61±0.19, and D(0)=-4.3±0.8.
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Mechanical properties of the $\Omega^-$ baryon from gravitational form factors
Using QCD sum rules, the authors extract seven gravitational form factors of the Omega baryon and derive its internal energy, angular momentum, pressure, shear, radii, and D-terms.
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