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Proton internal pressure from deeply virtual Compton scattering on collider kinematics

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arxiv 2410.13518 v1 pith:HCXNLGYV submitted 2024-10-17 hep-ph nucl-th

Hervé Dutrieux , Thibaud Meisgny , Cédric Mezrag , Hervé Moutarde This is my paper
classification hep-phnucl-th
keywords distributionscolliderconnectiondatadegreesexperimentalgeneralisedkinematic
verification ladder T0 review T1 audit T2 compute T3 formal
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The unique experimental connection to the QCD energy-momentum tensor offered by generalised parton distributions has been strongly highlighted in the past few years with attempts to extract the pressure and shear forces distributions within the nucleon. If, in principle, this can be performed in a model independent way from experimental data, in practice, the current limited precision and kinematic coverage make such an extraction very challenging. Moreover, the limitation to a leading-order description in the strong coupling of the data has provided only an indirect and weakly sensitive access to gluon degrees of freedoms, solely through their mixing to quarks via evolution. In this paper we address this issue by pro viding a next-to-leading order formalism allowing a reanalysis of global fits with genuine gluonic degrees of freedom. In addition, we provide an estimate of the reduction in uncertainty that could stem from the extended kinematic range relevant for the future Electron Ion Collider. Finally, we stress the connection between the analysis of the dispersion relation in terms of generalised parton distributions and the deconvolution problem.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Reconstructing the full kinematic dependence of GPDs from pseudo-distributions

    hep-lat 2026-04 unverdicted novelty 8.0 of 10

    Lattice QCD pseudo-distributions at m_π=358 MeV are inverted via multidimensional Gaussian process regression to reconstruct the full kinematic dependence of GPDs H^{u-d} and E^{u-d} while directly extracting double d...

  2. Constraining DVCS Compton Form Factors Using Lattice QCD informed Neural Network

    hep-ph 2026-06 unverdicted novelty 5.0 of 10

    A neural network framework informed by lattice QCD uses all-order dispersion relations to significantly constrain both real and imaginary parts of Compton Form Factors extracted from DVCS proton data.

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