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Extraction of the Parton Momentum-Fraction Dependence of Generalized Parton Distributions from Exclusive Photoproduction

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arxiv 2305.15397 v2 pith:Z5KKFTLP submitted 2023-05-24 hep-ph hep-exhep-latnucl-exnucl-th

classification hep-phhep-exhep-latnucl-exnucl-th
keywords dependencegpdspartonphotoproductiondistributionsexclusiveextractinggeneralized
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

The $x$ dependence of hadrons' generalized parton distributions (GPDs) $\mathcal{F}(x,\xi,t)$ is the most difficult to extract from the existing known processes, while the $\xi$ and $t$ dependence are uniquely determined by the kinematics of the scattered hadron. We study the single diffractive hard exclusive processes for extracting GPDs in the photoproduction. We demonstrate quantitatively the enhanced sensitivity on extracting the $x$ dependence of various GPDs from the photoproduction cross sections, as well as the asymmetries constructed from photon polarization and hadron spin that could be measured at JLab Hall D by GlueX Collaboration and future facilities.

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Cited by 3 Pith papers

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

  1. Parton distributions in the shockwave formalism

    hep-ph 2025-10 conditional novelty 7.0 of 10

    A single shockwave Feynman-rule framework reproduces and extends prior small-x parton distributions, yielding new quark GTMD/GPD/PDF and diffractive-TMD results.

  2. GUMP1.0 -- First global extraction of generalized parton distributions from experiment and lattice data with NLO accuracy

    hep-ph 2025-09 conditional novelty 6.0 of 10

    A new global extraction, GUMP1.0, fits generalized parton distributions to 2,646 experimental and lattice data points at NLO accuracy and uses them to image the proton and decompose its spin.

  3. Toward an event-level analysis of hadron structure using differential programming

    hep-ph 2025-07 conditional novelty 4.0 of 10

    LOITS is a differentiable sampling method, demonstrated in a GAN closure test, that maps sampled events back to the parameters of a target density for event-level inference.

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