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Double Distributions and Pseudo-Distributions

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arxiv 2311.06007 v2 pith:VGNFXJHO submitted 2023-11-10 hep-ph hep-lat

A.V. Radyushkin This is my paper · ORCID
classification hep-phhep-lat
keywords gpdsdistributionsadvantagedoubleformalismpseudo-distributionsalphaanother
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We describe the approach to lattice extraction of Generalized Parton Distributions (GPDs) that is based on the use of the double distributions (DDs) formalism within the pseudo-distribution framework. The advantage of using DDs is that GPDs obtained in this way have the mandatory polynomiality property, a non-trivial correlation between $x$- and $\xi$-dependences of GPDs. Another advantage of using DDs is that the $D$-term appears as an independent entity in the DD formalism rather than a part of GPDs $H$ and $E$. We relate the $\xi$-dependence of GPDs to the width of the $\alpha$-profiles of the corresponding DDs, and discuss strategies for fitting lattice-extracted pseudo-distributions by DDs.

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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. Gluon Unpolarized, Polarized, and Transversity GPDs from Lattice QCD: Lorentz-Covariant Parametrization (Part I)

    hep-lat 2024-12 conditional novelty 7.0 of 10

    A Lorentz-covariant tensor basis and projection set is constructed that isolates all eight leading-twist gluon GPDs from lattice QCD matrix elements for spin-0 and spin-1/2 hadrons.

  2. Mechanical properties of the nucleon from the generalized parton distributions

    hep-ph 2025-01 conditional novelty 5.0 of 10

    Using a double-distribution GPD model constrained by elastic-scattering data, the paper fits DQ(0) = -3.37 ± 0.17 from Compton form factors and derives proton pressure, shear, and radii.

  3. A simple non-parametric reconstruction of parton distributions from limited Fourier information

    hep-lat 2024-12 conditional novelty 5.0 of 10

    A fixed-hyperparameter Gaussian-process prior recovers parton distributions from limited Ioffe-time data and yields more realistic small-x uncertainties than simple parametric fits.

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