Document Type
Article
Publication Date
2025
DOI
10.1103/qct5-y7rp
Publication Title
Physical Review Letters
Volume
135
Issue
26
Pages
261903
Abstract
We report the first global extraction of generalized parton distributions, GUMP 1.0, by combining deeply virtual Compton scattering and ρ-meson production data from Jefferson Lab and the Hadron-Electron Ring Accelerator with global fits of parton distribution functions, charge form factors, and lattice quantum chromodynamics simulations. Using a conformal moment space parametrization, we achieve a unified description across low- and high-x regions at next to leading order accuracy in perturbative corrections. The results provide state-of-the-art generalized parton distributions consistent with almost all known facts, enabling three-dimensional nucleon imaging in impact parameter space and, at the same time, establishing a benchmark for future theoretical and experimental studies of the nucleon structure.
Rights
© 2025 by the authors.
Published under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by SCOAP3.
Data Availability
Article states: "The data that support the findings of this article are openly available [112]."
Original Publication Citation
Guo, Y., Aslan, F. P., Ji, X., & Santiago, M. G. (2025). First global extraction of generalized parton distributions from experiment and lattice data with next-to-leading-order accuracy. Physical Review Letters, 135(26), Article 261903. https://doi.org/10.1103/qct5-y7rp
ORCID
0000-0003-0770-6279 (Gabriel-Santiago)
Repository Citation
Guo, Yuxun; Aslan, Fatma P.; Ji, Xiangdong; and Santiago, M. Gabriel, "First Global Extraction of Generalized Parton Distributions From Experiment and Lattice Data with Next-to-Leading-Order Accuracy" (2025). Physics Faculty Publications. 1033.
https://digitalcommons.odu.edu/physics_fac_pubs/1033
Supplemental Material