RT Journal Article
T1 Unraveling generalized parton distributions through Lorentz symmetry and partial DGLAP knowledge
A1 Dall’Olio, P.
A1 Soto Borrero, Feliciano Carlos de
A1 Mezrag, Cédric
A1 Morgado Chávez, José Manuel
A1 Moutarde, Hervé
A1 Rodríguez Quintero, José
A1 Sznajder, P.
A1 Segovia González, Jorge
AB Relying on the polynomiality property of generalized parton distributions, which roots on Lorentzcovariance, we prove that it is enough to know them at vanishing and low skewness within the Dokshitzer-Gribov-Lipatov-Altarelli-Parisi region to obtain a unique extension to their entire support up to a D term.We put this idea in practice using two methods: reconstruction using artificial neural networks and finiteelementsmethods. We benchmark our results against standard models for generalized parton distributions.In agreement with the formal expectation, we obtain very a accurate reconstruction for a maximal value ofthe skewness as low as 20% of the longitudinal momentum fraction. This result might be relevant forreconstruction of generalized parton distribution from experimental and lattice QCD data, wherecomputations are, for now, restricted in skewness
PB American Physical Society
SN 2470-0010
SN 2470-0029 (electrónico)
YR 2024
FD 2024-05
LK https://hdl.handle.net/10272/24294
UL https://hdl.handle.net/10272/24294
LA eng
NO Dall’Olio, P., De Soto, F., Mezrag, C., Morgado Chávez, J. M., Moutarde, H., Rodríguez-Quintero, J., Sznajder, P., & Segovia, J. (2024). Unraveling generalized parton distributions through Lorentz symmetry and partial DGLAP knowledge. In Physical Review D (Vol. 109, Issue 9). American Physical Society (APS). https://doi.org/10.1103/physrevd.109.096013
NO J. M. M. C. thanks V. Bertone, B. Blossier, M. Riberdy,and T. San Jos´e for valuable discussions and comments.The work from C. M., J.M.M. C., and H. M. has beensupported by the GLUODYNAMICS project funded by“P2IO LabEx (ANR-10-LABX-0038)” in the framework ofInvestissements d’Avenir (ANR-11-IDEX-0003-01), managedby the Agence Nationale de la Recherche (ANR),France; and by the European Union’s Horizon 2020research and innovation program under Grant AgreementSTRONG 2020—No. 824093. P. D. O. acknowledgesfinancial support from “Junta de Andalucía” through“Programa Operativo FEDER de Andalucía 2014-2020 (PAIDI)” under the Project No. P20_00764. The work ofP. D. O., F. D. S., J. R.-Q., and J. S. was supported by the“Spanish Ministerio de Ciencia e Innovación (MICINN)”through Grants No. PID2019-107844-GB-C22 andNo. PID2022-140440-NB-C22
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026