Abstract
We studied the effects of NLO Q^2 evolution of generalized parton distributions (GPDs) using the aligned-jet model for the singlet quark and gluon GPDs at an initial evolution scale. We found that the skewness ratio for quarks is a slow logarithmic function of Q^2, reaching r^mathrm{S}=1.5{-}2 at Q^2=100 GeV^2 and r^mathrm{g} approx 1 for gluons in a wide range of Q^2. Using the resulting GPDs, we calculated the DVCS cross section on the proton in NLO pQCD and found that this model in conjunction with modern parameterizations of proton PDFs (CJ15 and CT14) provides a good description of the available H1 and ZEUS data in a wide kinematic range.
Highlights
generalized parton distributions (GPDs) are essentially non-perturbative quantities, which cannot be calculated from the first principles apart from first Mellin moments in special cases in lattice Quantum Chromodynamics (QCD) [14,15]
Evolution of GPDs with an increase of the resolution scale Q2 is predicted by the QCD Dokshitzer– Gribov–Lipatov–Altarelli–Parisi (DGLAP) evolution equations modified to the case of GPDs, which are presently known to the next-to-leading order (NLO) accuracy [16,17,18]
Note that in this work, we focus on the quark singlet q (q + q) and gluon GPDs: valence quark GPDs do not mix with singlet quark and gluon GPDs under the DGLAP evolution and do not appreciably contribute to the deeply virtual Compton scattering (DVCS) amplitude at high energies
Summary
GPDs are essentially non-perturbative quantities, which cannot be calculated from the first principles apart from first Mellin moments in special cases in lattice QCD [14,15]. One of directions of phenomenological studies of GPDs is to determine the non-perturbative input for these evolution equations. After early studies of GPDs using various dynamical models of the nucleon structure [19,20,21,22,23,24,25,26,27], one currently focuses on parameterizations of GPDs, which are determined from fitting the available data. The two main contemporary approaches include the flexible parameterization based on the conformal expansion of GPDs [28,29,30,31] and global fits of GPDs [32,33,34,35], which use the double distribution (DD) model [36,37,38,39,40] in the Vanderhaeghen–Guichon– Guidal (VGG) framework; see for details [34]. One should mention a pioneering study of global QCD fits of GPDs within the neural network approach [41]
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