Abstract

Relying on the collinear factorization approach, we demonstrate that H1 and ZEUS measurements of exclusive light vector meson and photon electroproduction cross sections can be simultaneously described for photon virtualities of $${\mathcal {Q}}\gtrsim 2\, \mathrm{GeV}$$ . Our findings reveal that quark exchanges are important in this small $$x_\mathrm{Bj}$$ region and that in leading order approximation the gluonic component is suppressed, e.g., the skewness ratio can be much smaller than one.

Highlights

  • In the collinear factorization approach the t-dependencies of the longitudinally deeply virtual meson production (DVMP) and deeply virtual Compton scattering (DVCS) cross sections arise from those of generalized parton distributions (GPDs)

  • The exponential t-slope of DVMP cross sections for light vector mesons decreases with growing Q2 and approaches at moderate photon virtuality the DVCS one, they are with B(Q2 ∼ 4 GeV2) ∼ 6/GeV2 larger than the B J/ ∼ 4.5/GeV2 slope of J/ electroproduction, see, e.g., Fig. 4 in [39]

  • We present the first simultaneous GPD fits to DVCS and DVMP measurements, which illustrate that in spite of various theoretical and experimental uncertainties the collinear framework might be applicable in the small xBj region for Q2 4 GeV2

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Summary

Introduction

Taking the universal t-dependency criteria literally, it has been argued from the experimental findings that the onset of the perturbative regime appears at rather large photon virtuality of Q2 ∼ 15 GeV2 or so This is somehow supported by numerical studies in which model dependent NLO corrections turn out to be large [34,35,40] and, at this order the residual factorization and renormalization scale dependencies might be still rather strong. We present the first simultaneous GPD fits to DVCS and DVMP measurements, which illustrate that in spite of various theoretical and experimental uncertainties the collinear framework might be applicable in the small xBj region for Q2 4 GeV2.

Formalism and GPD modeling
Collinear factorization versus measurements
Summary and conclusions
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