Conformal moments of the two-loop DVCS coefficient functions have been computed with a new technique.
Kumeriˇ cki and D
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abstract
We give a partonic interpretation for the deeply virtual Compton scattering (DVCS) measurements of the H1 and ZEUS collaborations in the small-x_B region in terms of generalized parton distributions. Thereby we have a closer look at the skewness effect, parameterization of the t-dependence, revealing the chromomagnetic pomeron, and at a model dependent access to the anomalous gravitomagnetic moment of nucleon. We also quantify the reparameterization of generalized parton distributions resulting from the inclusion of radiative corrections up to next-to-next-to-leading order. Beyond the leading order approximation, our findings are compatible with a `holographic' principle that would arise from a (broken) SO(2,1) symmetry. Utilizing our leading-order findings, we also perform a first model dependent dispersion relation fit of HERMES and JLAB DVCS measurements. From that we extract the generalized parton distribution H on its cross-over line and predict the beam charge-spin asymmetry, measurable at COMPASS.
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Replacing the rapidity argument of the dipole amplitude with ln min{1/|x|, 1/|ξ|} and refining initial conditions for non-linear evolution can eliminate two R-factors in small-x shockwave calculations.
Quantum-inspired deep neural networks extract Compton form factors from JLab data with higher predictive accuracy and tighter uncertainties than classical DNNs on pseudodata benchmarks, then applied to real measurements.
citing papers explorer
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Conformal moments of the two-loop coefficient functions in DVCS
Conformal moments of the two-loop DVCS coefficient functions have been computed with a new technique.
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On the Two $R$-Factors in the Small-$x$ Shockwave Formalism
Replacing the rapidity argument of the dipole amplitude with ln min{1/|x|, 1/|ξ|} and refining initial conditions for non-linear evolution can eliminate two R-factors in small-x shockwave calculations.
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Compton Form Factor Extraction using Quantum Deep Neural Networks
Quantum-inspired deep neural networks extract Compton form factors from JLab data with higher predictive accuracy and tighter uncertainties than classical DNNs on pseudodata benchmarks, then applied to real measurements.