Unpolarized GPDs and GTMDs at small x with non-zero skewness are expressed via the dipole amplitude N and odderon O with modified rapidity Y = ln min{1/|x|, 1/|ξ|}.
GPD phenomenology and DVCS fitting - Entering the high-precision era
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abstract
We review the phenomenological framework for accessing Generalized Parton Distributions (GPDs) using measurements of Deeply Virtual Compton Scattering (DVCS) from a proton target. We describe various GPD models and fitting procedures, emphasizing specific challenges posed both by the internal structure and properties of the GPD functions and by their relation to observables. Bearing in mind forthcoming data of unprecedented accuracy, we give a set of recommendations to better define the pathway for a precise extraction of GPDs from experiment.
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Gravitational form factors of pion and kaon are computed in BLFQ; A(Q^2) agrees with lattice QCD while D(Q^2) is enhanced at low Q^2 due to small-x and zero-mode sensitivity in the truncated model.
A neural network trained solely on integral observables from a known GPD model recovers the main features of the underlying distributions in a closure test.
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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Unpolarized GPDs at small $x$ and non-zero skewness
Unpolarized GPDs and GTMDs at small x with non-zero skewness are expressed via the dipole amplitude N and odderon O with modified rapidity Y = ln min{1/|x|, 1/|ξ|}.
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Gravitational form factors of light mesons from Basis Light-Front Quantization
Gravitational form factors of pion and kaon are computed in BLFQ; A(Q^2) agrees with lattice QCD while D(Q^2) is enhanced at low Q^2 due to small-x and zero-mode sensitivity in the truncated model.
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Neural Network Representation of Generalized Parton Distributions (NNGPD)
A neural network trained solely on integral observables from a known GPD model recovers the main features of the underlying distributions in a closure test.
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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.