An effective light-front wave function whose five-dimensional piece comes from holographic QCD yields pion gravitational form factors A(Q^{2}) and D(Q^{2}) that match lattice results after parameter tuning.
Generalized Parton Distributions in the valence region from Deeply Virtual Compton Scattering
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abstract
This work reviews the recent developments in the field of Generalized Parton Distributions (GPDs) and Deeply virtual Compton scattering in the valence region, which aim at extracting the quark structure of the nucleon. We discuss the constraints which the present generation of measurements provide on GPDs, and examine several state-of-the-art parametrizations of GPDs. Future directions in this active field are discussed.
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A neural network framework informed by lattice QCD uses all-order dispersion relations to significantly constrain both real and imaginary parts of Compton Form Factors extracted from DVCS proton data.
Quantum advantage in hadronic tomography should be evaluated selectively for CFFs, GPDs, TMDs, and GTMDs because their light-front and real-time correlation functions create ill-posed inverse problems that quantum algorithms may address at algorithmic, computational, and inference levels.
citing papers explorer
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Gravitational form factors of the pion in light-front holographic QCD
An effective light-front wave function whose five-dimensional piece comes from holographic QCD yields pion gravitational form factors A(Q^{2}) and D(Q^{2}) that match lattice results after parameter tuning.
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Constraining DVCS Compton Form Factors Using Lattice QCD informed Neural Network
A neural network framework informed by lattice QCD uses all-order dispersion relations to significantly constrain both real and imaginary parts of Compton Form Factors extracted from DVCS proton data.
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Toward selective quantum advantage in hadronic tomography:explicit cases from Compton form factors, GPDs, TMDs, and GTMDs
Quantum advantage in hadronic tomography should be evaluated selectively for CFFs, GPDs, TMDs, and GTMDs because their light-front and real-time correlation functions create ill-posed inverse problems that quantum algorithms may address at algorithmic, computational, and inference levels.