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.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
hep-ph 3years
2026 3verdicts
UNVERDICTED 3roles
background 2polarities
background 2representative citing papers
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.
citing papers explorer
-
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.
-
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.
-
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.