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.
Adams, Joshua Bautista, Marija Cuic, et al
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Joint MCMC likelihood of JLab cross sections and asymmetries constrains twist-two chiral-odd CFFs and helicity amplitudes for a fixed DVMP kinematic bin.
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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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Markov chain Monte Carlo (MCMC) based Likelihood Extraction of Chiral-Odd Compton Form Factors from Deeply Virtual Exclusive Experiments
Joint MCMC likelihood of JLab cross sections and asymmetries constrains twist-two chiral-odd CFFs and helicity amplitudes for a fixed DVMP kinematic bin.