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.
Unbiased determination of DVCS Compton Form Factors
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abstract
The extraction of Compton Form Factors (CFFs) in a global analysis of almost all Deeply Virtual Compton Scattering (DVCS) proton data is presented. The extracted quantities are DVCS sub-amplitudes and the most basic observables which are unambiguously accessible from this process. The parameterizations of CFFs are constructed utilizing the artificial neural network technique allowing for an important reduction of model dependency. The analysis consists of such elements as feasibility studies, training of neural networks with the genetic algorithm and a careful regularization to avoid over-fitting. The propagation of experimental uncertainties to extracted quantities is done with the replica method. The resulting parameterizations of CFFs are used to determine the subtraction constant through dispersion relations. The analysis is done within the PARTONS framework.
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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.
In the bag model, GTMD calculations are consistent, orbital angular momentum is tied to F_{1,4}^q through the Ji sum rule, and a deeper link to pretzelosity TMD is established.
citing papers explorer
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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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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.
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GTMDs, orbital angular momentum, and pretzelosity
In the bag model, GTMD calculations are consistent, orbital angular momentum is tied to F_{1,4}^q through the Ji sum rule, and a deeper link to pretzelosity TMD is established.