Lattice QCD pseudo-distributions at m_π=358 MeV are inverted via multidimensional Gaussian process regression to reconstruct the full kinematic dependence of GPDs H^{u-d} and E^{u-d} while directly extracting double distributions.
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Presents leading-order calculations of exclusive dijet electroproduction cross sections via GPDs in double distribution model, highlighting valence contributions at large x_P and azimuthal modulations consistent with ZEUS data for beta greater than or equal to 0.4.
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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Reconstructing the full kinematic dependence of GPDs from pseudo-distributions
Lattice QCD pseudo-distributions at m_π=358 MeV are inverted via multidimensional Gaussian process regression to reconstruct the full kinematic dependence of GPDs H^{u-d} and E^{u-d} while directly extracting double distributions.
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Probing GPDs in exclusive electroproduction of dijets
Presents leading-order calculations of exclusive dijet electroproduction cross sections via GPDs in double distribution model, highlighting valence contributions at large x_P and azimuthal modulations consistent with ZEUS data for beta greater than or equal to 0.4.
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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.