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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Derives t/P_z² and m_N²/P_z² kinematic corrections to the short-distance expansion of quasi-GPD matrix elements for lattice QCD applications.
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.
Lattice QCD now delivers high-precision results on hadron internal structure that directly support the scientific program of the Electron-Ion Collider.
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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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Finite-$t$ and target mass corrections for the short-distance expansion of quasi(pseudo) GPDs
Derives t/P_z² and m_N²/P_z² kinematic corrections to the short-distance expansion of quasi-GPD matrix elements for lattice QCD applications.
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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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Hadron Structure from lattice QCD in the context of the Electron-Ion Collider
Lattice QCD now delivers high-precision results on hadron internal structure that directly support the scientific program of the Electron-Ion Collider.