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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A nonparametric pixel-based Bayesian method integrates TMD evolution with generative AI sampling and SVD to extract parton distributions and identify unconstrained null components from multi-scale observables.
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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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TMDs in the Lens of Generative AI: A Pixel-Based Approach to Partonic Imaging
A nonparametric pixel-based Bayesian method integrates TMD evolution with generative AI sampling and SVD to extract parton distributions and identify unconstrained null components from multi-scale observables.