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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A new approach using near-side energy-energy correlators in dihadron fragmentation enables extraction of nucleon transversity PDF in collinear factorization without modeling intrinsic transverse momentum or dihadron resonances.
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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.
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Simplified approach to extracting nucleon transversity in collinear factorization using near-side energy-energy correlators
A new approach using near-side energy-energy correlators in dihadron fragmentation enables extraction of nucleon transversity PDF in collinear factorization without modeling intrinsic transverse momentum or dihadron resonances.