Tree-level and one-loop collinear matching relations are computed for leading-power gluon TMD PDFs, yielding the first Wandzura-Wilczek approximation for the gluon worm-gear T distribution along with a closed-form mass correction series.
Canonical reference
Barry et al.,First simultaneous analysis of transverse momentum dependent and collinear parton distributions in the proton,2510.13771
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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.
A framework based on linear response and influence functions maps data sensitivities in global QCD analyses to show how experiments determine central values, uncertainties, and correlations of non-perturbative functions.
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.
A deep neural network framework learns the structure kernel from Fermilab E288 and E605 Drell-Yan data and reconstructs x- and Q-dependent unpolarized TMDs via differentiable k-space quadrature.
An AI-assisted Bayesian framework extracts TMD PDFs from global Drell-Yan data using surrogate models for scalable MCMC sampling.
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Collinear matching for leading power gluon transverse momentum distributions
Tree-level and one-loop collinear matching relations are computed for leading-power gluon TMD PDFs, yielding the first Wandzura-Wilczek approximation for the gluon worm-gear T distribution along with a closed-form mass correction series.
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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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Mapping data sensitivities in global QCD analysis with linear response and influence functions
A framework based on linear response and influence functions maps data sensitivities in global QCD analyses to show how experiments determine central values, uncertainties, and correlations of non-perturbative functions.
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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.
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Deep Neural Network extraction of Unpolarized Transverse Momentum Distributions
A deep neural network framework learns the structure kernel from Fermilab E288 and E605 Drell-Yan data and reconstructs x- and Q-dependent unpolarized TMDs via differentiable k-space quadrature.
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AI-assisted modeling and Bayesian inference of unpolarized quark transverse momentum distributions from Drell-Yan data
An AI-assisted Bayesian framework extracts TMD PDFs from global Drell-Yan data using surrogate models for scalable MCMC sampling.
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