A nonparametric pixel-based Bayesian method integrates TMD evolution with generative AI and SVD to image parton distributions and reveal null TMDs unconstrained by 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.
An AI-assisted Bayesian framework extracts TMD PDFs from global Drell-Yan data using surrogate models for scalable MCMC sampling.
In the bag model, GTMD calculations are consistent, orbital angular momentum is tied to F_{1,4}^q through the Ji sum rule, and a deeper link to pretzelosity TMD is established.
citing papers explorer
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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 and SVD to image parton distributions and reveal null TMDs unconstrained by 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.
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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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GTMDs, orbital angular momentum, and pretzelosity
In the bag model, GTMD calculations are consistent, orbital angular momentum is tied to F_{1,4}^q through the Ji sum rule, and a deeper link to pretzelosity TMD is established.