Presents a linear PDF parametrization from dimensionality-reduced neural network bases for efficient Bayesian inference, tested via multi-closure tests on synthetic deep inelastic scattering data.
Novel parton density determination code
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An AI-assisted Bayesian framework extracts TMD PDFs from global Drell-Yan data using surrogate models for scalable MCMC sampling.
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A linear PDF model for Bayesian inference
Presents a linear PDF parametrization from dimensionality-reduced neural network bases for efficient Bayesian inference, tested via multi-closure tests on synthetic deep inelastic scattering data.
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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.