Physics-informed neural networks extract a model-independent color dipole amplitude from inclusive HERA data that predicts exclusive J/ψ photoproduction cross-sections without parameter retuning.
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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.
Proposes a scheme-invariant stratified factorization algebra framework that derives the DIS convolution formula independently of collinear scheme or operator basis choices.
A neural network trained solely on integral observables from a known GPD model recovers the main features of the underlying distributions in a closure test.
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Extraction of the color dipole amplitude with physics-informed neural networks
Physics-informed neural networks extract a model-independent color dipole amplitude from inclusive HERA data that predicts exclusive J/ψ photoproduction cross-sections without parameter retuning.
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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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Scheme-invariant stratified factorization algebras for inclusive deep inelastic scattering
Proposes a scheme-invariant stratified factorization algebra framework that derives the DIS convolution formula independently of collinear scheme or operator basis choices.
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Neural Network Representation of Generalized Parton Distributions (NNGPD)
A neural network trained solely on integral observables from a known GPD model recovers the main features of the underlying distributions in a closure test.