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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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.
MSbar PDFs are non-negative above an estimated perturbative scale, with clarification of the argument's domain of validity.
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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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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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On the positivity of MSbar parton distributions
MSbar PDFs are non-negative above an estimated perturbative scale, with clarification of the argument's domain of validity.