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.
Bayesian approach to inverse problems: an application to NNPDF closure testing
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The top-quark pole mass is determined to be 172.80 ± 0.26 GeV from a global NNPDF analysis at approximate N³LO QCD including NLO QED, EW, and toponium corrections.
Radiative corrections applied to MINERvA antineutrino data yield updated values for the nucleon axial-vector form factor G_A and axial radius.
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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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A Determination of the Top Mass from a Global PDF Analysis
The top-quark pole mass is determined to be 172.80 ± 0.26 GeV from a global NNPDF analysis at approximate N³LO QCD including NLO QED, EW, and toponium corrections.
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Nucleon axial-vector form factor and radius from radiatively-corrected antineutrino scattering data
Radiative corrections applied to MINERvA antineutrino data yield updated values for the nucleon axial-vector form factor G_A and axial radius.