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Unbiased determination of the proton structure function F_2^p with faithful uncertainty estimation

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it
abstract

We construct a parametrization of the deep-inelastic structure function of the proton F_2 based on all available experimental information from charged lepton deep-inelastic scattering experiments. The parametrization effectively provides a bias-free determination of the probability measure in the space of structure functions, which retains information on experimental errors and correlations. The result is obtained in the form of a Monte Carlo sample of neural networks trained on an ensemble of replicas of the experimental data. We discuss in detail the techniques required for the construction of bias-free parameterizations of large amounts of structure function data, in view of future applications to the determination of parton distributions based on the same method.

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dataset 2 method 1

citation-polarity summary

fields

hep-ph 3

years

2026 2 2019 1

verdicts

UNVERDICTED 3

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representative citing papers

Probing Proton Structure via Physics-Guided Neural Networks in Holographic QCD

hep-ph · 2026-04-03 · unverdicted · novelty 7.0

A physics-guided neural network embedding AdS5 Dirac equation and holographic Pomeron fits SLAC proton F2 data with chi-squared per degree of freedom of 0.91 and identifies a kinematic crossover at x approximately 0.19 while recovering Pomeron intercept of 1.0786.

Lepton interactions from GeV to EeV

hep-ph · 2026-06-04 · unverdicted · novelty 3.0

Phenomenological study predicting incomplete tau polarization at FASER2, observable neutrino and muon trident processes, and contributions to hadron structure from IceCube neutrino events.

VBSCan Thessaloniki 2018 Workshop Summary

hep-ph · 2019-06-26 · unverdicted · novelty 1.0

The document reports the first year of activity of the VBSCan COST Action network on vector-boson scattering phenomenology and experiments from a 2018 workshop.

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Showing 3 of 3 citing papers.

  • Probing Proton Structure via Physics-Guided Neural Networks in Holographic QCD hep-ph · 2026-04-03 · unverdicted · none · ref 55

    A physics-guided neural network embedding AdS5 Dirac equation and holographic Pomeron fits SLAC proton F2 data with chi-squared per degree of freedom of 0.91 and identifies a kinematic crossover at x approximately 0.19 while recovering Pomeron intercept of 1.0786.

  • Lepton interactions from GeV to EeV hep-ph · 2026-06-04 · unverdicted · none · ref 145 · internal anchor

    Phenomenological study predicting incomplete tau polarization at FASER2, observable neutrino and muon trident processes, and contributions to hadron structure from IceCube neutrino events.

  • VBSCan Thessaloniki 2018 Workshop Summary hep-ph · 2019-06-26 · unverdicted · none · ref 51 · internal anchor

    The document reports the first year of activity of the VBSCan COST Action network on vector-boson scattering phenomenology and experiments from a 2018 workshop.