Re-evaluation of the Gottfried sum using neural networks

Abstract

We provide a determination of the Gottfried sum from all available data, based on a neural network parametrization of the nonsinglet structure function F_2. We find S_G=0.244 +- 0.045, closer to the quark model expectation S_G=1/3 than previous results. We show that the uncertainty from the small x region is somewhat underestimated in previous determinations.

4 pages, 3 figures, latex with revtex

References in corpus (12)

• The Three-Loop Splitting Functions in QCD: The Non-Singlet Case
• Measurement of the proton and deuteron structure functions, F2p and F2d, and of the ratio sigma(L)/sigma(T)
• Flavor Asymmetry of Antiquark Distributions in the Nucleon
• Neural Network Parametrization of Deep-Inelastic Structure Functions
• Accurate Measurement of F2d/F2p and Rd-Rp
• A Kinematically Complete Measurement of the Proton Structure Function F2 in the Resonance Region and Evaluation of Its Moments
• Soft-gluon resummation effects on parton distributions
• Comparison of the Gottfried and Adler sum rules within the large-Nc expansion
• Non-singlet structure functions: Combining the leading logarithms resummation at small-x with DGLAP
• Order ${\alpha }_s^2$ perturbative QCD corrections to the Gottfried sum rule
• Evaluation of the isospin asymmetry of the nucleon structure functions with CLAS++
• New extraction of alpha_s(MZ) from proton DIS data

Cited by in corpus (10)

• Distribution Functions of the Nucleon and Pion in the Valence Region
• nNNPDF2.0: Quark Flavor Separation in Nuclei from LHC Data
• NNLO QCD contributions to the flavor non-singlet sector of ${\mathbf{F}}_2(\mathbf{x},{\mathbf{Q}}^2)$
• Nucleon Quark Distribution Functions from the Dyson-Schwinger Equations
• Extraction of the neutron F2 structure function from inclusive proton and deuteron deep-inelastic scattering data
• The Bjorken sum rule with Monte Carlo and Neural Network techniques
• The neural network approach to parton distribution functions
• Parton densities with the quark linear potential in the statistical approach
• Analytical perturbation theory and Nucleon structure function in infrared region
• Exploring the substructure of nucleons and nuclei with machine learning