A first unbiased global NLO determination of parton distributions and their uncertainties

2010/02/28 by Richard D. Ball, Luigi Del Debbio, Stefano Forte +5 · 2 citations
Physics and Astronomy · #Deep inelastic scattering #HERA #High-Energy Particle Collisions Research #Inelastic scattering #Large Hadron Collider #Normalization (sociology) #Parametrization (atmospheric modeling) #Particle physics #Particle physics theoretical and experimental studies #Parton #Physics #Quantum Chromodynamics and Particle Interactions #Quantum chromodynamics #Radiative transfer #Scattering #hep-ex #hep-ph

paper · pdf · doi:10.1016/j.nuclphysb.2010.05.008

published as Nucl.Phys.B838:136-206,2010 · 86 pages, 41 figures. PDF sets available from http://sophia.ecm.ub.es/nnpdf/nnpdf_pdfsets.htm and from LHAPDF. Final version to be published in Nucl. Phys. B. Various typos corrected and small clarifications added, fig. 4 added, extended discussion of data consistency especially in sect 5.1 and 5.4

arxiv created 2010/05/19 · openalex publication_date 2010/05/21 · arxiv updated 2014/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08

Abstract

We present a determination of the parton distributions of the nucleon from a global set of hard scattering data using the NNPDF methodology: NNPDF2.0. Experimental data include deep-inelastic scattering with the combined HERA-I dataset, fixed target Drell-Yan production, collider weak boson production and inclusive jet production. Next-to-leading order QCD is used throughout without resorting to K-factors. We present and utilize an improved fast algorithm for the solution of evolution equations and the computation of general hadronic processes. We introduce improved techniques for the training of the neural networks which are used as parton parametrization, and we use a novel approach for the proper treatment of normalization uncertainties. We assess quantitatively the impact of individual datasets on PDFs. We find very good consistency of all datasets with each other and with NLO QCD, with no evidence of tension between datasets. Some PDF combinations relevant for LHC observables turn out to be determined rather more accurately than in any other parton fit.

Citations

• Review of Particle Physics
• Neural network parametrization of spectral functions from hadronic tau decays and determination of QCD vacuum condensates
• Determination of the atmospheric neutrino fluxes from atmospheric neutrino data
• Combined measurement and QCD analysis of the inclusive e ± p scattering cross sections at HERA
• Fixed target Drell-Yan data and NNLO QCD fits of parton distribution functions
• A determination of parton distributions with faithful uncertainty estimation
• Measurement of the forward-backward charge asymmetry fromW→eνproduction inpp¯collisions ats=1.96 TeV
• Measurement of charged current deep inelastic scattering cross sections with a longitudinally polarised electron beam at HERA
• Revisiting the global electroweak fit of the Standard Model and beyond with Gfitter
• Measurement of the Inclusive Jet Cross Section in pp Collisions at √s = 1.96 TeV
• Measurement of the shape of the boson rapidity distribution forpp¯→Z/γ*→e+e−+Xevents produced atsof 1.96 TeV
• Big Bang Nucleosynthesis (in "The Review of Particle Properties" 2004)
• Precise measurement of dimuon production cross sections inνμFeandν¯μFedeep inelastic scattering at the Fermilab Tevatron
• Direct Measurement of theWProduction Charge Asymmetry inpp¯Collisions ats=1.96 TeV
• The NNPDF1.2 parton set: implications for the LHC
• Update on Neural Network Parton Distributions: NNPDF1.1

Cited by

• Weakly supervised anomaly detection with event-level variables
• Search for physics beyond the standard model in events with τ leptons, jets, and large transverse momentum imbalance in pp collisions at √(s)=7 TeV