Erratum to: “A determination of parton distributions with faithful uncertainty estimation” [Nucl. Phys. B 809 (2009) 1–63]

Ball, Richard D.; Debbio, Luigi Del; Forte, Stefano; Guffanti, A.; Latorre, José I.; Piccione, Andrea; Rojo, Juan; Ubiali, Maria

doi:10.1016/j.nuclphysb.2009.02.027

Cited by 82 publications

(92 citation statements)

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“…The latter are designed to recognize structure in a given data set and to quantify the statistical properties of this structure. They have already been successfully applied to the task of fitting hadronic structure functions to the data, for standard PDFs [166,167], or electromagnetic form factors [168]. It is a mathematical theorem that neural networks are able to approximate any smooth function [169], so they can be used as a GPD model without danger of introducing bias from the model parameters.…”

Section: Fitting Methodsmentioning

confidence: 99%

GPD phenomenology and DVCS fitting

2016

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Abstract. We review the phenomenological framework for accessing Generalized Parton Distributions (GPDs) using measurements of Deeply Virtual Compton Scattering (DVCS) from a proton target. We describe various GPD models and fitting procedures, emphasizing specific challenges posed both by the internal structure and properties of the GPD functions and by their relation to observables. Bearing in mind forthcoming data of unprecedented accuracy, we give a set of recommendations to better define the pathway for a precise extraction of GPDs from experiment.

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Section: Fitting Methodsmentioning

confidence: 99%

GPD phenomenology and DVCS fitting

2016

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“…In a recent paper, we presented the NNPDFpol1.0 parton set [1], a first unbiased determination of polarized parton distribution functions (PDFs) of the proton and their associated uncertainties based on the NNPDF methodology [2][3][4][5]. This methodology differs from that used in other recent next-to-leading order (NLO) analyses [6][7][8][9][10], in that it relies on a Monte Carlo sampling and representation of PDFs, and it uses a parametrization of PDFs based on neural networks with a very large number of free parameters.…”

Section: A Global Polarized Pdf Determinationmentioning

confidence: 99%

A first unbiased global determination of polarized PDFs and their uncertainties

Nocera¹,

Ball²,

Forte³

et al. 2014

Nuclear Physics B

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We present a first global determination of spin-dependent parton distribution functions (PDFs) and their uncertainties using the NNPDF methodology: NNPDFpol1.1. Longitudinally polarized deep- inelastic scattering data, already used for the previous NNPDFpol1.0 PDF set, are supplemented with the most recent polarized hadron collider data for inclusive jet and W boson production from the STAR and PHENIX experiments at RHIC, and with open-charm production data from the COMPASS experiment, thereby allowing for a separate determination of the polarized quark and anti-quark PDFs, and an improved determination of the medium- and large-x polarized gluon PDF. We study the phe- nomenological implications of the NNPDFpol1.1 set, and we provide predictions for the longitudinal double-spin asymmetry for semi-inclusive pion production at RHIC

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“…Several collaborations have published updated parton densitites providing valuable input for precise hadron collider phenomenology. Recently, new ideas have emerged on the extraction of parton densities from experimental data, using Artificial Neural Network methods [41]. In addition, several improvements have been made on the theoretical treatment of the…”

Section: The Legacy Of Hera and Parton Densities For The Tevatron Andmentioning

confidence: 99%