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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…
We construct a parametrization of deep-inelastic structure functions which retains information on experimental errors and correlations, and which does not introduce any theoretical bias while interpolating between existing data points. We…
We present a fit of the virtual-photon scattering asymmetry of polarized Deep Inelastic Scattering which combines a Monte Carlo technique with the use of a redundant parametrization based on Neural Networks. We apply the result to the…
We introduce the neural network approach to the parametrization of parton distributions. After a general introduction, we present in detail our approach to parametrize experimental data, based on a combination of Monte Carlo methods and…
POLDIS is a Monte Carlo program for polarized (semi-inclusive) deep inelastic scattering (DIS). Unpolarized DIS events are generated with the existing lepto-production event generators LEPTO for DIS and AROMA for Heavy Flavor production.…
In the past year, polarized deep inelastic scattering experiments at CERN and SLAC have obtained structure function measurements off proton, neutron and deuteron targets at a level of precision never before achieved. The measurements can be…
In this paper polarized valon distribution is derived from unpolarized valon distribution. In driving polarized valon distribution some unknown parameters exist which must be determined by fitting to experimental data. Here we have used…
We provide a determination of the isotriplet quark distribution from available deep--inelastic data using neural networks. We give a general introduction to the neural network approach to parton distributions, which provides a solution to…
We present the determination of a set of parton distributions of the nucleon, at next-to-leading order, from a global set of deep-inelastic scattering data: NNPDF1.0. The determination is based on a Monte Carlo approach, with neural…
We determine polarized parton distribution functions (PPDFs) and structure functions from recent experimental data of polarized deep inelastic scattering (DIS) on nuleons at next-to-next-to-leading order (NNLO) approximation in perturbative…
We present a determination of a set of polarized parton distributions (PDFs) of the nucleon, at next-to-leading order, from a global set of longitudinally polarized deep-inelastic scattering data: NNPDFpol1.0. The determination is based on…
Using a simple picture of the constituent quark as a composite system of point-like partons, we construct the polarized parton distributions by a convolution between constituent quark momentum distributions and constituent quark structure…
We will show an application of neural networks to extract information on the structure of hadrons. A Monte Carlo over experimental data is performed to correctly reproduce data errors and correlations. A neural network is then trained on…
We extract parton distribution functions (PDFs) and structure functions from recent experimental data of polarized lepton-DIS on nucleons at next-to-leading order (NLO) Quantum Chromodynamics. We apply the Jacobi polynomial method to the…
We discuss the determination of polarized parton distributions from charged-current deep-inelastic scattering experiments. We summarize the next-to-leading order treatment of charged-current polarized structure functions, their relation to…
We summarise the perturbative QCD analysis of the structure function data for g_1 from longitudinally polarized deep inelastic scattering from proton, deuteron and neutron targets, with particular emphasis on testing sum rules, determining…
We review the present status of polarized structure functions measured in deep-inelastic scattering. We discuss the x and Q^2 dependence of the structure function g_1, and how it can be used to test perturbative QCD at next-to-leading order…
A novel approach to parton distributions parameterization in terms of quantum statistical functions is here outlined. The description, already proposed in previous publications, is here improved by adding to the statistical distributions an…
Recent progress in the understanding of the nucleon is presented. The unpolarised structure functions are obtained with unprecedented precision from the combined H1 and ZEUS data and are used to extract proton parton distribution functions…
We consider the generic problem of performing a global fit to many independent data sets each with a different overall multiplicative normalization uncertainty. We show that the methods in common use to treat multiplicative uncertainties…