arXiv subjects

The NNPDF Collaboration

Publications and source records attributed to The NNPDF Collaboration.

23 records · Page 2Linked to original sources

arXiv2011

On the Impact of NMC Data on NLO and NNLO Parton Distributions and Higgs Production at the Tevatron and the LHC

We discuss the impact of the treatment of NMC structure function data on parton distributions in the context of the NNPDF2.1 global PDF determination at NLO and NNLO. We show that the way these data are treated, and even their complete removal, has no effect on parton distributions at NLO, and at NNLO has an effect which is below one sigma. In particular, the Higgs production cross-section in the gluon fusion channel is very stable.

hep-ph↗

arXiv2010

Fitting Parton Distribution Data with Multiplicative Normalization Uncertainties

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 lead to systematic biases. We develop a method which is unbiased, based on a self--consistent iterative procedure. We demonstrate the use of this method by applying it to the determination of parton distribution functions with the NNPDF methodology, which uses a Monte Carlo method for uncertainty estimation.

hep-ph↗

arXiv2009

Precision determination of electroweak parameters and the strange content of the proton from neutrino deep-inelastic scattering

We use recent neutrino dimuon production data combined with a global deep-inelastic parton fit to construct a new parton set, NNPDF1.2, which includes a determination of the strange and antistrange distributions of the nucleon. The result is characterized by a faithful estimation of uncertainties thanks to the use of the NNPDF methodology, and is free of model or theoretical assumptions other than the use of NLO perturbative QCD and exact sum rules. Better control of the uncertainties of the strange and antistrange parton distributions allows us to reassess the determination of electroweak parameters from the NuTeV dimuon data. We perform a direct determination of the |V_cd| and |V_cs| CKM matrix elements, obtaining central values in agreement with the current global CKM fit: specifically we find |V_cd|=0.244\pm 0.019 and |V_cs|=0.96\pm 0.07. Our result for |V_cs| is more precise than any previous direct determination. We also reassess the uncertainty on the NuTeV determination of \sin^2θ_W through the Paschos-Wolfenstein relation: we find that the very large uncertainties in the strange valence momentum fraction are sufficient to bring the NuTeV result into complete agreement with the results from precision electroweak data.

hep-ph↗

arXiv2007

Neural network determination of parton distributions: the nonsinglet case

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 the problem of constructing a faithful and unbiased probability distribution of parton densities based on available experimental information. We discuss in detail the techniques which are necessary in order to construct a Monte Carlo representation of the data, to construct and evolve neural parton distributions, and to train them in such a way that the correct statistical features of the data are reproduced. We present the results of the application of this method to the determination of the nonsinglet quark distribution up to next--to--next--to--leading order, and compare them with those obtained using other approaches.

hep-ph↗

arXiv2005

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

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.

hep-ph↗