Francesco Cerutti

Impact of heavy quark masses on parton distributions and LHC phenomenology

Abstract We present a determination of the parton distributions of the nucleon from a global set of hard scattering data using the NNPDF methodology including heavy quark mass effects: NNPDF2.1. In comparison to the previous NNPDF2.0 parton determination, the dataset is enlarged to include deep-inelastic charm structure function data. We implement the FONLL-A general-mass scheme in the FastKernel framework and assess its accuracy by comparison to the Les Houches heavy quark benchmarks. We discuss the impact on parton distributions of the treatment of the heavy quark masses, and we provide a determination of the uncertainty in the parton distributions due to uncertainty in the masses. We assess the impact of these uncertainties on LHC observables by providing parton sets with different values of the charm and bottom quark masses. Finally, we construct and discuss parton sets with a fixed number of flavors.

Reweighting NNPDFs: The W lepton asymmetry

We present a method for incorporating the information contained in new datasets into an existing set of parton distribution functions without the need for refitting. The method involves reweighting the ensemble of parton densities through the computation of the chi-square to the new dataset. We explain how reweighting may be used to assess the impact of any new data or pseudodata on parton densities and thus on their predictions. We show that the method works by considering the addition of inclusive jet data to a DIS+DY fit, and comparing to the refitted distribution. We then use reweighting to determine the impact of recent high statistics lepton asymmetry data from the D0 experiment on the NNPDF2.0 parton set. We find that the D0 inclusive muon and electron data are perfectly compatible with the rest of the data included in the NNPDF2.0 analysis and impose additional constraints on the large-x d/u ratio. The more exclusive D0 electron datasets are however inconsistent both with the other datasets and among themselves, suggesting that here the experimental uncertainties have been underestimated.

Precision determination of

Abstract We determine the strong coupling α s from a next-to-leading order analysis of processes used for the NNPDF2.1 parton determination, which includes data from neutral and charged current deep-inelastic scattering, Drell–Yan and inclusive jet production. We find α s ( M Z ) = 0.1191 ± 0.0006 exp , where the uncertainty includes all statistical and systematic experimental uncertainties, but not purely theoretical uncertainties, which are expected to be rather larger. We study the dependence of the results on the dataset, by providing further determinations based respectively on deep-inelastic data only, and on HERA data only. The deep-inelastic fit gives the consistent result α s ( M Z ) = 0.1177 ± 0.0009 exp , but the result of the HERA-only fit is only marginally consistent. We provide evidence that individual data subsets can have runaway directions due to poorly determined PDFs, thus suggesting that a global dataset is necessary for a reliable determination.

\alpha_s

Abstract 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 an effect which is below one sigma at NNLO. In particular, the Higgs production cross-section in the gluon fusion channel is very stable.

using an unbiased global NLO parton set

We present a method developed by the NNPDF Collaboration that allows the inclusion of new experimental data into an existing set of parton distribution functions without the need for a complete refit. A Monte Carlo ensemble of PDFs may be updated by assigning each member of the ensemble a unique weight determined by Bayesian inference. The reweighted ensemble therefore represents the probability density of PDFs conditional on both the old and new data. This method is applied to the inclusion of W-lepton asymmetry data into the NNPDF2.1 fit producing a new PDF set, NNPDF2.2.