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Maria Ubiali

Publications and source records attributed to Maria Ubiali.

80 records · Page 5Linked to original sources

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

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.

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Combined PDF and strong coupling uncertainties at the LHC with NNPDF2.0

We present predictions for relevant LHC observables obtained with the NNPDF2.0 set. We compute the combined PDFs uncertainties on these observables, and show that combining errors in quadrature yields an excellent approximation to exact error propagation. We then compare the NNPDF2.0 results to the other global PDF fits using a common value of $α_s$. At LHC 7 TeV, reasonable agreement, both in central values and in uncertainties, is found for NNPDF2.0, CTEQ6.6 and MSTW08.

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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.

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The NNPDF1.2 parton set: implications for the LHC

Recently a new set of Parton Distribution Functions (NNPDF1.2) has been produced and released by the NNPDF Collaboration. The inclusion of dimuon data in the analysis allows a determination of the strange content of the proton with faithful uncertainty estimation together with a precision determination of electroweak parameters. In this contribution, we discuss some of the implications of the NNPDF1.2 set, and in particular of its uncertainty determination of the strange PDFs, for LHC phenomenology. First of all, we study the impact on the electroweak boson production cross-section, with special attention to the Z/W ratio. Then we revisit the top pair production cross-section, and perform a comparison of partonic fluxes between various PDF sets. Finally, we discuss the potential of using associated production of W with a charm quark at the Tevatron and the LHC to constrain the proton strangeness.

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A determination of parton distributions with faithful uncertainty estimation

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 networks used as unbiased interpolants. This method, previously discussed by us and applied to a determination of the nonsinglet quark distribution, is designed to provide a faithful and statistically sound representation of the uncertainty on parton distributions. We discuss our dataset, its statistical features, and its Monte Carlo representation. We summarize the technique used to solve the evolution equations and its benchmarking, and the method used to compute physical observables. We discuss the parametrization and fitting of neural networks, and the algorithm used to determine the optimal fit. We finally present our set of parton distributions. We discuss its statistical properties, test for its stability upon various modifications of the fitting procedure, and compare it to other recent parton sets. We use it to compute the benchmark W and Z cross sections at the LHC. We discuss issues of delivery and interfacing to commonly used packages such as LHAPDF.

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Update on Neural Network Parton Distributions: NNPDF1.1

We present recent progress within the NNPDF parton analysis framework. After a brief review of the results from the DIS NNPDF analysis, NNPDF1.0, we discuss results from an updated analysis with independent parametrizations for the strange and anti-strange distributions, denoted by NNPDF1.1. We examine the phenomenological implications of this improved analysis for the strange PDFs.

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NNPDF1.0 parton set for the LHC

We present the first NNPDF full set of Parton Distribution Functions from a comprehensive DIS analysis. This approach, combining a Monte Carlo sampling of the probability measure in the space of PDFs with the use of neural networks as interpolating functions, provides a faithful and statistically sound determination of the uncertainty in parton distributions. The features of the fit and the results are discussed in details as well as some preliminary phenomenological analysis

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Borel resummation of soft gluon radiation and higher twists

We show that the well-known divergence of the perturbative expansion of resummed results for processes such as deep-inelastic scattering and Drell-Yan in the soft limit can be treated by Borel resummation. The divergence in the Borel inversion can be removed by the inclusion of suitable higher twist terms. This provides us with an alternative to the standard 'minimal prescription' for the asymptotic summation of the perturbative expansion, and it gives us some handle on the role of higher twist corrections in the soft resummation region.

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