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 producti...

Ամբողջական նկարագրություն

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Հիմնական հեղինակներ: Ball, R, Del Debbio, L, Forte, S, Guffanti, A, Latorre, J, Rojo, J, Ubiali, M
Ձևաչափ: Journal article
Լեզու:English
Հրապարակվել է: 2010
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author Ball, R
Del Debbio, L
Forte, S
Guffanti, A
Latorre, J
Rojo, J
Ubiali, M
author_facet Ball, R
Del Debbio, L
Forte, S
Guffanti, A
Latorre, J
Rojo, J
Ubiali, M
author_sort Ball, R
collection OXFORD
description 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. © 2010 Elsevier B.V.
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spelling oxford-uuid:43d69ab4-130b-442c-afdf-a33b80f2fe9d2022-03-26T14:57:53ZA first unbiased global NLO determination of parton distributions and their uncertaintiesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:43d69ab4-130b-442c-afdf-a33b80f2fe9dEnglishSymplectic Elements at Oxford2010Ball, RDel Debbio, LForte, SGuffanti, ALatorre, JRojo, JUbiali, MWe 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. © 2010 Elsevier B.V.
spellingShingle Ball, R
Del Debbio, L
Forte, S
Guffanti, A
Latorre, J
Rojo, J
Ubiali, M
A first unbiased global NLO determination of parton distributions and their uncertainties
title A first unbiased global NLO determination of parton distributions and their uncertainties
title_full A first unbiased global NLO determination of parton distributions and their uncertainties
title_fullStr A first unbiased global NLO determination of parton distributions and their uncertainties
title_full_unstemmed A first unbiased global NLO determination of parton distributions and their uncertainties
title_short A first unbiased global NLO determination of parton distributions and their uncertainties
title_sort first unbiased global nlo determination of parton distributions and their uncertainties
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