A determination of parton distributions with faithful uncertainty estimation

By R. Ball, Luigi Del Debbio, S. Forte, Alberto Guffanti, José I. Latorre, Juan Rojo, M. Ubiali. 2008

271 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/f78fee424081dd2f8c7d58da01837773a1ff013d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

A Monte Carlo global analysis of the Standard Model Effective Field Theory: the top quark sector (2019, methodology): “By exploiting these conceptual similarities, in this work we develop a novel strategy for global SMEFT analyses inspired by the NNPDF framework, successfully applied to the determination of the parton distributions of the proton [26–36] and of hadron fragmentation functions [37, 38].”
Deep learning in color: towards automated quark/gluon jet discrimination (2016, methodology): “ons have uncertainties associated with them. { 5 { Neural networks are capable of sensible error propagation when supplied with input values with errors. One approach taken by the NNPDF collaboration [45] is to sample several \replicated" datasets from the distribution determined by the measure”
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, methodology): “Neural networks for PDFs have been studied by the NNPDF collaboration for many years [583, 584].”
Parton distribution benchmarking with LHC data (2012, background): “3 includes exactly kinematical target mass corrections [30], known to be a substantial part of the higher twist corrections.”
Handbook of LHC Higgs Cross Sections: 1. Inclusive Observables (2011, methodology): “ted by taking means, variances, and covariances over this replica sample. NNPDF uses a Monte Carlo method, with each PDF replica obtained as the minimum χ2 which satisﬁes a cross-validation criterion [134, 252], and is thus largerthan the absolute minimum of the χ2. This method has been used in all ”
Electroweak and QCD corrections to top-pair hadroproduction in association with heavy bosons (2015, result): “The PDF uncertainties are computed, again through reweighting, by following the NNPDF methodology [57]; we report the 68% CL symmetric interval (that is the one that contains only 68 replicas out of a total of a hundred; this is done in order to avoid the problem of outliers, which is severe in this”
NLO Higgs boson production plus one and two jets using the POWHEG BOX, MadGraph4 and MCFM (2012, background): “The same calculations can easily be performed with any other available set [41, 42], but a study of pdf effects is beyond the scope of the present paper.”