A determination of parton distributions with faithful uncertainty estimation

Abstract

Abstract:We present the determination of a set of parton distributions of the nucleon, at nextto-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.1

Measurements of the top quark branching ratios into channels with leptons and quarks with the ATLAS detector

“…An event-by-event weighting is applied to a default MC@NLO sample that uses the central value of CT10 [28]. MSTW2008 [46] and NNPDF2.0 [47,48] sets are taken to estimate the systematic uncertainty due to the PDF. The uncertainty due to ISR/FSR is evaluated using the ALPGEN generator interfaced to the PYTHIA shower model, and by varying the parameters controlling ISR and FSR in a range consistent with experimental data [49].…”

Neural network parameterizations of electromagnetic nucleon form-factors

“…The ANN techniques have already been applied by NNPDF collaboration [45] to represent the nucleon and deuteron EM structure functions [43,[46][47][48][49][50]. The method is based on the large collection of networks [48] of the same architecture prepared on the artificial data sets generated from original experimental measurements.…”

“…With the help of the Bayesian framework the best solution is chosen. The NNPDF group considers one particular network architecture (2-5-3-1 type) to fit the data [48]. But some discussion of the dependence of results on the network architecture is presented.…”

Sensitivity of isolated photon production at TeV hadron colliders to the gluon distribution in the proton

“…CTEQ6.1M [22] and MRST04 [23], obtained a few years ago. Recently, new PDF sets have become available 3 -CTEQ6.6 [24], MSTW08 [25] and NNPDF1.2 [26] -which include the most up-to-date fits to data from deep-inelastic lepton-proton scattering and hadronic collisions as well as various theoretical improvements. The purpose of this paper is twofold.…”