arXiv subjects

NNPDF Collaboration

Publications and source records attributed to NNPDF Collaboration.

4 recordsLinked to original sources

arXiv2011

Reweighting and Unweighting of Parton Distributions and the LHC W lepton asymmetry data

We develop in more detail our reweighting method for incorporating new datasets in parton fits based on a Monte Carlo representation of PDFs. After revisiting the derivation of the reweighting formula, we show how to construct an unweighted PDF replica set which is statistically equivalent to a given reweighted set. We then use reweighting followed by unweighting to test the consistency of the method, specifically by verifying that results do not depend on the order in which new data are included in the fit via reweighting. We apply the reweighting method to study the impact of LHC W lepton asymmetry data on the NNPDF2.1 set. We show how these data reduce the PDF uncertainties of light quarks in the medium and small x region, providing the first solid constraints on PDFs from LHC data.

hep-ph↗

arXiv2008

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.

hep-ph↗

arXiv2008

Recent progress on NNPDF for LHC

We present recent results of the NNPDF collaboration on a full DIS analysis of Parton Distribution Functions (PDFs). Our method is based on the idea of combining a Monte Carlo sampling of the probability measure in the space of PDFs with the use of neural networks as unbiased universal interpolating functions. The general structure of the project and the features of the fit are described and compared to those of the traditional approaches.

hep-ph↗

arXiv2007

Progress on neural parton distributions

We give a status report on the determination of a set of parton distributions based on neural networks. In particular, we summarize the determination of the nonsinglet quark distribution up to NNLO, we compare it with results obtained using other approaches, and we discuss its use for a determination of $α_s$.

hep-ph↗