The NNPDF2.2 Parton Set

TL;DR

The paper introduces a Bayesian reweighting method for updating parton distribution functions with new experimental data without full refitting, demonstrated by creating the NNPDF2.2 set.

Contribution

A novel Bayesian reweighting technique for efficiently updating PDFs with new data, avoiding complete refits of existing PDF ensembles.

Findings

Successfully incorporated W-lepton asymmetry data into NNPDF2.1.

Produced the updated NNPDF2.2 PDF set.

Demonstrated the method's effectiveness in PDF updates.

Abstract

We present a method developed by the NNPDF Collaboration that allows the inclusion of new experimental data into an existing set of parton distribution functions without the need for a complete refit. A Monte Carlo ensemble of PDFs may be updated by assigning each member of the ensemble a unique weight determined by Bayesian inference. The reweighted ensemble therefore represents the probability density of PDFs conditional on both the old and new data. This method is applied to the inclusion of W-lepton asymmetry data into the NNPDF2.1 fit producing a new PDF set, NNPDF2.2.