A determination of parton distributions with faithful uncertainty estimation

TL;DR

This paper introduces a new method for determining nucleon parton distributions at next-to-leading order using neural networks and Monte Carlo techniques, providing a statistically faithful uncertainty estimation validated through various tests.

Contribution

The paper presents a novel neural network-based Monte Carlo approach for parton distribution determination, ensuring unbiased and reliable uncertainty quantification.

Findings

The method produces stable parton distributions consistent with existing sets.

It accurately predicts W and Z cross sections at the LHC.

The approach offers a statistically sound uncertainty estimate.

Abstract

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.