Helicity-dependent parton distribution functions at next-to-next-to-leading order accuracy from inclusive and semi-inclusive deep-inelastic scattering data

TL;DR

This paper introduces MAPPDFpol1.0, a new set of helicity-dependent proton PDFs determined using NNLO QCD corrections, neural networks, and Monte Carlo methods, based on inclusive and semi-inclusive deep-inelastic scattering data.

Contribution

It provides the first NNLO QCD analysis of polarized PDFs using neural networks and Monte Carlo techniques, improving the accuracy and reliability of the distributions.

Findings

Enhanced precision of helicity-dependent PDFs at NNLO

Assessment of higher-order correction impacts

Robust uncertainty quantification using Monte Carlo methods

Abstract

We present MAPPDFpol1.0, a new determination of the helicity-dependent parton distribution functions (PDFs) of the proton from a set of longitudinally polarised inclusive and semi-inclusive deep-inelastic scattering data. The determination includes, for the first time, next-to-next-to-leading order QCD corrections to both processes, and is carried out in a framework that combines a neural-network parametrisation of PDFs with a Monte Carlo representation of their uncertainties. We discuss the quality of the determination, in particular its dependence on higher-order corrections, on the choice of data set, and on theoretical constraints.