Gluon helicity distribution in the nucleon from lattice QCD and machine learning

TL;DR

This paper combines lattice QCD and machine learning to determine the gluon helicity distribution in the nucleon, addressing contamination issues and improving extraction methods for understanding nucleon spin structure.

Contribution

It introduces a novel approach integrating AI with lattice QCD to accurately extract gluon helicity PDFs, overcoming previous contamination challenges.

Findings

Evidence disfavoring negative gluon polarization

A new method to eliminate contamination in lattice correlations

Improved extraction of gluon helicity PDFs from lattice data

Abstract

We present the first lattice QCD determination of the light cone gluon helicity correlation parton distribution function (PDF) with numerical evidence toward disfavoring negative gluon polarization in the nucleon. We present a solution for eliminating an inevitable contamination term that dominates the Euclidean correlations and makes determining gluon helicity PDF unfeasible. The proposed synergy between lattice QCD and artificial intelligence offers a superior platform to alleviate the defining challenge of extracting quark and gluon PDFs from the lattice data that are available in a limited domain due to a finite range of accessible hadron momenta. We suggest a systematically improvable method to extract PDFs from the lattice data, independent of inadequate parametrizations. The result of the gluon helicity will improve our understanding of the role of spin in the strong interaction and the nucleon spin structure.