Hybrid RBRC Seminar

Abstract: Gluons are mediator bosons of the strong interaction and play a crucial role in the mass and spin structures of the nucleon. Understanding gluon distributions and their roles in the nucleon structure remain some of the most fundamental yet challenging issues in nuclear and particle physics. This talk focuses on the first lattice QCD determination of the gluon helicity parton distribution function, presenting numerical evidence toward disfavoring negative gluon polarization in the nucleon. Additionally, we discuss the reliability of model-based extraction of unpolarized gluon distribution from limited lattice data, which is often overly constrained. To tackle this challenge, we demonstrate the effectiveness of several machine learning algorithms on Euclidean correlation functions. Our results show that the proposed synergy between lattice QCD and machine learning can provide theoretical support for analyzing experimental data and point toward some useful future applications of extracting quark and gluon PDFs from lattice data.