Speakers
Description
We present an analysis of Machine Learning techniques applied to the determination of ratios of nucleon 3-point and 2-point correlation functions in Euclidean time. These quantities are typically computed in the regime of Lattice QCD and are of interest in determining various measures of hadronic structure, such as parton distribution functions (PDFs) and generalised parton distributions (GPDs). Where direct lattice calculations are computationally expensive and time consuming, machine learning offers the opportunity for significant gain in computing efficiency, ideally without the loss of key physics, and while maintaining accuracy. In our analysis, we train various regression models and explore the performance on a sample of lattice QCD data. The correlation functions were computed at various values of $P_z$, the momentum of the nucleon, and $z$ the gauge link length, and several light quark masses $u$ and $d$. We study the correlations between the choices of $P_z$ and $z$, and use this to make predictions of high $P_z$ correlators for use in the LaMET framework. In particular, we analyse the evolution of uncertainties across multiple data partitions and parameter choices, quantifying the systematic uncertainties.
