Research Projects 2021-2022
Inverse Mappers of QCD Global Analysis
Manal Almaeen (ODU)
We present a new Machine Learning technique based on Variational Autoen- coders in the context of deep learning to construct an effective “inverse function” that maps experimental data into quantum correlation functions (QCFs) such as parton distribution functions in the nucleon. As such it provides a powerful comple- mentary tool for QCD global analysis where the Bayesian inference associate with the inverse problem of QCFs can be implemented efficiently allowing the possibil- ity to explore systematically different choices for the likelihood functions, Bayesian priors and have the possibility to understand in great detail how each data point or sets of data points influence the uncertainty quantification for the QCFs. (Manal Almaeen is supported by a graduate fellowship from Center for Nuclear Femtogra- phy, SURA, Washington DC)
Deep Learning In Deep Inelastic Scattering Kinematics
Abdullah Farhat (ODU)
Higher Order Kinematical Effects in DVCS
Yuxun Guo (UMD)
We apply the frame-independent deeply virtual Compton scattering cross-section formula to the EIC kinematics and study the high order kinematical effects, including both kinematical higher-twist effects, corrections from higher-twist CFFs and quantum loop corrections to the next-to-leading order of strong coupling. We show how those effects compared with the leading order contribution, which plays an important role in extracting the leading twist Compton form factors with precision. This research is supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, under contract number DE-SC0020682, and the Center for Nuclear Femtography, Southeastern Universities Research Association, Washington D.C. Y. Guo is partially supported by a graduate fellowship from Center for Nuclear Femtography, SURA, Washington DC.
Smart Lookup Tables for Compton Form Factors
Mitchell Kerver (ODU)
We utilize the Old Dominion University's Center for Real-Time Computing image-to-mesh software to create a database of unstructured tessellations of the Compton Form Factors (CFFs). Unstructured tessellations provide a discretization of the phase-space using irregular simplices. Tessellations are able to capture the non-uniform characteristics of the functions by concentrating more points in areas that require higher granularity, while still creating a fewer number of vertices overall. An iterative adaptivity algorithm was designed to efficiently refine the tessellations to ensure interpolation error is minimized. These lookup tables and software utilities provide a flexibility for any model to generate a tessellation of its data set; greatly reducing computation resources and increasing data portability while maintaining an rms error of 1% between interpolation and model.