Speaker
Adam Freese
(Jefferson Lab)
Description
Generalized parton distributions (GPDs) are functions of four variables, one of which is a renormalization scale. The functional dependence on this renormalization scale is fully determined by a renormalization group equation---or "evolution equation"---that can be derived from perturbative QCD. A fast numerical implementation of the scale evolution is vital to any global phenomenology effort. Moreover, for a framework leveraging neural networks, differentiability is also necessary. In this talk, I will discuss an ultra-fast, differentiable implementation of GPD evolution in momentum fraction space, in which the evolution equation itself is (approximately) rendered as a differential matrix equation.
Primary author
Adam Freese
(Jefferson Lab)
Co-authors
Daniel Adamiak
(Ohio State University)
Ian Cloet
(Argonne National Laboratory)
Jianwei Qiu
(Jefferson Lab)
Marco Zaccheddu
(University and INFN Cagliari)
Nobuo Sato
(Jefferson Lab)
