Speaker
Zhite Yu
(Jefferson Lab)
Description
Obtaining the $x$-dependent generalized parton distributions (GPDs) is essential for advancing our understanding of hadron tomography. However, this goal has been hindered by the limited sensitivity of most well-known experimental processes, such as deeply virtual Compton scattering (DVCS) and time-like Compton scattering (TCS). In this talk, I will compare these traditional processes with new ones that offer enhanced sensitivity to the $x$-dependence. By employing a pixelated GPD construction using a normalizing flow neural network, we can visualize and quantitatively examine the point-by-point sensitivity encoded in the physical processes.
Author
Zhite Yu
(Jefferson Lab)
