Generalized parton distributions (GPDs) are accessible through experimental processes such as deep virtual Compton scattering (DVCS) and deep virtual meson production (DVMP). Extracting GPDs directly from Compton form factors is complicated by the inherent ambiguity of deconvolution when parametrizing GPDs directly in momentum fraction $x$-space using double distributions. To overcome this...
We extend the formalism of Phys.Rev.Lett. 133 (2024) 24, 241901 to helicity generalized parton distributions (GPDs) with the skewness dependence modeled by t-channel exchanges of spin-j operators in AdS space. Based on the conformal moment expansion, the GPDs are obtained through Mellin-Barnes integrals which bypass the convolution problem and are valid for all values of the skewness...
Zero mode issue in the minus-minus component calculation of the transition form factors in the light-front dynamics Among the three forms of Hamiltonian dynamics Dirac proposed in 1949, the light-front dynamics (LFD) has the most kinematical Poincare operators. In particular, the longitudinal boost operator becomes kinematical in the LFD. The LFD has very distinct vacuum properties, leading to...
In this talk, I will discuss recent advances in our ability to image parton distribution functions (PDFs) in bound nucleons. I will review topics from last year’s QCD Evolution workshop, such as methods for extracting nuclear-modified PDFs and TMDs. Additionally, I will discuss perturbative approaches that show how the evolution of TMD PDFs in Drell-Yan (p+A) collisions follows a BFKL...
I will present on the extraction of disconnected contribution to the isoscalar matrix elements for light and strange quarks for the proton using Lattice QCD. While the connected contributions dominate the disconnected contributions are non-zero and must be considered to properly determine the matrix elements. In the case of the strange quark, there is no connected contribution, thus it is...
Inverse problems are ubiquitous in hadron structure and tomography, where accurately characterizing uncertainties is crucial for unraveling new physics hiding within these uncertainties. In this new precision era of QCD, it is vital to create a translation between our physics and next generation AI/ML algorithms, using tools such as evidential deep learning and information-theoretic metrics to...
