Speaker
Description
In this talk, I will provide an overview of recent theoretical developments in understanding the three-dimensional momentum-space structure of hadrons.
Transverse Momentum Distributions (TMDs) are essential for describing processes like semi-inclusive deep inelastic scattering (SIDIS), Drell-Yan production, and hadron-hadron collisions at low transverse momentum, where transverse dynamics play a crucial role.
I will present the latest global fits for unpolarized TMDs and discuss the different phenomenological frameworks used to extract these distributions from experimental data, highlighting both the challenges and successes of current approaches. Additionally, I will explore the incorporation of neural networks in TMD analyses, discussing how the flexibility of machine learning techniques can enhance our ability to model the non-perturbative part of TMDs.
Building on these developments, significant advancements have also been made in recent years in extracting polarized TMDs, like the Sivers function and the helicity TMD, from experimental data. These advancements deepen our understanding of spin-dependent phenomena and the internal spin structure of hadrons, bringing us closer to a comprehensive picture of hadron dynamics in momentum space.
