Speaker
Description
The phenomenology of unpolarised quark transverse-momentum-dependent distributions (TMDs) has reached an unprecedented level of sophistication. Modern global analyses of Drell–Yan and semi-inclusive deep-inelastic scattering data achieve next-to-next-to-next-to-leading logarithmic accuracy and incorporate more than one thousand experimental data points. Despite these advances, significant discrepancies remain among TMD extractions obtained by different groups. A possible source of these tensions is the choice of the functional form used to model the nonperturbative component of TMDs, leading to a potential parametrisation bias.
To mitigate this issue, neural-network-based approaches have recently been introduced, providing a more flexible and data-driven framework for TMD extractions. In this talk, I will review the current status of TMD phenomenology based on neural networks and present the first results of the MAP Collaboration on the simultaneous extraction of TMD parton distribution functions and fragmentation functions using neural-network parametrisations.