Artur Avkhadiev, Valerio Bertone, Chiara Bissolotti, Matteo Cerutti, Yang Fu, Simone Rodini, Phiala Shanahan, Michael Wagman, Yong Zhao
We present a first joint extraction of the Collins-Soper kernel (CSK) combining experimental and lattice QCD data in the context of an analysis of transverse-momentum-dependent distributions (TMDs). Based on a neural-network parametrization, we perform a Bayesian reweighting of an existing fit of TMDs using lattice data, as well as a joint TMD fit to lattice and experimental data. We consistently find that the inclusion of lattice information shifts the central value of the CSK by approximately 10% and reduces its uncertainty by 40%-50%, highlighting the potential of lattice inputs to improve TMD extractions.