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 fits 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.
@article{arxiv.2510.26489,
title = {An extraction of the Collins-Soper kernel from a joint analysis of experimental and lattice data},
author = {Artur Avkhadiev and Valerio Bertone and Chiara Bissolotti and Matteo Cerutti and Yang Fu and Simone Rodini and Phiala Shanahan and Michael Wagman and Yong Zhao},
journal= {arXiv preprint arXiv:2510.26489},
year = {2025}
}