An extraction of the Collins-Soper kernel from a joint analysis of experimental and lattice data

TL;DR

This paper combines experimental and lattice QCD data using neural networks to extract the Collins-Soper kernel, significantly improving the precision of the measurement and demonstrating the value of lattice inputs in TMD studies.

Contribution

It introduces a joint analysis method that integrates experimental and lattice data for the first time to extract the Collins-Soper kernel.

Findings

Inclusion of lattice data shifts the CSK value by ~10%.

Uncertainty in CSK is reduced by 40-50%.

Neural-network based Bayesian reweighting effectively combines diverse data sources.

Abstract

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.