Extraction of the Collins-Soper Kernel from a Joint Analysis of Experimental and Lattice Data

Physical Review Letters 2026 Open access

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Authors: Artur Avkhadiev, Valerio Bertone, Chiara Bissolotti, M. Cerutti, Yang Fu, Simone Rodini, Phiala E. Shanahan, Michael L. Wagman, Yong Zhao
DOI: 10.1103/zphz-4k8q
Cited by: 5
References: 53

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 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.

Fields of Study & Subjects

Point processes and geometric inequalities Bayesian Methods and Mixture Models Morphological variations and asymmetry

Publication details

Journal / source
Physical Review Letters
Publisher
American Physical Society (APS)
Volume
136
Issue
17
Pages
171902–171902
Publication date
11 March 2026
Language
EN