Automated Extraction of Collins-Soper Kernel from Lattice QCD using An Autonomous AI Physicist System
Abstract
We employ PhysMaster, an autonomous agentic AI system integrating theoretical reasoning, numerical computation, and exploitation strategies towards ultra-long horizon automation, to tackle long-standing challenges in non-perturbative lattice analyzes, including low signal-to-noise ratio at large transverse separation, complex systematic uncertainties, and labor-intensive manual workflows. Using the extraction of the CS kernel from quasi-transverse-momentum-dependent wave functions (quasi-TMDWFs) via large-momentum effective theory (LaMET) as a showcase, we demonstrate that PhysMaster automates high-dimensional fitting, renormalization, continuum-chiral extrapolation, and non-perturbative reconstruction in a fully autonomous manner. This framework drastically reduces the duration of the workflow from months to hours without compromising precision, stabilizes signals in the large-b region to 1~ fm, and produces results consistent with perturbative QCD and state-of-the-art traditional lattice calculations. This work validates the effectiveness of physicist-AI collaboration for first-principles QCD research and establishes a generalizable, reproducible paradigm for automated studies of parton structure and other non-perturbative observables from lattice QCD.
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