Automated Extraction of Collins-Soper Kernel from Lattice QCD using An Autonomous AI Physicist System
Jin-Xin Tan, Ting-Jia Miao, Mu-Hua Zhang, Xiang-He Pang, Ze-Xi Liu, Lin-Feng Zhang, Si-Heng Chen, Wei Wang

TL;DR
This paper introduces PhysMaster, an autonomous AI system that automates complex lattice QCD analyses, significantly reducing time and improving stability in extracting the Collins-Soper kernel, demonstrating effective physicist-AI collaboration.
Contribution
The work presents a fully autonomous AI framework for lattice QCD analysis, automating multiple complex steps and achieving results comparable to traditional methods with much greater efficiency.
Findings
Workflow time reduced from months to hours
Stable signals in large transverse separation region
Results consistent with perturbative QCD and traditional lattice calculations
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 \textsc{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…
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Taxonomy
TopicsQuantum Chromodynamics and Particle Interactions · Particle physics theoretical and experimental studies · High-Energy Particle Collisions Research
