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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2603.22471 |
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| _version_ | 1866911538943623168 |
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| author | Tan, Jin-Xin Miao, Ting-Jia Zhang, Mu-Hua Pang, Xiang-He Liu, Ze-Xi Zhang, Lin-Feng Chen, Si-Heng Wang, Wei |
| author_facet | Tan, Jin-Xin Miao, Ting-Jia Zhang, Mu-Hua Pang, Xiang-He Liu, Ze-Xi Zhang, Lin-Feng Chen, Si-Heng Wang, Wei |
| contents | 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 large-$b_\perp$ region to $1~\rm fm$, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22471 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automated Extraction of Collins-Soper Kernel from Lattice QCD using An Autonomous AI Physicist System Tan, Jin-Xin Miao, Ting-Jia Zhang, Mu-Hua Pang, Xiang-He Liu, Ze-Xi Zhang, Lin-Feng Chen, Si-Heng Wang, Wei High Energy Physics - Lattice High Energy Physics - Phenomenology 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 large-$b_\perp$ region to $1~\rm fm$, 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. |
| title | Automated Extraction of Collins-Soper Kernel from Lattice QCD using An Autonomous AI Physicist System |
| topic | High Energy Physics - Lattice High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2603.22471 |