PROBING PARTICLE PHYSICS WITH ARTIFICIAL INTELLIGENCE
ZHAO Yiyang;HU Zhen;
Abstract:
Particle physics experiments have long research cycles and high barriers to entry.Limited human resources make it difficult to fully explore and analyze large volumes of collision data.Agents based on large language models can write code,retrieve literature,and carry out multi-step analyses,offering an opportunity to address this challenge.This article focuses on the Just Furnish Context(JFC)agent framework and examines results produced by the framework,including the CMS H→τ~+τ~-signal-strength measurement and the ALEPH Lund jet-plane density measurement,as well as their implications for particle physics research.It also summarizes the framework's current limitations and discusses prospects for benchmarkdataset construction,data processing at next-generation large-scale scientific facilities,and the training of future physicists.
Key Words: particle physics;high energy physics experiment;artificial intelligence;large language models;AI agents
Foundation: 国家自然科学基金项目W2511007、125B1008;; 清华大学自主科研计划的支持
Authors: ZHAO Yiyang;HU Zhen;
DOI: 10.27024/j.wlygc.2026.08.13.is
References:
- [1] GUEST D,CRANMER K,WHITESON D.Deep learning and its application to LHC physics[J].Annual Review of Nuclear and Particle Science,2018,68:161-181.
- [2] GENDREAU-DISTLER E,HO J,KIM D,et al.Automating high energy physics data analysis with LLM-powered agents[Z].arXiv:2512.07785,2025.
- [3] BADEA A,CHEN Y,MAGGI M,et al.Agentic AI-physicist collaboration in experimental particle physics:a proof-of-concept measurement with LEP open data[Z].arXiv:2603.05735,2026.
- [4] LI K,LIU B J,MELLADO B,et al.AI agents,language,deep learning,and the next revolution in science[J].Frontiers of Physics,2026,21(9):096401.
- [5] MORENO E A,BRIGHT-THONNEY S,NOVAK A,et al.AI agents can already autonomously perform experimental high energy physics[Z].arXiv:2603.20179,2026.
- [6] MCGREIVY J,DELANEY B,BECK A,et al.Seeing the forest through the trees:knowledge retrieval for streamlining particle physics analysis[Z].arXiv:2509.06855,2025.
- [7] The CMS Collaboration.Evidence for the 125GeV Higgs boson decaying to a pair of τ leptons[J].Journal of High Energy Physics,2014,2014(5):104.
- [8] ATLAS Collaboration.Measurement of the Lund jet plane using charged particles in 13TeV proton-proton collisions with the ATLAS detector[J].Physical Review Letters,2020,124:222002.
- [9] The ALEPH Collaboration.Measurement of the Z resonance parameters at LEP[J].The European Physical Journal C-Particles and Fields,2000,14(1):1-50.
- [10] HENDRYCKS D,BURNS C,KADAVATH S,et al.Measuring mathematical problem solving with the MATH dataset[C]//Proceedings of the 35th Conference on Neural Information Processing Systems Track on Datasets and Benchmarks.2021.
- [11] JIMENEZ C E,YANG J,WETTIG A,et al.The Twelfth International Conference on Learning Representations,2024.
- [12] FAROUGHY D A,SCHWEITZER S P,PANG I,et al.Collider-Bench:benchmarking AI agents with particle physics analysis reproduction[Z].arXiv:2605.13950,2026.
- [13] DIERLAMM A,on behalf of the CMS Tracker Collaboration.The CMS outer tracker upgrade for the HL-LHC[J].Nuclear Instruments and Methods in Physics Research Section A:Accelerators,Spectrometers,Detectors and Associated Equipment,2019,924:256-261.
- [14] BOCCALI T.Computing models in high energy physics[J].Reviews in Physics,2019,4:100034.
- [15] FCC Collaboration.FCC-ee:The lepton collider[J].The European Physical Journal Special Topics,2019,228(2):261-623.
- [16] The CEPC Study Group.CEPC technical design report:Accelerator[J].Radiation Detection Technology and Methods,2024,8(1):1-1105.
- [17] AI X C,AN L P,AN S Z,et al.Conceptual design report of the Super Tau-Charm Facility:The accelerator[J].Nuclear Science and Techniques,2025,36(12):242.