Agentic AI -- Physicist Collaboration in Experimental Particle Physics: A Proof-of-Concept Measurement with LEP Open Data

arXiv:2603.05735v3 Announce Type: replace-cross Abstract: We present an agentic measurement of the thrust distribution in $e^{+}e^{-}$ collisions at $\sqrt{s}=91.2$ GeV using ALEPH archived data. This work was carried out through an AI agent-physicist collaboration, using interactive natural-language prompting of Anthropic Claude and OpenAI Codex. Under physicist guidance, the agents wrote and executed all analysis code and drafted the majority of the text included in this paper. A fully-corrected thrust spectrum is obtained via Iterative Bayesian Unfolding and Monte Carlo based corrections. The principal contribution of this work is the demonstration of the agentic analysis, with thrust serving as a benchmark observable for the method. Dedicated precision measurements of thrust are reported in the community elsewhere. This work represents a step toward a theory-experiment loop in which AI agents assist with experimental measurements and theoretical calculations, and synthesize insights by comparing the results, thereby accelerating the cycle that drives discovery in fundamental physics. Our work suggests that precision physics, leveraging the open LEP data and advanced theoretical landscape, provides an ideal testing ground for developing AI agent systems for scientific applications.

Abstract:We present an agentic measurement of the thrust distribution in $e^{+}e^{-}$ collisions at $\sqrt{s}=91.2$ GeV using ALEPH archived data. This work was carried out through an AI agent-physicist collaboration, using interactive natural-language prompting of Anthropic Claude and OpenAI Codex. Under physicist guidance, the agents wrote and executed all analysis code and drafted the majority of the text included in this paper. A fully-corrected thrust spectrum is obtained via Iterative Bayesian Unfolding and Monte Carlo based corrections. The principal contribution of this work is the demonstration of the agentic analysis, with thrust serving as a benchmark observable for the method. Dedicated precision measurements of thrust are reported in the community elsewhere. This work represents a step toward a theory-experiment loop in which AI agents assist with experimental measurements and theoretical calculations, and synthesize insights by comparing the results, thereby accelerating the cycle that drives discovery in fundamental physics. Our work suggests that precision physics, leveraging the open LEP data and advanced theoretical landscape, provides an ideal testing ground for developing AI agent systems for scientific applications.

36 pages, 17 figures, 4 tables. Published in HiHEP 2 (2026) 3, 10; this version includes the journal proofreading corrections. Code: this https URL

High Energy Physics - Experiment (hep-ex); Artificial Intelligence (cs.AI); High Energy Physics - Phenomenology (hep-ph)

arXiv:2603.05735 [hep-ex]

(or arXiv:2603.05735v3 [hep-ex] for this version)

https://doi.org/10.48550/arXiv.2603.05735 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.53941/hihep.2026.100010 Focus to learn more DOI(s) linking to related resources

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Agentic AI -- Physicist Collaboration in Experimental Particle Physics: A Proof-of-Concept Measurement with LEP Open Data

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