🤖 AI Summary
This work investigates the feasibility of employing artificial intelligence agents to carry out complex measurement tasks in high-energy physics experiments, with a focus on end-to-end analysis of the thrust distribution in e⁺e⁻ collisions. The study presents the first complete AI-driven measurement pipeline: leveraging open data from LEP, an AI agent—guided by expert supervision and powered by OpenAI Codex and Anthropic Claude—automatically executes data processing, performs shape corrections via iterative Bayesian unfolding combined with Monte Carlo-based corrections, and generates a full analysis report. The resulting fully corrected thrust distribution demonstrates the effectiveness of AI agents in precision physics measurements and establishes a novel paradigm for closed-loop collaboration between theoretical modeling and experimental analysis.
📝 Abstract
We present an AI agentic measurement of the thrust distribution in $e^{+}e^{-}$ collisions at $\sqrt{s}=91.2$~GeV using archived ALEPH data. The analysis and all note writing is carried out entirely by AI agents (OpenAI Codex and Anthropic Claude) under expert physicist direction. A fully corrected spectrum is obtained via Iterative Bayesian Unfolding and Monte Carlo based corrections. 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 advanced AI systems for scientific applications.