Agentic AI for Gravitational Wave Data Analysis: A Head-to-Head Comparison of Coding Agents Executing a Matched Filter Pipeline on Einstein Telescope Simulated Data

תקציר מקורי באנגליתarXiv:2605.28916v3 Announce Type: replace-cross Abstract: We report a methodological study of agentic AI in gravitational-wave data analysis: two systems, Claude Code (Anthropic) and Codex (OpenAI), autonomously executed the same simple end-to-end pipeline on Einstein Telescope (ET) simulated data, on shared infrastructure and without human intervention. The object of study is the behaviour, reliability and auditability of the agents, not the physics output, used here as a controlled test case. The pipeline comprises power spectral density estimation from simulated ET noise, geometric template bank generation with IMRPhenomD waveforms, matched-filter recovery of 100 binary black hole injections, results generation, and LLM-assisted production of a LaTeX manuscript in Physical Review D styl