\textsc{RooAgent}: An LLM Agent for \textsc{Root}-Based High Energy Physics Analysis

Abstract: We present \textsc{RooAgent} as a natural-language interface for \textsc{Root}-based high energy physics data analysis. The package provides physics analysis functions as tools that an LLM agent invokes in response to plain-language prompts. Two operating modes are supported: a \textsc{LangGraph}-based agent compatible with \textsc{OpenAI}'s \texttt{GPT-4.1} via GitHub Copilot and with \texttt{DeepSeek-V3} via \textsc{Ollama}, and a Model Context Protocol server for use with the Anthropic \textsc{Claude} CLI (\texttt{Sonnet~4.6}). In both modes the analysis logic is implemented in \textsc{PyRoot} and the LLM selects tools and supplies the required arguments. The package supports histogram inspection, event selection, visualisation of kinematic distributions, fitting, and significance estimation, among other tasks. We illustrate \textsc{RooAgent} with tests based on Monte Carlo simulations of $pp\to ZH$ ($Z\to\ell^+\ell^-$, $H\to b\bar{b}$), a multi-task signal-background workflow, a toy statistical analysis, and an application to ATLAS open data for $H\to ZZ^*\to 4\ell$. The package is available on \textsc{PyPI} and the source code is hosted at \url{https://github.com/amanmdesai/RooAgent}.