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AI Agents for Ground-Based Gamma Astronomy

D. Kostunin, V. Sotnikov, S. Golovachev, A. Strube

TL;DR

Next-generation ground-based gamma-ray astronomy with CTAO presents operational and data-analysis challenges due to its scale. The authors propose AI agents based on instruction-finetuned LLMs that can interact with external tools and codebases, incorporating a validation workflow to ensure safe actions. They demonstrate two prototypes: an LLM-powered telescope-control agent integrated with the ACADA Configuration Database and an AstroAgent for Gammapy-based data analysis using Retrieval-Augmented Generation. This work suggests a pathway to automate complex workflows in astronomy, while acknowledging the need for human oversight and a move toward open-source LLMs for reproducibility.

Abstract

Next-generation instruments for ground-based gamma-ray astronomy are marked by a substantial increase in complexity, featuring dozens of telescopes. This leap in scale introduces significant challenges in managing system operations and offline data analysis. Methods, which depend on advanced personnel training and sophisticated software, become increasingly strained as system complexity grows, making it more challenging to effectively support users in such a multifaceted environment. To address these challenges, we propose the development of AI agents based on instruction-finetuned large language models (LLMs). These agents align with specific documentation and codebases, understand the environmental context, operate with external APIs, and communicate with humans in natural language. Leveraging the advanced capabilities of modern LLMs, which can process and retain vast amounts of information, these AI agents offer a transformative approach to system management and data analysis by automating complex tasks and providing intelligent assistance. We present two prototypes that integrate with the Cherenkov Telescope Array Observatory pipelines for operations and offline data analysis. The first prototype automates data model implementation and maintenance for the Configuration Database of the Array Control and Data Acquisition (ACADA). The second prototype is an open-access code generation application tailored for data analysis based on the Gammapy framework.

AI Agents for Ground-Based Gamma Astronomy

TL;DR

Next-generation ground-based gamma-ray astronomy with CTAO presents operational and data-analysis challenges due to its scale. The authors propose AI agents based on instruction-finetuned LLMs that can interact with external tools and codebases, incorporating a validation workflow to ensure safe actions. They demonstrate two prototypes: an LLM-powered telescope-control agent integrated with the ACADA Configuration Database and an AstroAgent for Gammapy-based data analysis using Retrieval-Augmented Generation. This work suggests a pathway to automate complex workflows in astronomy, while acknowledging the need for human oversight and a move toward open-source LLMs for reproducibility.

Abstract

Next-generation instruments for ground-based gamma-ray astronomy are marked by a substantial increase in complexity, featuring dozens of telescopes. This leap in scale introduces significant challenges in managing system operations and offline data analysis. Methods, which depend on advanced personnel training and sophisticated software, become increasingly strained as system complexity grows, making it more challenging to effectively support users in such a multifaceted environment. To address these challenges, we propose the development of AI agents based on instruction-finetuned large language models (LLMs). These agents align with specific documentation and codebases, understand the environmental context, operate with external APIs, and communicate with humans in natural language. Leveraging the advanced capabilities of modern LLMs, which can process and retain vast amounts of information, these AI agents offer a transformative approach to system management and data analysis by automating complex tasks and providing intelligent assistance. We present two prototypes that integrate with the Cherenkov Telescope Array Observatory pipelines for operations and offline data analysis. The first prototype automates data model implementation and maintenance for the Configuration Database of the Array Control and Data Acquisition (ACADA). The second prototype is an open-access code generation application tailored for data analysis based on the Gammapy framework.

Paper Structure

This paper contains 5 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Designing agents for astronomy
  3. Agent for telescope control
  4. Agent for data analysis
  5. Conclusion

Figures (1)

  • Figure 1: Left: schematic representation of the astronomical agent with an abstract depiction of the validation step. Right: data flow of CTAO (drawing from the official website). We marked with red the parts of the flow addressed in this work.