Speaker
Description
The next generation of ground-based gamma-ray astronomy instruments will involve arrays of dozens of telescopes, leading to an increase in operational and analytical complexity. This scale-up poses challenges for both system operations and offline data processing, especially when conventional approaches struggle to scale effectively. To address these challenges, we are developing AI agents built on instruction-finetuned large language models (LLMs). These agents leverage domain-specific documentation and codebases, understand contextual operational requirements, interact with external APIs, and engage with users in natural language. Our prototypes focus on integration with the Cherenkov Telescope Array Observatory pipelines, both for operational workflows and for offline data analysis. In this presentation, we outline our approach, discuss encountered challenges, and highlight future plans.
References
https://arxiv.org/abs/2503.00821
| Experiment context, if any | CTAO, H.E.S.S. |
|---|