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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.01299 |
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| _version_ | 1866909900213321728 |
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| author | Kostunin, Dmitriy Jones, Elisa Sotnikov, Vladimir Sotnikov, Valery Golovachev, Sergo Strube, Alexandre |
| author_facet | Kostunin, Dmitriy Jones, Elisa Sotnikov, Vladimir Sotnikov, Valery Golovachev, Sergo Strube, Alexandre |
| contents | We develop AI agents based on instruction-finetuned large language models (LLMs) to assist in the engineering and operation of the Cherenkov Telescope Array Observatory (CTAO) Control and Data Acquisition Software (ACADA). These agents align with project-specific documentation and codebases, understand contextual information, interact with external APIs, and communicate with users in natural language. We present our progress in integrating these features into CTAO pipelines for operations and offline data analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01299 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing the development of Cherenkov Telescope Array control software with Large Language Models Kostunin, Dmitriy Jones, Elisa Sotnikov, Vladimir Sotnikov, Valery Golovachev, Sergo Strube, Alexandre Instrumentation and Methods for Astrophysics Artificial Intelligence We develop AI agents based on instruction-finetuned large language models (LLMs) to assist in the engineering and operation of the Cherenkov Telescope Array Observatory (CTAO) Control and Data Acquisition Software (ACADA). These agents align with project-specific documentation and codebases, understand contextual information, interact with external APIs, and communicate with users in natural language. We present our progress in integrating these features into CTAO pipelines for operations and offline data analysis. |
| title | Enhancing the development of Cherenkov Telescope Array control software with Large Language Models |
| topic | Instrumentation and Methods for Astrophysics Artificial Intelligence |
| url | https://arxiv.org/abs/2510.01299 |