StarWhisper Telescope: An AI framework for automating end-to-end astronomical observations

Cunshi Wang, Yu Zhang, Yuyang Li, Xinjie Hu, Yiming Mao +23 more
12/9/2024
astro-ph.IMcs.AIcs.CL

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Abstract

The exponential growth of large-scale telescope arrays has boosted time-domain astronomy development but introduced operational bottlenecks, including labor-intensive observation planning, data processing, and real-time decision-making. Here we present the StarWhisper Telescope system, an AI agent framework automating end-to-end astronomical observations for surveys like the Nearby Galaxy Supernovae Survey. By integrating large language models with specialized function calls and modular workflows, StarWhisper Telescope autonomously generates site-specific observation lists, executes real-time image analysis via pipelines, and dynamically triggers follow-up proposals upon transient detection. The system reduces human intervention through automated observation planning, telescope controlling and data processing, while enabling seamless collaboration between amateur and professional astronomers. Deployed across Nearby Galaxy Supernovae Survey's network of 10 amateur telescopes, the StarWhisper Telescope has detected transients with promising response times relative to existing surveys. Furthermore, StarWhisper Telescope's scalable agent architecture provides a blueprint for future facilities like the Global Open Transient Telescope Array, where AI-driven autonomy will be critical for managing 60 telescopes.

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@article{wang2024starwhisper,
  title  = {StarWhisper Telescope: An AI framework for automating end-to-end astronomical observations},
  author = {Cunshi Wang and Yu Zhang and Yuyang Li and Xinjie Hu and Yiming Mao and Xunhao Chen and Pengliang Du and Rui Wang and Ying Wu and Hang Yang and Yansong Li and Beichuan Wang and Haiyang Mu and Zheng Wang and Jianfeng Tian and Liang Ge and Yongna Mao and Shengming Li and Xiaomeng Lu and Jinhang Zou and Yang Huang and Ningchen Sun and Jie Zheng and Min He and Yu Bai and Junjie Jin and Hong Wu and Jifeng Liu},
  year   = {2024},
  eprint = {2412.06412},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.06412v3}
}

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