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📄 Abstract
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.
Authors (28)
Cunshi Wang
Yu Zhang
Yuyang Li
Xinjie Hu
Yiming Mao
Xunhao Chen
+22 more
Submitted
December 9, 2024
arXiv Category
astro-ph.IM
Key Contributions
Presents the StarWhisper Telescope system, an AI agent framework that automates end-to-end astronomical observations by integrating LLMs with function calls and modular workflows. It autonomously generates observation lists, performs real-time analysis, and triggers follow-up proposals, significantly reducing human intervention.
Business Value
Accelerates astronomical discovery by automating complex and time-consuming observation processes, allowing researchers to focus on scientific insights rather than operational tasks. Improves efficiency and potentially enables new types of time-domain surveys.