StarWhisper Telescope: An AI framework for automating end-to-end astronomical observations
This addresses labor-intensive observation planning and data processing for astronomers, particularly in time-domain astronomy, though it appears incremental as it builds on existing AI and modular workflow concepts.
The paper tackles the operational bottlenecks in large-scale telescope arrays by introducing the StarWhisper Telescope system, an AI framework that automates end-to-end astronomical observations, resulting in reduced human intervention and promising response times for transient detection in a network of 10 amateur telescopes.
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.