AI自动管理天文观测全流程，提升发现瞬变天体效率
StarWhisper Telescope: An AI framework for automating end-to-end astronomical observations
- 用大模型+模块化流程实现观测计划自动生成
- 10台业余望远镜网络中实测响应速度快于现有巡天
- 适合需大规模自主观测的天文项目参考
大规模望远镜阵列的快速发展推动了时域天文研究，但也带来了人工规划观测、数据处理和实时决策等操作瓶颈。本文提出StarWhisper Telescope系统，一个面向近邻星系超新星巡天等项目的全链路自动化天文观测AI框架。通过整合大语言模型与专用函数调用、模块化工作流，该系统可自主生成本地观测列表，通过流水线实时分析图像，并在发现暂现源时动态触发后续观测提案。系统显著减少人工干预，实现观测规划、望远镜控制与数据处理的自动化，促进业余与专业天文学家协同。部署于近邻星系超新星巡天的10台业余望远镜网络，其暂现源响应时间表现优于现有巡天。此外，该系统的可扩展代理架构为未来全球开放暂现源望远镜阵列（60台）提供范式，其中人工智能自主性将成为关键。
原文摘要 · Abstract (English)
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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