JW-ASTClaw: A Generalizable Multi-agent Framework for Autonomous Solar Telescope and Its Implementation within Chinese Meridian Project
TL;DR
This work represents the first concrete engineering step toward the embodied intelligent solar telescope concept, providing a validated foundation for the transition from automated scheduling to AI-driven autonomous observation.
Abstract
We present the first deployment of an end-to-end autonomous control system driven by a large language model (LLM) on an operational solar telescope—the Solar Full-disk Multi-layer Magnetograph, named JW-ASTClaw. This system employs a multi-agent framework adopting a decoupled three-layer architecture (perception–decision–execution) interconnected through the Model Context Protocol, which addresses real-time adaptive scheduling under complex environmental conditions while achieving high portability: the perception and decision logic are reused unchanged across instruments, requiring only telescope-specific command interfaces to be adapted. Three perception agents—data-quality-agent, cloud-analyzer-agent, and flare-detector-agent—encode senior observer expertise, including wind jitter detection via limb-ring standard deviation, projected-circle zonal cloud analysis, and multi-band active region identification, as LLM-callable rules, while a central reasoning engine performs multi-source fusion and conflict resolution. The system supports graceful degradation from cloud LLM to local inference and finally to rule-based fallback, designed for remote field stations with unstable connectivity. Cross-season validation on archival data demonstrates 100% cloud detection with zero false positives across 10 distinct observation dates, with active-region counts and positions closely matching the NOAA Solar Region Summary (SRS) reports (102 versus 100 across 10 separate validation dates). These capabilities significantly improve scientific-intent-driven observation accessibility, enable rapid flare response for space weather monitoring, enhance data usability under adverse conditions, and increase observability during partially cloudy periods. This work represents the first concrete engineering step toward the embodied intelligent solar telescope concept, providing a validated foundation for the transition from automated scheduling to AI-driven autonomous observation.