一个可直接部署的MLOps软件平台，用于米级地面天文台的人造卫星与近地天体探测
A ready-to-deploy MLOps software platform for satellite and NEO detection at meter-class ground-based observatories
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中文总结 AI 辅助
针对天文台缺乏集成ML工具的问题，本文提出StreakMind Workbench，一个基于PyQt5的MLOps平台，集成参考AI模型，支持从FITS摄取到数据库存储的完整工作流，并在La Sagra天文台273幅图像上验证成功，促进空间态势感知。
中文摘要 AI 辅助
地面天文观测中经常包含由人造卫星、空间碎片以及潜在近地天体（NEOs）产生的条纹。虽然机器学习模型能够可靠地检测这些特征，但由于缺乏用于视觉检查、验证、工作流管理和结构化数据存储的集成工具，这些模型在实际天文台运行中的采用往往受到限制。本文介绍了StreakMind Workbench，一个应用MLOps实践将研究导向的机器学习流水线与常规天文台运行衔接起来的框架。该Workbench并未引入新的检测算法，而是解决天文计算中的一个软件工程挑战：在维护科学处理代码单一权威来源的同时，为天文学家提供用于工作流执行和结果检查的运行环境。该Workbench与Carrillo等人（2026）的参考StreakMind AI模型集成，使用Python和PyQt5实现，支持从FITS数据摄取到数据库存储的完整工作流，包括推理、结果检查、数据库探索、训练管理以及小行星中心格式的观测数据输出。在La Sagra天文台273幅图像上的验证表明，端到端工作流成功运行，同时与底层StreakMind科学代码保持一致性。该平台促进了研究型机器学习流水线在米级天文台和中等规模观测活动中的运行使用，支持空间态势感知和行星防御。
英文摘要
Ground-based astronomical observations frequently contain streaks produced by artificial satellites, space debris, and potentially Near-Earth Objects (NEOs). While machine-learning models can reliably detect these features, their practical adoption in observatory operations is often limited by the lack of integrated tools for visual inspection, validation, workflow management, and structured data storage. This paper presents the StreakMind Workbench, a framework that applies MLOps practices to bridge research-oriented machine-learning pipelines with routine observatory operations. Rather than introducing new detection algorithms, the Workbench addresses a software-engineering challenge in astronomical computing: maintaining a single authoritative source of scientific processing code while providing astronomers with an operational environment for workflow execution and result inspection. Integrated with the reference StreakMind AI model of Carrillo et al. (2026) and implemented in Python using PyQt5, the Workbench supports the complete workflow from FITS ingestion to database storage, including inference, result inspection, database exploration, training management, and Minor Planet Center formatted observations. Validation on 273 images from La Sagra Observatory demonstrates successful end-to-end workflows while maintaining consistency with the underlying StreakMind scientific code. The platform facilitates operational use of a research ML pipeline in meter-class observatories and moderate-scale campaigns, supporting Space Situational Awareness and planetary defence.
发表机构
- Real Instituto y Observatorio de la Armada (ROA)（皇家海军研究院与天文台）
- Universidad de Granada（格拉纳达大学）
- Instituto de Astrofísica de Andalucía (IAA-CSIC)（安达卢西亚天体物理研究所）
- Safran（赛峰集团）
- Universidad de Cádiz（加的斯大学）
机构由 AI 辅助整理，请以论文原文为准。
