AI 中文总结
研究利用深度学习技术对加那利群岛天文台的光学湍流数据库进行再分析，通过开发软件框架、生成热图、用卷积神经网络处理及无监督聚类，揭示了湍流结构，为该天文台大气条件提供新视角，是大规模湍流表征新方法。
AI 中文摘要
大气光学湍流是地基望远镜图像退化的主要原因，其垂直分布由折射率结构常数（Cn2(h)）描述。加那利群岛天文台（OCAN）通过长期广义SCIDAR活动进行了广泛表征，拥有近300个观测夜和数十万条Cn2(h)剖面数据。本文利用深度学习技术对OCAN数据库进行再分析，开发了端到端软件框架，生成时间-垂直湍流热图，用预训练卷积神经网络处理以获得紧凑深度嵌入，通过无监督聚类识别潜在大气状态，为加那利群岛天文台的大气条件提供了补充视角，是利用现代深度学习技术进行大规模湍流表征的新方法。
英文摘要
Atmospheric optical turbulence, caused by refractive-index fluctuations driven by wind and temperature inhomogeneities, is the primary source of image degradation in ground-based telescopes. Its vertical distribution, described by the refractive-index structure constant (Cn2(h)), determines essential parameters such as seeing, isoplanatic angle, and coherence time, which characterize astronomical sites and are also crucial for the design and performance of high-resolution instruments. The Observatorios de Canarias (OCAN), comprising the Observatorio del Roque de los Muchachos on La Palma and the Observatorio del Teide on Tenerife, have been extensively characterized through long-term Generalized SCIDAR campaigns. In total, the database comprises nearly 300 observing nights and hundreds of thousands of individual Cn2(h) profiles. We present a reanalysis of the OCAN database using deep-learning techniques. An end-to-end software framework was developed to ingest, preprocess, and transform the original turbulence measurements into standardized numerical representations suitable for machine-learning analysis. Temporal-vertical turbulence heatmaps are generated under multiple temporal configurations and enriched with seasonal information. These representations are processed by pre-trained convolutional neural networks (CNN) used as frozen feature extractors to obtain compact deep embeddings. Unsupervised clustering is subsequently applied to identify latent atmospheric regimes within the dataset. The resulting clusters reveal coherent turbulence structures and offer a complementary perspective on atmospheric conditions at the Canary Islands observatories. This methodology provides new insights into turbulence variability at the Canary Islands sites and represents a novel approach to large-scale turbulence characterization using modern deep-learning techniques.
Comments11 pages, 7 figures, SPIE Astronomical Telescopes + Instrumentation 2026, Paper 14150-247
