论文标题
基于环境和望远镜工作条件的天文图像质量预测
Astronomical Image Quality Prediction based on Environmental and Telescope Operating Conditions
论文作者
论文摘要
智能安排在基于地面的天文观测器上进行的科学暴露序列非常具有挑战性。观察时间过度订阅，大气条件不断变化。我们建议使用机器学习指导天文台调度。利用加拿大 - 弗朗西·霍瓦伊望远镜记录的曝光，环境和工作条件的15年档案，我们构建了一个概率数据驱动的模型，可准确预测图像质量。我们证明，通过优化放置在望远镜圆顶上的十二个通风孔的开口和关闭，我们可以降低圆顶引起的湍流并将望远镜图像质量提高（0.05-0.2弧秒）。这意味着减少了$ \ sim 10-15 \％$的曝光时间（因此成本）。我们的研究是迈向基于数据的当前和下一代望远镜运营的基于数据优化的第一步。
Intelligent scheduling of the sequence of scientific exposures taken at ground-based astronomical observatories is massively challenging. Observing time is over-subscribed and atmospheric conditions are constantly changing. We propose to guide observatory scheduling using machine learning. Leveraging a 15-year archive of exposures, environmental, and operating conditions logged by the Canada-France-Hawaii Telescope, we construct a probabilistic data-driven model that accurately predicts image quality. We demonstrate that, by optimizing the opening and closing of twelve vents placed on the dome of the telescope, we can reduce dome-induced turbulence and improve telescope image quality by (0.05-0.2 arc-seconds). This translates to a reduction in exposure time (and hence cost) of $\sim 10-15\%$. Our study is the first step toward data-based optimization of the multi-million dollar operations of current and next-generation telescopes.
