标题： 帕拉纳尔天文台（VLT和ELT）科学观测的短期调度优化 显示英文标题

标题： The optimisation of short-term scheduling of science observations at Paranal observatory (VLT and ELT)

摘要： 科学观测短期调度（STS）的效率可以定义为单位时间内完成的高排名观测数量的函数。 当前欧洲南方天文台（ESO）帕拉纳尔天文台的STS是通过使用明确算法对观测进行过滤和排序来实现的，从而在时间t提出一个建议的观测。这个帕拉纳尔STS模型已经成功应用了十余年。 在这里，我们总结当前的VLT(I) STS模型，并概述优化VLT(I)和未来极大望远镜（ELT）科学回报的持续努力。 我们描述了构建的STS模拟器，它使我们能够评估模型假设的变化如何影响STS的效果。 这些变化包括：使用短期大气参数预测代替假设其时间演化是恒定的；评估如何改变不同观测参数的排序权重以优化调度；使STS更加“动态”，以考虑中期调度约束。 我们展示了具体的结果，比较了机器学习对视宁度的预测在与当前模型（使用过去10分钟测量视宁度的中位数进行观测选择）相比时，如何提高STS的效率。 显示英文摘要

摘要： The efficiency of science observation Short-Term Scheduling (STS) can be defined as being a function of how many highly ranked observations are completed per unit time. Current STS at ESO's Paranal observatory is achieved through filtering and ranking observations via well-defined algorithms, leading to a proposed observation at time t. This Paranal STS model has been successfully employed for more than a decade. Here, we summarise the current VLT(I) STS model, and outline ongoing efforts of optimising the scientific return of both the VLT(I) and future ELT. We describe the STS simulator we have built that enables us to evaluate how changes in model assumptions affect STS effectiveness. Such changes include: using short-term predictions of atmospheric parameters instead of assuming their constant time evolution; assessing how the ranking weights on different observation parameters can be changed to optimise the scheduling; changing STS to be more `dynamic' to consider medium-term scheduling constraints. We present specific results comparing how machine learning predictions of the seeing can improve STS efficiency when compared to the current model of using the last 10\,min median of the measured seeing for observation selection.