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.
Comparative analysis of sky quality and meteorological variables during the total lunar eclipse on 14-15 April 2014 and their effect on qualitative measurements of the Bortle scale
Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images
Western Sydney University（西悉尼大学）；CSIRO Astronomy and Space Science（CSIRO天文与空间科学部）；The Australian National University（澳大利亚国立大学）；Data61, CSIRO（CSIRO Data61）；Universidad de Guanajuato（瓜纳华托大学）；University of Oxford（牛津大学）；University of Minnesota（明尼苏达大学）
AXS: A framework for fast astronomical data processing based on Apache Spark
我们介绍AXS（Astronomy eXtensions for Spark），这是一个基于Apache Spark的可扩展开源天文数据分析框架，Apache Spark是一个广泛使用的行业标准大数据处理引擎。基于Spark中的功能，AXS旨在使用熟悉的Python/AstroPy概念，DataFrame API和SQL语句查询和分析几乎任意大的天文目录。我们通过i）为Spark添加支持以实现高效的在线位置交叉匹配，ii）提供支持天文数据分析常用操作的Python库。为了支持可扩展的交叉匹配，我们开发了一种ZONES算法（Gray et al. 2004）的变体，能够在分布式、无共享架构中运行。我们将其与数据分区方案相结合，该方案可以实现快速目录交叉匹配，并处理深空数据集中经常出现的数据倾斜。交叉匹配和其他常用功能通过易于使用的Python API向最终用户公开。我们展示了AXS在SDSS、ZTF、Gaia DR 2和AllWise目录上的技术和科学性能。使用AXS，我们能够在大约30秒内对Gaia DR 2（18亿行）和AllWise（9亿行）数据集进行动态交叉匹配。我们将讨论像AXS这样的云就绪分布式系统如何提供一种自然的方式来实现对LSST等大型数据集的全面最终用户分析。
Beyond Point Estimates: Benchmarking Uncertainty Quantification Methods on the AION-1 Astronomical Foundation Model
Scientific Computing Division Fermi National Accelerator Laboratory（费米国家加速器实验室科学计算部）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；Department of Astronomy and Astrophysics University of Chicago（芝加哥大学天文学与天体物理学系）；NSF and Simons SkAI Institute（国家科学基金会与Simons SkAI研究所）；Google DeepMind（谷歌DeepMind）
