Putting Astronomy's Head in the Cloud

将天文学的头脑置于云端

• 批准号: 0844580
• 负责人: Andrew Connolly
• 金额: $ 45万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-02-01 至 2012-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0844580&HistoricalAwards=false
• 关键词: Putting; Astronomy; Head; Cloud

Astrophysics is addressing many fundamental questions about the nature of the universe through a series of ambitious wide-field optical and infrared imaging surveys (e.g. studying the properties of dark matter and the nature of dark energy). To accomplish these goals requires new methodologies for analyzing and understanding petascale data sets (with the data being collected at a rate 1000x greater than current surveys). This research focusses on exploring an emerging paradigm for data intensive applications, map-reduce(using Hadoop for the implementation of map-reduce), and how it scales to the analysis of astronomical images. The work is addressing the efficiency of map-reduce for determining spatial and temporal overlaps between terabyte scale imaging data sets when compared to standard database techniques. We are delivering new algorithms for indexing, accessing and analyzing astronomical images using map-reduce that can balance the load between the compute nodes on distributed systems. We are also delivering applications that will analyze the spatial distribution of star formation within galaxies (combining large multispectral data sets) and for identifying asteroids within a time series of data where the asteroid may be below the detection threshold of any one image. This work will have a broad range of applications to any data intensive field.

天体物理学正在通过一系列雄心勃勃的宽视场光学和红外成像调查（例如研究暗物质的性质和暗能量的性质）来解决有关宇宙性质的许多基本问题。为了实现这些目标，需要新的方法来分析和理解千万亿次数据集（数据收集的速度比当前调查高1000倍）。 这项研究的重点是探索一个新兴的数据密集型应用程序的范例，map-reduce（使用Hadoop实现map-reduce），以及它如何扩展到天文图像的分析。 这项工作是解决效率的地图减少确定的空间和时间重叠之间的TB规模的成像数据集相比，标准的数据库技术。我们正在使用map-reduce提供索引，访问和分析天文图像的新算法，可以平衡分布式系统上计算节点之间的负载。我们还提供了一些应用程序，这些应用程序将分析星系内星星形成的空间分布（结合大型多光谱数据集），并在一系列数据中识别小行星，其中小行星可能低于任何一张图像的检测阈值。这项工作将有一个广泛的应用到任何数据密集型领域。