Putting Astronomy's Head in the Cloud

天体物理学正在通过一系列雄心勃勃的广域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)来解决有关宇宙本质的许多基本问题。为了实现这些目标，需要新的方法来分析和理解千万亿级的数据集(收集数据的速度比目前的调查高出1000倍)。这项研究的重点是探索一种新兴的数据密集型应用程序-MAP-REDUTE(使用Hadoop实现MAP-REDUTE)，以及它如何扩展到天文图像的分析。这项工作是解决与标准数据库技术相比，地图缩减在确定TB级成像数据集之间的空间和时间重叠方面的效率。我们正在为索引、访问和分析天文图像提供新的算法，使用MAP-Reduce可以平衡分布式系统上计算节点之间的负载。我们还提供将分析星系内恒星形成的空间分布的应用程序(结合大型多光谱数据集)，并在小行星可能低于任何一张图像的探测阈值的数据时间序列中识别小行星。这项工作将在任何数据密集型领域有广泛的应用。

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.