KDI: Computational Challenges in Cosmology

KDI：宇宙学的计算挑战

• 批准号: 9872979
• 负责人: Andrew Jaffe
• 金额: $ 140万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-10-01 至 2003-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9872979&HistoricalAwards=false
• 关键词: KDI; Computational; Challenges; Cosmology

Silk9872979 In the past decade cosmology has undergone a renaissance,transforming from a data-starved science to a data-driven one.The COBE satellite and subsequent observations of the CosmicMicrowave Background (CMB) have begun to give us a detailedpicture of the early Universe; telescopes have found galaxies atdistances corresponding to the Universe at one-tenth of itspresent age; large-scale redshift surveys have begun to map outthe structure of the nearby Universe. However, the size of thesedatasets threatens to leave cosmology data-swamped. Realizing ourscientific goals depends on meeting the qualitatively newcomputational challenges set by the quantitatively new data. Theissues cosmologists face in the analysis, synthesis andpresentation of the data --- including data compression andtransmission, mass storage, data mining, parallel algorithms andscaling, inverse problems and regularization methods, and complexdata visualization --- are also at the forefront of currentresearch in Computer Science and Statistics. The investigatorsand their colleagues form a focused collaboration betweenastrophysicists, statisticians and computer scientists to developcomputational tools, techniques and technologies to cope with thenew challenges posed by these datasets. The needs of Cosmologyprovide a practical spur to new developments in Computer Scienceand Statistics, enabling and informing new research both withinastrophysics and more widely in other data-intensive disciplines.Research is organized along four interlocking paths. First, theremust be appropriate tools to analyze the individual datasets.Next are the synthesis and simulation of the datasets toformulate a coherent picture of the evolution of structure in theUniverse. Finally, there must be access to the data and theproducts of the analysis, both to members of the collaborationand to the outside community. Cosmology is the quest for the understanding of the Universeon the largest scales, and of the events that unfolded in thefirst moments after the Big Bang. Light that now makes up theCosmic Microwave Background (or CMB) last interacted with matterin the Universe when it was about one hundred thousand years old(a small fraction of its age today of fifteen billion years);observing the CMB allows cosmologists to map out the Universe atthese very early times. Somewhat closer to home, observing thedistribution of galaxies lets us see the present state of theUniverse. Only ten years ago, the amount of data involved inthese studies was tiny. Advances in telescope and detectortechnology has allowed a manyfold increase in these data; thisvast expansion pushes the limits of our computational ability toanalyze it. The investigators and their colleagues, fromastrophysics, computer science, and statistics, develop tools toanalyze and synthesize this vast amount of data. This allows themto form a coherent and complete picture of the evolution of theUniverse from the earliest times to the present day and ---perhaps most importantly --- far into the future.

Silk9872979 在过去的十年中，宇宙学经历了一次复兴，从一门缺乏数据的科学转变为一门数据驱动的科学。COBE 卫星和随后对宇宙微波背景 (CMB) 的观测已经开始为我们提供早期宇宙的详细图片；望远镜已经发现了一些星系，其距离相当于宇宙当前年龄的十分之一；大规模的红移调查已经开始绘制附近宇宙的结构图。 然而，这些数据集的规模可能会让宇宙学数据陷入困境。 实现我们的科学目标取决于满足新定量数据带来的新定性计算挑战。 宇宙学家在数据分析、综合和呈现方面面临的问题——包括数据压缩和传输、海量存储、数据挖掘、并行算法和缩放、逆问题和正则化方法以及复杂的数据可视化——也处于当前研究的前沿。计算机科学和统计学。 研究人员及其同事在天体物理学家、统计学家和计算机科学家之间进行了重点合作，开发计算工具、技巧和技术，以应对这些数据集带来的新挑战。 宇宙学的需求为计算机科学和统计学的新发展提供了实际的推动力，为天体物理学以及更广泛的其他数据密集型学科的新研究提供了支持和信息。研究沿着四个相互关联的路径进行组织。 首先，必须有适当的工具来分析各个数据集。接下来是数据集的综合和模拟，以形成宇宙结构演化的连贯图景。 最后，协作成员和外部社区都必须能够访问数据和分析产品。 宇宙学是寻求在最大尺度上理解宇宙以及大爆炸后最初时刻所发生的事件。 现在构成宇宙微波背景（或 CMB）的光最后一次与宇宙中的物质相互作用是在宇宙大约十万岁的时候（只是今天 150 亿年年龄的一小部分）；观测 CMB 使宇宙学家能够绘制出宇宙处于非常早期的时期。 离我们的家园更近一些，观察星系的分布可以让我们看到宇宙的现状。 仅在十年前，这些研究涉及的数据量还很少。 望远镜和探测器技术的进步使得这些数据增加了很多倍。这种巨大的扩展突破了我们分析它的计算能力的极限。 研究人员及其来自天体物理学、计算机科学和统计学的同事开发了工具来分析和综合这些大量数据。 这使他们能够形成一幅连贯而完整的宇宙演化图景，从最早的时代到现在，甚至——也许最重要的是——遥远的未来。