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.

中国人9872979 在过去的十年里，宇宙学经历了一次复兴，从一门数据匮乏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）的观测已经开始给我们提供早期宇宙的详细图像;望远镜已经发现了距离相当于宇宙目前年龄十分之一的星系;大规模的红移测量已经开始绘制出附近宇宙的结构。 然而，这些数据集的规模有可能使宇宙学数据淹没。 实现我们的科学目标取决于满足定量的新数据所带来的定性的新计算挑战。 宇宙学家在数据的分析、综合和呈现中所面临的问题--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数据可视化--也是当前计算机科学和统计学研究的前沿。 天文学家和他们的同事们在天体物理学家、统计学家和计算机科学家之间形成了一个有重点的合作，以开发计算工具、技术和技术来科普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，使天体物理学和其他数据密集型学科的新研究成为可能并为之提供信息。研究沿着沿着四条相互关联的路径组织。 首先，必须有合适的工具来分析单个数据集。其次是对数据集进行合成和模拟，以形成宇宙结构演化的连贯画面。 最后，必须能够访问数据和分析产品，无论是合作成员还是外部社区。 宇宙学是在最大尺度上理解宇宙的探索，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（CMB）的光最后一次与宇宙中的物质相互作用是在它大约10万年的时候（它的年龄是今天150亿年的一小部分）;观察CMB使宇宙学家能够绘制出这些早期的宇宙。 在地球附近，观察星系的分布可以让我们看到宇宙的现状。 仅仅在十年前，这些研究所涉及的数据量还很小。 望远镜和探测器技术的进步使得这些数据成倍增长;这种巨大的增长推动了我们分析这些数据的计算能力的极限。研究人员和他们来自天体物理学、计算机科学和统计学的同事们开发了分析和综合这些大量数据的工具。 这使他们能够形成一个连贯的和完整的宇宙演化的图片从最早的时间到今天，也许最重要的是，到遥远的未来。