Next generation data analysis for CMB and large-scale structure

CMB 和大型结构的下一代数据分析

• 批准号: RGPIN-2014-04855
• 负责人: Smith, Kendrick
• 金额: $ 2.19万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=588059
• 关键词: Next; generation; data; analysis; CMB

Cosmology is the study of the universe on its largest scales, from the Big Bang until now. The overarching goal is to answer some of the oldest questions in science: What is the universe made of? How did it begin? How will it end? Our quest to answer these questions has led to profound surprises. We have learned that only a small fraction of the universe is composed of the ordinary atomic matter we are familiar with on Earth. Most of the universe is made of "dark matter", a type of matter we have not been able to produce or observe in the laboratory. We have candidate theories to explain how the universe began, or more precisely how the universe came to be in its Big Bang state: hot, dense, and nearly uniform. These theories require one or more new fields -- fields which have never been observed in the laboratory -- to generate the Big Bang state via quantum mechanical processes. Finally, we have found that recently in cosmic history, the universe has come to be dominated by "dark energy", a fluid-like source of energy and pressure which is distinct from dark matter and ordinary matter. We have uncovered the basic ingredients of our universe: dark matter, dark energy, and a quantum mechanical Big Bang. These ingredients are unexpected and we do not understand their fundamental nature. For example, the existence of dark matter tells us that our understanding of particle physics is incomplete: most of the universe is composed of one or more new types of particles, which have not yet been found by particle physicists. Our next challenge is to elucidate the fundamental nature of dark matter, dark energy, and the Big Bang, using powerful new experiments. If we can understand _why_ these ingredients exist, and how they are connected to the rest of physics, we will have succeeded in understanding the birth of our universe and its evolution from the Big Bang to now, one of the oldest and deepest mysteries in science. My own research is centered on analyzing data from new experiments. I am participating in the Planck mission, a European satellite experiment which is making groundbreaking measurements of the cosmic microwave background (CMB). I am also a member of the HSC collaboration, a new experiment which will measure statistical properties of galaxies using a new Japanese-built camera on the 8-meter Subaru telescope in Hawaii. In addition to direct participation in large experiments, I also study theoretical, statistical, and computational questions which are directly related to analysis of future datasets. In cosmology, our basic tool for understanding the universe is statistical analysis of large datasets, such as maps of galaxies throughout the universe. The statistical procedures are extremely complicated, and each generation of experiments brings new challenges. To some extent these challenges can be anticipated in advance, and I am broadly interested in developing statistical machinery which will be used to analyze a wide range of experiments in the future. My proposal focuses on data analysis within the Planck and HSC experiments, and attacking unsolved statistical problems which will have broad impact within the field as a whole. As one of several examples, I am developing a general software package which will allow researchers in the field to run supercomputer simulations of the universe which correspond to different models of the Big Bang, providing a fundamental tool for studying Big Bang physics. The research in my proposal will play a critical role in our efforts to understand how our universe began and evolved, and it is inspiring to think that we may unravel these mysteries in the not-too-distant future.

宇宙学是对宇宙最大尺度的研究，从大爆炸到现在。首要目标是回答一些科学界最古老的问题：宇宙是由什么组成的？它是怎么开始的？它将如何结束？我们对这些问题的探索带来了深刻的惊喜。我们了解到，宇宙中只有一小部分是由我们在地球上熟悉的普通原子物质组成的。宇宙的大部分是由“暗物质”组成的，这是一种我们在实验室里无法产生或观察到的物质。我们有候选的理论来解释宇宙是如何开始的，或者更准确地说，宇宙是如何进入大爆炸状态的：热的、稠密的和几乎一致的。这些理论需要一个或多个新的场--实验室中从未观察到的场--才能通过量子力学过程产生大爆炸状态。最后，我们发现，在最近的宇宙史上，宇宙开始被“暗能量”主导，这是一种有别于暗物质和普通物质的类似流体的能量和压力来源。 我们已经发现了宇宙的基本成分：暗物质、暗能量和量子力学大爆炸。这些成分是意想不到的，我们不了解它们的基本性质。例如，暗物质的存在告诉我们，我们对粒子物理的理解是不完整的：宇宙的大部分是由一种或多种新类型的粒子组成的，这些粒子还没有被粒子物理学家发现。我们的下一个挑战是利用强大的新实验阐明暗物质、暗能量和大爆炸的基本性质。如果我们能够理解这些成分为什么存在，以及它们如何与物理学的其他部分联系在一起，我们就会成功地理解我们的宇宙的诞生及其从大爆炸到现在的演化，这是科学界最古老、最深刻的谜团之一。 我自己的研究集中在分析新实验的数据上。我正在参加普朗克任务，这是一项欧洲卫星实验，正在对宇宙微波背景(CMB)进行开创性的测量。我也是HSC Collaboration的成员，这是一项新的实验，将使用位于夏威夷的8米斯巴鲁望远镜上的日本制造的新相机来测量星系的统计特性。除了直接参与大型实验，我还学习理论、统计和计算问题，这些问题与未来数据集的分析直接相关。在宇宙学中，我们理解宇宙的基本工具是对大数据集的统计分析，例如整个宇宙中星系的地图。统计程序极其复杂，每一代实验都带来新的挑战。在某种程度上，这些挑战可以提前预料到，我对开发统计机制非常感兴趣，这些统计机制将用于分析未来广泛的实验。 我的建议侧重于在普朗克和HSC实验中进行数据分析，并解决尚未解决的统计问题，这些问题将在整个领域产生广泛影响。作为几个例子之一，我正在开发一个通用软件包，它将允许该领域的研究人员运行对应于不同大爆炸模型的宇宙的超级计算机模拟，为研究大爆炸物理学提供一个基本工具。我的提议中的研究将在我们努力理解我们的宇宙如何起源和演化的过程中发挥关键作用，想到我们可能在不太遥远的未来解开这些谜团，这是鼓舞人心的。