CDS&E: Reconstruction of universe's initial conditions with galaxies

CDS

基本信息

批准号：
1814370
负责人：
Uros Seljak
金额：
$ 52.08万
依托单位：
University of California-Berkeley
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1814370&HistoricalAwards=false
关键词：
CDS&E Reconstruction universe initial conditions

项目摘要

The universe evolved from a simple state where matter was almost uniformly distributed in space. In the present day the matter is very strongly clustered into galaxies, clusters of galaxies, and even larger structures. This evolution is governed by gravity and by additional processes such as formation of stars in galaxies. There is enormous amount of information about the universe origins, content, and future evolution hidden in the galaxy distribution. This information is difficult to access in the present-day form because it has been scrambled by gravity and other processes. The goal of this project is to use simulations to reconstruct the initial conditions of our universe. When these are evolved in time with known laws of physics, they give rise to our visible universe. Ultimately this will allow a movie to made of our universe starting from the initial smooth distribution and ending in images of actual galaxies such as the Hubble Deep Field. A major benefit of this method is that information about our universe can be simply extracted from the initial conditions. More broadly, an aim of this project is to impact other communities where similar problems arise such as machine learning via the methods and tools developed The primary goal of this project is to develop and apply a new set of theoretical and computational instruments, including new statistical methods, algorithms, and computational implementations, to optimally reconstruct the initial condition of our universe from the spatial distribution of galaxies. Galaxies are a primary probe of the large scale structure of the universe that are or will be observed by surveys such as the Sloan Digital Sky Survey (SDSS), the Dark Energy Survey (DES), the Large Synoptic Survey Telescope (LSST), the Dark Energy Spectroscopic Instrument (DESI), EUCLID and the Wide Field Infrared Survey Telescope (WFIRST). This project will extend a hierarchical probabilistic generative model developed by the PI's team to the modelling of galaxies. The framework attempts to solve an exact probabilistic model for the initial conditions that is conditioned on the data with a process that combines elements of numerical optimization in high dimensions and analytic marginalization to find the best solution and their covariance matrix. The proposed research will apply this method to galaxy redshift catalogs and their surrounding dark matter information inferred from weak lensing. The method will be developed using realistic simulations of both dark matter and of galaxies populated in the dark matter and hydro simulations, before being applied to real data. This research will explore best methods to achieve fast convergence in the search for local and global minimum and aims to have an impact more broadly to research areas (e.g. neural networks) outside astronomy in the tools developed for non-convex optimization in very high dimensions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

宇宙是从物质几乎均匀分布在空间中的简单状态演化而来的。如今，物质非常强烈地聚集成星系、星系团，甚至更大的结构。这种演化受到重力和其他过程（例如星系中恒星的形成）的控制。星系分布中隐藏着关于宇宙起源、内容和未来演化的大量信息。这些信息以目前的形式很难获取，因为它已经被重力和其他过程打乱了。该项目的目标是利用模拟来重建宇宙的初始条件。当它们按照已知的物理定律及时演化时，就会产生我们可见的宇宙。最终，这将使我们的宇宙成为一部电影，从最初的平滑分布开始，到哈勃深场等实际星系的图像结束。这种方法的一个主要好处是可以从初始条件中简单地提取有关我们宇宙的信息。更广泛地说，该项目的目标是通过开发的方法和工具影响出现类似问题的其他社区，例如机器学习。该项目的主要目标是开发和应用一套新的理论和计算工具，包括新的统计工具方法、算法和计算实现，以根据星系的空间分布最优地重建宇宙的初始条件。星系是宇宙大尺度结构的主要探测器，正在或将通过诸如斯隆数字巡天（SDSS）、暗能量巡天（DES）、大型综合巡天望远镜（LSST）、暗能量光谱仪 (DESI)、EUCLID 和广域红外巡天望远镜 (WFIRST)。该项目将把 PI 团队开发的分层概率生成模型扩展到星系建模。该框架试图通过结合高维数值优化和分析边缘化元素的过程来求解以数据为条件的初始条件的精确概率模型，以找到最佳解决方案及其协方差矩阵。拟议的研究将将该方法应用于星系红移目录及其从弱透镜推断的周围暗物质信息。该方法将通过对暗物质和暗物质中填充的星系的真实模拟以及水力模拟来开发，然后再应用于实际数据。这项研究将探索在寻找局部和全局最小值时实现快速收敛的最佳方法，旨在通过为极高维度的非凸优化而开发的工具对天文学以外的研究领域（例如神经网络）产生更广泛的影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。