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

CDS

• 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

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.

宇宙从物质在空间中几乎均匀分布的简单状态演化而来。在今天，物质非常强烈地聚集成星系、星系团，甚至更大的结构。这种演化受引力和其他过程（如星系中恒星的形成）的支配。在星系分布中隐藏着大量关于宇宙起源、内容和未来演化的信息。这些信息很难以今天的形式获得，因为它已经被重力和其他过程打乱了。这个项目的目标是用模拟来重建我们宇宙的初始条件。当它们按照已知的物理定律进化时，就产生了我们可见的宇宙。最终，这将允许电影制作我们的宇宙，从最初的平滑分布开始，以实际星系的图像结束，比如哈勃深场。这种方法的一个主要好处是，可以简单地从初始条件中提取有关我们宇宙的信息。更广泛地说，这个项目的目的是通过开发的方法和工具来影响其他出现类似问题的社区，比如机器学习。这个项目的主要目标是开发和应用一套新的理论和计算工具，包括新的统计方法、算法和计算实现，以从星系的空间分布中最佳地重建我们宇宙的初始条件。星系是宇宙大尺度结构的主要探测器，它正在或将被诸如斯隆数字巡天（SDSS）、暗能量巡天（DES）、大型综合巡天望远镜（LSST）、暗能量光谱仪器（DESI）、EUCLID和宽视场红外巡天望远镜（WFIRST）等巡天观测项目观测到。该项目将把PI团队开发的分层概率生成模型扩展到星系模型。该框架试图解决初始条件的精确概率模型，该模型以数据为条件，结合高维数值优化元素和分析边缘化元素，以找到最佳解及其协方差矩阵。拟议的研究将把这种方法应用于星系红移目录及其周围的暗物质信息，这些信息是由弱透镜效应推断出来的。在应用于实际数据之前，该方法将通过对暗物质和暗物质中星系的真实模拟和氢模拟来发展。这项研究将探索在搜索局部和全局最小值时实现快速收敛的最佳方法，并旨在通过为非常高维的非凸优化开发的工具，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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Marginal unbiased score expansion and application to CMB lensing

边际无偏分数扩展及其在 CMB 透镜中的应用

• DOI: 10.1103/physrevd.105.103531
• 发表时间: 2022
• 期刊: Physical Review D
• 影响因子: 5
• 作者: Millea, Marius;Seljak, Uroš
• 通讯作者: Seljak, Uroš

Translation and rotation equivariant normalizing flow (TRENF) for optimal cosmological analysis

用于最佳宇宙学分析的平移和旋转等变归一化流 (TRENF)

• DOI: 10.1093/mnras/stac2010
• 发表时间: 2022
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: Dai, Biwei;Seljak, Uroš
• 通讯作者: Seljak, Uroš

The relativistic dipole and gravitational redshift on LSS

• DOI: 10.1088/1475-7516/2019/04/050
• 发表时间: 2018-11
• 期刊: Journal of Cosmology and Astroparticle Physics
• 影响因子: 6.4
• 作者: E. Dio;U. Seljak

Disconnected covariance of 2-point functions in large-scale structure

• DOI: 10.1088/1475-7516/2019/01/016
• 发表时间: 2018-11
• 期刊: Journal of Cosmology and Astroparticle Physics
• 影响因子: 6.4
• 作者: Yin Li;Sukhdeep Singh;Byeonghee Yu;Yu Feng;U. Seljak

FlowPM: Distributed TensorFlow implementation of the FastPM cosmological N-body solver

• DOI: 10.1016/j.ascom.2021.100505
• 发表时间: 2020-10
• 期刊: Astron. Comput.
• 作者: C. Modi;F. Lanusse;U. Seljak