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.

宇宙从一个简单的状态演变，即物质几乎均匀分布在太空中。现在，物质非常强烈地聚集在星系，星系簇甚至更大的结构中。该进化受重力和其他过程（例如星系中恒星形成）的控制。关于宇宙起源，内容和未来的进化，隐藏在银河分布中的信息有大量信息。 这些信息很难以当今形式访问，因为它已经被重力和其他过程拼凑而成。该项目的目的是使用仿真来重建我们宇宙的初始条件。当它们随着已知的物理定律而及时地演变而来，它们会产生我们的可见宇宙。最终，这将使电影从最初的平滑分布开始，结束于实际星系（例如哈勃深场）的图像开始。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，该项目的目的是影响出现类似问题的其他社区，例如通过方法和工具开发机器学习，该项目的主要目标是开发和应用一组新的理论和计算工具，包括新的统计方法，算法和计算实现，从而最佳地从天然分布的宇宙中重建宇宙的初始条件。星系是对宇宙大规模结构的主要探针，这些调查（例如Sloan Digital Sky调查（SDSS），暗能量调查（DES），大型Synoptic调查望远镜（LSST），深色能源光谱仪器（DESI），Euclid仪器（DESI），Euclid和Field Field Field Infrared Infrared Telescope（Wfirst）（WFIRST）等调查（sdss），深度能量调查（DES），大型概要调查望远镜（LSST）。该项目将把PI团队开发的层次概率生成模型扩展到星系建模。该框架试图解决基于数据的初始条件的确切概率模型与将数值优化元素结合在高维度和分析边缘化中的过程中，以找到最佳的解决方案及其协方差矩阵。拟议的研究将将这种方法应用于Galaxy Redshift目录及其周围的暗物质信息，从弱透镜中推断出来。该方法将使用对暗物质和水电模拟中填充的暗物质和星系的现实模拟进行开发，然后将其应用于真实数据。 这项研究将探讨在寻找本地和全球最低最低限度的快速融合的最佳方法，并旨在在天文学之外的研究领域（例如神经网络）在非常高度的非convex优化的工具中更加广泛地影响，以非常高的范围优化。该奖项反映了NSF的法定任务，并通过评估范围的范围来反映出支持者的知识群体，并通过评估了基础的范围。

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