TBC

待定

• 批准号: 2903931
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2903931
• 关键词: TBC

The cosmic web is the largest structural pattern known in nature. Galaxies, gas, and dark matter are woven into a complex weblike structure on scales of a few up to a hundred megaparsec, forming dense compact clusters, elongated filaments, and sheetlike walls surrounding near-empty void regions. The complex connectivity and intrinsic multiscale character of the cosmic web reflect the primordial conditions out of which the structural elements of the universe have emerged through non-linear gravitational evolution. Modern surveys capture the cosmic matter distribution with unprecedented precision, and the complex weblike structure can be readily reproduced in N-body simulations. Yet, a theoretical understanding of the emergent complexity from the underlying structure formation processes remains elusive.Remarkably, the geometry of the cosmological structure formation can be rigorously understood in the mathematical framework of catastrophe theory (e.g. Arnol'd et al. (1985)), which reveals that the collapsing structure may be classified in terms of caustics in the dark matter flow. The caustic skeleton of the cosmic web goes back to the seminal work of Arnol'd, Shandarin and Zel'dovich (1982), but it was only recently that Feldbrugge et al. (2018) provided an exhaustive list of caustic conditions for the formation of the cosmic web in the realistic three-dimensional universe. This PhD thesis will build on and further develop this formalism in order to investigate the cosmic web from an unprecedented analytical perspective.Several projects studying different aspects of the caustic skeleton model of the cosmic web are envisaged. Firstly, we will use the caustic conditions to set up constrained random initial conditions for specialised N-body simulations. This will allow us to infer the characteristic geometries and physical observables of the different structural elements. Moreover, random field theory enables a systematic investigation of their number densities and correlation functions as imprinted in the primordial initial conditions. As far as the formalism is concerned, this thesis will also study the influence of more general cosmological structure formation models on the evolving caustic network, which can be directly compared to traditional and community-standard identification methods for the cosmic web. In a broader context, this will also pave the way for novel machine learning techniques for analysing cosmological large-scale structure in next-generation inference pipelines.

宇宙网络是自然界已知的最大结构模式。星系，气体和暗物质被编织成一个复杂的Weblike结构，尺寸为几百兆帕，形成密集的紧凑型簇，细长的细丝和近乎空的空隙区域周围的薄状墙壁。宇宙网络的复杂连通性和内在的多尺度特征反映了宇宙的结构元素通过非线性引力演化而出现的原始条件。现代调查以前所未有的精度捕获了宇宙物质分布，并且可以在N体模拟中容易复制复杂的Weblike结构。 Yet, a theoretical understanding of the emergent complexity from the underlying structure formation processes remains elusive.Remarkably, the geometry of the cosmological structure formation can be rigorously understood in the mathematical framework of catastrophe theory (e.g. Arnol'd et al. (1985)), which reveals that the collapsing structure may be classified in terms of caustics in the dark matter flow.宇宙网络的苛刻骨骼可以追溯到Arnol'd，Shandarin和Zel'Dovich（1982）的开创性工作，但直到最近，Feldbrugge等人。 （2018年）为在现实的三维宇宙中形成宇宙网络提供了详尽的苛性条件清单。该博士学位论文将在前所未有的分析角度研究宇宙网络，并进一步发展这种形式主义。首先，我们将使用苛性条件为专门的N体模拟设置受约束的随机初始条件。这将使我们能够推断出不同结构元素的特征几何形状和物理可观察物。此外，随机场理论可以系统地研究其数量密度和相关功能，如原始初始条件所印象。就形式主义而言，本论文还将研究更通用的宇宙学结构形成模型对不断发展的苛性网络的影响，该模型可以直接与宇宙网络的传统和社区标准识别方法直接进行比较。在更广泛的背景下，这也将为新的机器学习技术铺平道路，用于分析下一代推理管道中的宇宙学大规模结构。