Reconstructing structure growth across cosmic time.

重建跨宇宙时间的结构增长。

• 批准号: 2444981
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2444981
• 关键词: Reconstructing; structure; growth; across; cosmic

The growth of structure on large scales is one of the most sensitive observables in cosmology to detect deviations from General Relativity and to constrain the properties of Dark Energy. Currently a number of different low-redshift probes have individually found low-significance tensions with the measurement of the Cosmic Microwave Background anisotropies, which seem to indicate a slower growth at late times than predicted in the standard cosmological model. The main goal of this project will be to unify the constraints of all available datasets and build a model-independent reconstruction of the Universe's growth history that can then be used to constrain alternatives Dark Energy models. This will allow us to ascertain the source of the ongoing tension between low-redshift and high-redshift probes of inhomogeneities.This project falls within the area of cosmological theory and data analysis. The project will make use of data from past and ongoing experiments, including the Planck satellite, the Dark Energy Survey, HSC, SDSS, 2MASS, WISE and SuperCosmos. The data thus analysed in a unified, consistent framework, will be publicly releases together with the software developed to carry out the analysis. This will allow us and the community to carry out other multi-probe cosmological analyses, extracting the most out of currently existing datasets.

大尺度结构的生长是宇宙学中最敏感的观测量之一，可以用来检测广义相对论的偏差，并限制暗能量的性质。目前，许多不同的低红移探测器在测量宇宙微波背景各向异性时，都发现了低显著性的张力，这似乎表明在后期的增长速度比标准宇宙学模型中预测的要慢。该项目的主要目标是统一所有可用数据集的约束，并建立一个独立于模型的宇宙增长历史重建，然后可以用来约束替代暗能量模型。这将使我们能够确定低红移和高红移不均匀性探测器之间持续存在的紧张关系的来源。该项目福尔斯宇宙学理论和数据分析领域。该项目将利用过去和正在进行的实验的数据，包括普朗克卫星、暗能量调查、HSC、SDSS、2MASS、WISE和SuperCosmos。在统一、一致的框架内分析的数据将与为进行分析而开发的软件一起公布。这将使我们和社区能够进行其他多探测器宇宙学分析，从现有的数据集中提取最多的信息。