A Generative Model of Cosmic Large Scale Structure

宇宙大尺度结构的生成模型

• 批准号: 1966433
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1966433
• 关键词: Generative; Model; Cosmic; Large; Scale

Current and forthcoming galaxy surveys will provide unprecedentedly tight constraints on physics beyond the cosmological standard model. Just as important as the best-fit parameters obtained from these datasets are the statistical uncertainties associated with them. The measurement errors are usually determined from large suites of mock survey realisations, each built upon computationally expensive cosmological N-body simulations. 10,000s of such simulations will be required in the near future, but it is unlikely sufficient super-computer time will be available. This is a limiting factor in cosmological analyses of current surveys, and a critical unsolved problem for the next generation of surveys such as Euclid and LSST. In this project we will develop a deep-learning network that effectively compresses the input simulation information and subsequently generates statistically independent mock universes with characteristics identical to the input, thus enabling the calculation of realistic error models with minimal extra computational cost from just a few N-body simulations.

当前和即将进行的星系巡天将对宇宙学标准模型之外的物理学提供前所未有的严格约束。与从这些数据集获得的最佳拟合参数同样重要的是与它们相关的统计不确定性。测量误差通常是从大型模拟测量实现中确定的，每个都建立在计算昂贵的宇宙学N体模拟上。在不久的将来，将需要10,000次这样的模拟，但不太可能有足够的超级计算机时间。这是当前巡天宇宙学分析的一个限制因素，也是下一代巡天（如欧几里得和LSST）尚未解决的关键问题。在这个项目中，我们将开发一个深度学习网络，有效地压缩输入模拟信息，随后生成统计上独立的模拟宇宙，其特征与输入相同，从而能够从几个N体模拟中以最小的额外计算成本计算真实的误差模型。