A Generative Model of Cosmic Large Scale Structure

当前和即将到来的星系调查将提供超越宇宙标准模型的前所未有的严格物理约束。与从这些数据集获得的最佳拟合参数同样重要的是与之相关的统计不确定性。测量误差通常由大型模拟测量实现套件确定，每个模拟测量实现都建立在计算昂贵的宇宙n体模拟基础上。在不久的将来，这样的模拟将需要1万次，但超级计算机不太可能有足够的时间。这是当前调查的宇宙学分析的一个限制因素，也是下一代调查（如欧几里得和LSST）的一个关键未解决问题。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，随后生成与输入特征相同的统计独立模拟宇宙，从而能够以最小的额外计算成本从几个n体模拟中计算出现实的误差模型。

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.