COSMOLOGY THEORY MEETS DATA: MODELLING NON-LINEAR SCALES FOR DARK ENERGY EXPERIMENTS

在过去的几年里，我们已经进入了观测宇宙学的“黄金时代”。标准的宇宙学模型非常符合数据，但需要存在两种奇异的成分，即宇宙学常数形式的暗能量和冷暗物质。暗能量目前主导着宇宙，它是宇宙加速膨胀的原因。在未来几年，最先进的宇宙学调查与仪器，如欧几里得卫星和平方公里阵列（SKA），有望绘制宇宙的大尺度结构，并确定暗能量的性质。这个博士项目旨在解决理论和观测宇宙学的最大挑战之一：如何准确有效地模拟暗能量模型的非线性（即小尺度）行为。学生将获得理论和观测宇宙学，暗能量理论，数值方法的经验，并将成为充满活力的国际科学家社区的一部分。该学生将参与欧几里得和SKA的合作。该项目将涉及模拟和分析大型数据集，例如大型天空光学星系目录和中性氢强度地图。它还将涉及使用HPC设施的高性能计算（例如，为了执行马尔可夫链蒙特卡罗分析）。我们还计划利用机器学习技术进行宇宙学参数估计。

During the last few years, we have entered the, "golden era", of observational cosmology. The standard cosmological model fits the data extremely well, but requires the existence of two exotic constituents, namely dark energy in the form of a cosmological constant, and cold dark matter. Dark energy currently dominates the Universe and it is responsible for its accelerated expansion.In the next few years, state-of-the-art cosmological surveys with instruments like the Euclid satellite and the Square Kilometre Array (SKA), are promising to map the large scale structure of the Universe and pin down the nature of dark energy.This PhD project aims to tackle one of the biggest challenges in theoretical and observational cosmology: how to model accurately and efficiently the non-linear (that is, small scale) behaviour of dark energy models. The student will gain experience in theoretical and observational cosmology, dark energy theory, numerical methods, and will be part of a vibrant international community of scientists. The student will be involved in the Euclid & SKA collaborations.This project will involve simulating and analysing large datasets, for example large sky optical galaxy catalogues and neutral hydrogen intensity maps. It will also involve high performance computing using HPC facilities (for example in order to perform Markov Chain Monte Carlo analyses). We also plan to utilise machine learning techniques for cosmological parameter estimation.