Astronomy and Astrophysics at the University of Nottingham - 2023 to 2026

今天推动天文学的一个最基本的问题是，我们在宇宙中观察到的结构是如何产生的？星系及其形成的星系团是宇宙中发现的最大的引力约束系统，因此研究它们的组装和演化是解决这个问题的关键。在这个提议中，我们利用一系列的观测和理论技术，并借鉴诺丁汉在星系和星系团方面的著名专业知识，专注于这个问题。我们拟议的研究使用了从无线电波到光学、直至X射线的整个电磁频谱的成像数据，使用地面和天基设施进行获取。我们试图通过直接分析这些数据，并将其与诺丁汉自己最先进的宇宙结构形成模拟相比较，来了解推动宇宙中许多现象的天体物理学。我们提议的研究的具体目标将解决一些相关的问题。我们将使用三管齐下的方法来更好地了解环境对星系的影响，借鉴地面和空间观测以及对宇宙的大规模模拟。我们将通过开发第一类星体的新的复杂模型来解决我们对宇宙如何在其早期阶段变得重新电离的理解中目前的一个问题。我们将探索机器学习在天文学中的各种应用，作为处理即将到来的大量新数据集的解决方案，包括它在通过测量引力透镜系统中的子结构来表征暗物质性质方面的使用。我们将利用机器学习来开发更客观的星系分类方案，以便更深入地了解它们的形成机制。继续我们对宇宙历史上星系演化的成功研究，我们将通过对附近星系的恒星群的详细分析，建立在我们目前对星系结构的理解基础上，并对关闭中距离和非常遥远的年轻星系的恒星形成过程有更好的理解。最后，我们将开发必要的软件工具，以消除新的和即将到来的长波干涉仪中的无用信号。

One of the most fundamental questions driving astronomy today is how did the structure that we observe in the Universe arise? Galaxies and the clusters that they form are the largest gravitationally bound systems found in the Universe and so studying their assembly and evolution is key to addressing this question.In this proposal, we focus on this very problem, using a range of observational and theoretical techniques and drawing upon Nottingham's renowned expertise in galaxies and clusters. Our proposed research uses imaging data obtained across the entire electromagnetic spectrum, from radio waves, through the optical, up to X-rays, using both ground-based and space-based facilities for its acquisition. We seek to understand the astrophysics driving the many phenomena seen in the Universe by direct analysis of this data and through its contextualisation and comparison with Nottingham's own state-of-the-art simulations of the Universe's structure formation.The specific aims of our proposed research will tackle a number of pertinent questions. We will use a three-pronged approach to better understand the effects of environment on galaxies, drawing upon ground-based and space-based observations and large-scale simulations of the Universe. We will solve a current problem in our understanding of how the Universe became re-ionized in its early stages by developing new sophisticated models of the first quasars. We will explore a variety of applications of machine learning in astronomy as a solution to coping with vast new forthcoming datasets, including its use in characterising the nature of dark matter by measuring sub-structure in gravitational lensing systems. We will use machine learning to develop a more objective galaxy classification scheme to enable greater insight into their formation mechanisms. Continuing our successful study of the evolution of galaxies throughout the Universe's history, we will build upon our current understanding of their construction from detailed analysis of the stellar populations of nearby galaxies and gain greater comprehension of the processes that shut down star-formation in medium-distance and very distant, young galaxies. Finally, we will develop essential software tools for the removal of unwanted signal in new and forthcoming long-wavelength interferometers.