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

诺丁汉大学天文学和天体物理学 - 2023 至 2026

基本信息

批准号：
ST/X000982/1
负责人：
Simon Dye
金额：
$ 263.34万
依托单位：
University of Nottingham
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=ST%2FX000982%2F1
关键词：
Astronomy Astrophysics University Nottingham 2023

项目摘要

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.

今天推动天文学的最根本问题之一是我们在宇宙中观察到的结构是如何产生的？星系及其形成的群集是宇宙中发现的最大的重力系统，因此研究它们的组装和进化是解决这个问题的关键。在这项建议中，我们将重点放在这个非常问题上，使用一系列观察性和理论技术，并借鉴了诺丁汉在星系和集团中享有诺丁汉的专业知识。我们提出的研究使用整个电磁频谱中获得的成像数据，从无线电波到光学到X射线，最高X射线，同时使用基于地面的设施和基于空间的设施进行获取。我们试图通过直接分析该数据以及与诺丁汉自身对宇宙结构形成的最先进的模拟来理解推动宇宙中许多现象的天体物理学。我们拟议的研究的具体目的将解决许多相关问题。我们将使用三方面的方法更好地理解环境对星系的影响，借鉴了基于地面和空间的观测以及对宇宙的大规模模拟。我们将通过开发新的第一个类星体的新复杂模型来理解宇宙如何在早期阶段重新离子的当前问题。我们将探讨机器学习在天文学中的各种应用，以解决应对大量新的即将到来的数据集的解决方案，包括通过测量引力透镜系统中的亚构造来表征暗物质的性质。我们将使用机器学习来开发一个更客观的星系分类方案，以使其更深入地了解其形成机制。继续我们成功研究星系在整个宇宙历史上的演变，我们将从对附近星系的恒星种群的详细分析中进行当前对它们的构建的理解，并对以中等距离和非常遥远的年轻星系的恒星形成的过程有更深入的理解。最后，我们将开发基本的软件工具，以在新的和即将到来的长波长干涉仪中删除不需要的信号。