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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.

当今推动天文学发展的最基本问题之一是我们在宇宙中观察到的结构是如何产生的？星系和它们形成的星系团是宇宙中发现的最大的引力束缚系统，因此研究它们的聚集和演化是解决这个问题的关键。在这项提案中，我们将重点关注这个问题，使用一系列观测和理论技术，并借鉴诺丁汉大学在星系和星团方面的著名专业知识。我们提出的研究使用整个电磁频谱的成像数据，从无线电波到光学，再到x射线，使用地面和太空设施进行采集。我们试图通过对这些数据的直接分析，并通过其背景化和与诺丁汉大学自己最先进的宇宙结构形成模拟的比较，来理解驱动宇宙中许多现象的天体物理学。我们所提议的研究的具体目标将涉及一些相关问题。我们将采用三管齐下的方法来更好地理解环境对星系的影响，利用地面和太空的观测以及对宇宙的大规模模拟。我们将通过开发第一批类星体的新复杂模型来解决当前的一个问题，即我们对宇宙在早期阶段如何被再电离的理解。我们将探索机器学习在天文学中的各种应用，作为应对大量即将到来的新数据集的解决方案，包括通过测量引力透镜系统中的子结构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，以便更深入地了解它们的形成机制。继续我们对整个宇宙历史中星系演化的成功研究，我们将通过对附近星系恒星群的详细分析，建立我们目前对它们结构的理解，并对中距离和非常遥远的年轻星系中关闭恒星形成的过程有更深入的了解。最后，我们将开发必要的软件工具，用于在新的和即将到来的长波干涉仪中去除不需要的信号。