Galaxies and galaxy clusters are the largest structures in the Universe, and astronomers at the University of Nottingham are using telescopes and supercomputer simulations to figure out how they formed and evolved. This research tackles a fundamental gap in knowledge: we do not yet understand how the Universe’s large-scale structure emerged from the Big Bang. The team will combine observations across the entire electromagnetic spectrum—radio, optical, and X-ray—with their own simulations to study how galaxies change depending on their environment, how the first quasars re-ionised the early Universe, and what shuts down star formation in distant galaxies. They will also develop machine-learning tools to classify galaxies objectively and to measure dark matter substructure in gravitational lensing systems. This is fundamental science with no immediate practical application. It addresses a core question about the origin of cosmic structure. However, the machine-learning techniques and software tools developed to handle vast new datasets—such as signal removal for long-wavelength interferometers—could later find uses in data-intensive fields like medical imaging or communications. Past fundamental astronomy research has led to breakthroughs in everything from Wi-Fi to digital camera sensors.
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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.
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