Emulating the Universe: a simultaneous exploration of cosmology and “gastrophysics”
Supervisors: Ian McCarthy (LJMU), Andreea Font (LJMU), Tianyi Yang (LJMU)
Institution: Liverpool John Moores University
For the past 25 years, the standard model of cosmology has been remarkably successful in describing a wealth of astronomical observations of our Universe, including the detailed statistical properties of the cosmic microwave background, the abundance of the light elements, the rotation curves and gravitational lensing signals of galaxies and clusters of galaxies, and the accelerating expansion of the
Universe inferred from supernovae. However, the past few years has seen the advent of large multiwavelength surveys of the large-scale distribution of matter (large-scale structure, LSS) that have led to increasingly precise and independent constraints on the cosmological parameters and the first possible signs of cracks in the standard model have now begun to appear.
The most well-known of these are the so-called Hubble and S8 tensions, reflecting differences in the observed and predicted local expansion rate of space and ‘clumpiness’ of the matter distributions, respectively. In addition, surprising new results from the Dark Energy Survey Instrument appear to favour an evolving dark energy scenario (rather than a cosmological constant) and a summed mass of
neutrinos which is mild conflict with laboratory oscillation measurements.
The robustness of these conclusions, and our ability to test the standard model generally, hinges crucially on our ability to accurately predict the properties of LSS. During this project, you will help develop state-of-the-art cosmological simulations of large-scale structure. These simulations, which will be run on national high-performance computing (HPC) facilities, will explore the standard model
of cosmology, but also exciting extensionsincluding models of dynamical dark energy. The simulations will incorporate full hydrodynamics and will also explore astrophysical sources of “feedback”, including exploding supernovae stars and accreting super massive black holes. In addition to the development of new simulations, you will help to develop novel machine learning models trained on
the simulations to quickly and accurately predict various statistical measures of LSS, which will be confronted with state-of-the-art astronomical observations.
As a student, you will work closely with researchers in the Astrophysics Research Institute (ARI) andbenefit from collaborative visits to various institutions in the UK and EU. Your work will provide astronomers with a powerful tool for handling the vast quantities of data generated by new telescopes and enable more reliable scientific discoveries. The skills you acquire, particularly in HPC usage and
machine learning, large-scale data processing, and cross-domain applications, will be highly transferable to industry.
Throughout the project, you will have access to the ARI’s postgraduate training program and datascience training provided by the Centre for Doctoral Training LIV.INNO. You’ll also gain specialised skills through Nvidia DLI training, which will help you make the most of high-performance computing resources at Liverpool John Moores University. Additionally, a six-month industry placement will give
you the opportunity to broaden your research experience and career skills, making this an exceptional opportunity to contribute to both astrophysics and the broader field of machine learning.
This project will be carried out over 48 months and is fully funded (tuition fees + stipend set by UKRI guidelines + a research/training budget), inclusive of the 6-month industry placement. We encourage applications from underrepresented groups to help foster diversity, inclusivity, and equity within our research community. Applications are welcomed from those with backgrounds in astrophysics, physics, and computer science.
The delivery of this project is subject to funding approval. Information on how to apply will follow shortly.