COSMOLOGY THEORY MEETS DATA: MODELLING NON-LINEAR SCALES FOR DARK ENERGY EXPERIMENTS

During the last few years, we have entered the, "golden era", of observational cosmology. The standard cosmological model fits the data extremely well, but requires the existence of two exotic constituents, namely dark energy in the form of a cosmological constant, and cold dark matter. Dark energy currently dominates the Universe and it is responsible for its accelerated expansion.
In the next few years, state-of-the-art cosmological surveys with instruments like the Euclid satellite and the Square Kilometre Array (SKA), are promising to map the large scale structure of the Universe and pin down the nature of dark energy.
This PhD project aims to tackle one of the biggest challenges in theoretical and observational cosmology: how to model accurately and efficiently the non-linear (that is, small scale) behaviour of dark energy models. The student will gain experience in theoretical and observational cosmology, dark energy theory, numerical methods, and will be part of a vibrant international community of scientists. The student will be involved in the Euclid & SKA collaborations.
This project will involve simulating and analysing large datasets, for example large sky optical galaxy catalogues and neutral hydrogen intensity maps. It will also involve high performance computing using HPC facilities (for example in order to perform Markov Chain Monte Carlo analyses). We also plan to utilise machine learning techniques for cosmological parameter estimation.