Student: Davide Piras
Current and forthcoming galaxy surveys will provide unprecedentedly tight constraints on physics beyond the cosmological standard model. Just as important as the best-fit parameters obtained from these datasets are the statistical uncertainties associated with them. The measurement errors are usually determined from large suites of mock survey realisations, each built upon computationally expensive cosmological N-body simulations. 10,000s of such simulations will be required in the near future, but it is unlikely sufficient super-computer time will be available. This is a limiting factor in cosmological analyses of current surveys, and a critical unsolved problem for the next generation of surveys such as Euclid and LSST. In this project we will develop a deep-learning network that effectively compresses the input simulation information and subsequently generates statistically independent mock universes with characteristics identical to the input, thus enabling the calculation of realistic error models with minimal extra computational cost from just a few N-body simulations.