Using neural networks to learn how dark matter and dark energy affect structure formation in the Universe

Supervisors: Ian G. McCarthy (ARI, LJMU), Ian H. Jarman (CSM, LJMU)
Internship: Naimuri

The field of large-scale structure cosmology is presently on the cusp of a revolution, with a large number of imminent wide-field surveys (particularly LSST and Euclid) poised to make unprecedentedly precise measurements of the distribution of matter in the Universe in order to constrain the nature of dark matter and dark energy.

To achieve these aims, these surveys will rely heavily on cosmological simulations to predict what the Universe should look like for different dark sector scenarios. However, cosmological simulations are very computationally expensive, particularly when non-gravitational (“baryonic”) interactions are incorporated. This prevents theoretical astrophysicists from systematically exploring the full parameter space associated with both baryonic and cosmological processes. 

In order to address these issues, we will use neural networks to: i) develop models for mapping the relations between less expensive gravity-only simulations and full cosmological hydrodynamical simulations including baryons; and ii) link the outcome of simulations (e.g., weak lensing mass maps) and the input parameters specifying the initial conditions and the nature of dark matter and dark energy. 

The neural networks will be trained and tested on a grid of simulations carried out on the LJMU HPC facility, Prospero. The derived models will be used to produce accurate predictions for LSST and Euclid.