Constraining the Complex Relationship Between Galaxies and their Dark Matter Haloes with Machine Learning

The project will utilize a class of machine learning algorithm known as sparse regression methods (SRM) to extract robust relationships between the diverse properties of galaxies, their gaseous environments and their host dark matter haloes, formed in cosmological hydrodynamical simulations of the galaxy population.

A vast quantity of galaxy parameter relationships may have the potential to be predicted by the simulations (such as formation time, spin, merger history), but are difficult to quantify through direct means.

This is where the benefit of SRM is presented clearly, as it is a method that (unlike other machine learning techniques) discards unneeded free parameters and efficiently extracts the "governing" equations of physical systems from state descriptions of the system alone, without a need for detailed prior understanding of the relevant physics.

This would also allow for galaxy populations to be "painted" onto dark matter-only simulations, which are relatively inexpensive to generate compared to full baryonic simulations through a process known as halo modelling.