Constraining the complex relationship between galaxies and their dark matter haloes with machine learning

Student: Ryan Roberts
Supervisors: Rob Crain (ARI, LJMU), Ivan Baldry (ARI, LJMU), Paul Bell (CSM, LJMU)
Institution: LJMU

Detailed numerical simulations of the formation of cosmic large-scale dark matter structure show that the spatial clustering of dark matter haloes (the sites where galaxies form) depends primarily on their mass, but also significantly on secondary properties such as their assembly history or local environment. New state-of-the-art gas-dynamical numerical simulations indicate that these secondary properties also have an important influence on the properties of the galaxies forming within. However, owing to the diversity and complexity of the physics underpinning galaxy evolution, a consensus view of precisely how the assembly history and environment of dark matter haloes influences the evolution of galaxies has yet to emerge.

Machine learning (ML) presents an exciting opportunity to disentangle the complex relationships between the diverse present-day populations of galaxies and the dark matter haloes. Whilst ML has seen widespread adoption in astrophysics as a means to predict observable galaxy properties based on halo catalogues drawn from detailed, but physically “simple” dark matter-only simulations, it can also be used in the reverse sense: to predict halo properties based on galaxy properties. Here, we aim to train ML models using state-of-the-art gas-dynamical simulations (e.g. EAGLE-XL, IllustrisTNG-300) to learn the complex relationships between galaxies and haloes that each simulation predicts.

The models will then be used to create mock galaxy catalogues that will be confronted with the rich, multidimensional data yielded by the ongoing and forthcoming observational “megasurveys” (spectroscopic, e.g. WAVES, DESI, and deep/detailed imaging, e.g. LSST, Euclid), which are set to revolutionise extragalactic astrophysics over the coming decade, enabling the discrimination between leading models and placing unprecedentedly stringent constraints on galaxy formation theory.