Ultra-fast Model Emulation with PRISM: Analyzing the Meraxes Galaxy Formation Model

Abstract

Abstract We demonstrate the potential of an emulator-based approach to analyzing galaxy formation models in the domain where constraining data is limited. We have applied the open-source Python package Prism to the galaxy formation model Meraxes. Meraxes is a semianalytic model, purposely built to study the growth of galaxies during the Epoch of Reionization. Constraining such models is however complicated by the scarcity of observational data in the EoR. Prism’s ability to rapidly construct accurate approximations of complex scientific models using minimal data is therefore key to performing this analysis well. This paper provides an overview of our analysis of Meraxes using measurements of galaxy stellar mass densities, luminosity functions, and color–magnitude relations. We demonstrate the power of using Prism instead of a full Bayesian analysis when dealing with highly correlated model parameters and a scarce set of observational data. Our results show that the various observational data sets constrain Meraxes differently and do not necessarily agree with each other, signifying the importance of using multiple observational data types when constraining such models. Furthermore, we show that Prism can detect when model parameters are too correlated or cannot be constrained effectively. We conclude that a mixture of different observational data types, even when they are scarce or inaccurate, is a priority for understanding galaxy formation and that emulation frameworks such as Prism can guide the selection of such data.