Ultra-fast model emulation with PRISM; analyzing the Meraxes galaxy formation model

TL;DR

This paper demonstrates how the PRISM emulator enables rapid and accurate analysis of the Meraxes galaxy formation model during the Epoch of Reionization, especially with limited observational data, improving model constraints.

Contribution

It introduces the application of PRISM to efficiently emulate the Meraxes model, highlighting its effectiveness in analyzing scarce data and identifying parameter correlations.

Findings

PRISM provides fast, accurate emulation of Meraxes.

Different observational data constrain the model differently.

PRISM detects parameter correlations and constraints.

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 semi-analytic model, purposefully built to study the growth of galaxies during the Epoch of Reionization (EoR). 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 like PRISM can guide the selection of such data.