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We demonstrate the potential of an emulator-based approach to analyzinggalaxy formation models in the domain where constraining data is limited. Wehave applied the open-source Python package PRISM to the galaxy formation modelMeraxes. Meraxes is a semi-analytic model, purposefully built to study thegrowth of galaxies during the Epoch of Reionization (EoR). Constraining suchmodels is however complicated by the scarcity of observational data in the EoR.PRISM's ability to rapidly construct accurate approximations of complexscientific models using minimal data is therefore key to performing thisanalysis well.  This paper provides an overview of our analysis of Meraxes using measurementsof galaxy stellar mass densities; luminosity functions; and color-magnituderelations. We demonstrate the power of using PRISM instead of a full Bayesiananalysis when dealing with highly correlated model parameters and a scarce setof observational data. Our results show that the various observational datasets constrain Meraxes differently and do not necessarily agree with eachother, signifying the importance of using multiple observational data typeswhen constraining such models. Furthermore, we show that PRISM can detect whenmodel parameters are too correlated or cannot be constrained effectively. Weconclude that a mixture of different observational data types, even when theyare scarce or inaccurate, is a priority for understanding galaxy formation andthat emulation frameworks like PRISM can guide the selection of such data.

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