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.

我们展示了基于模拟器的方法在约束数据有限的领域中分析星系形成模型的潜力。我们已将开源 P ython包 P rism应用于星系形成模型 M eraxes。中号eraxes是半解析模型，特意建立再电离的时代中，研究星系的生长。然而，由于 EoR 中观测数据的稀缺性，限制此类模型变得复杂。因此，P rism能够使用最少的数据快速构建复杂科学模型的准确近似值，这是很好地执行此分析的关键。本文概述了我们对 M eraxes 的分析使用星系恒星质量密度、光度函数和颜色-星等关系的测量。我们展示了在处理高度相关的模型参数和一组稀缺的观察数据时使用 P rism而不是完整的贝叶斯分析的能力。我们的结果表明，各种观测数据集对 M eraxes 的约束不同，并且不一定彼此一致，这表明在约束此类模型时使用多种观测数据类型的重要性。此外，我们证明了 P rism可以检测模型参数何时过于相关或无法有效约束。我们得出的结论是，不同观测数据类型的混合，即使它们稀缺或不准确，也是理解星系形成的优先事项，并且 P rism等仿真框架可以指导此类数据的选择。