标题： 模拟星系目录的自动校准用于宇宙学分析 显示英文标题

标题： Automated calibration of simulated galaxy catalogues for cosmological analyses

摘要： 模拟星系目录已成为准备和利用星系巡天观测的重要工具。 它们是建模分析中存在系统不确定性的关键组成部分。 然而，为了达到星系巡天所需的大量体积和高精度，我们通常根据某些理论方法将星系填充到暗物质晕中。 这些方法包含自由参数，这些参数通过将模拟与观测进行比较来校准，但星系模拟的创建是一个随机过程，具有大量需要校准的自由参数。 我们提出了一种新的流程，基于差分进化算法，可以在具有大参数空间的真实场景中全自动地校准星系模拟。 我们将该流程应用于结合晕占有率分布和子晕丰度匹配技术构建的星系模拟。 我们表明，我们的流程可以正确地将星系模拟与观测进行校准，适用于$\Lambda$CDM 和修改引力晕目录。 这种校准流程为自动化未来大规模星系模拟的校准提供了一个新工具。 显示英文摘要

摘要： Simulated galaxy catalogues have become an essential tool for preparing and exploiting observations from galaxy surveys. They constitute a key ingredient in modelling the systematic uncertainties present in the analysis. However, in order to reach the large volume and high precision required for galaxy surveys, we generally populate dark matter haloes with galaxies following certain theoretical recipes. Such recipes contain free parameters that are calibrated comparing the simulations against observations, but the creation of galaxy mocks is a stochastic process with a large number of free parameters to calibrate. We present a new pipeline, based on the differential evolution algorithm, that can calibrate galaxy mocks in a fully automated way for realistic scenarios with a large parameter space. We apply the pipeline to galaxy mocks built on a combination of halo occupation distribution and sub-halo abundance matching techniques. We show that our pipeline can properly calibrate the galaxy mocks against observations for both $\Lambda$CDM and modified gravity halo catalogues. This type of calibration pipeline provides a new tool for automating the calibration of future massive galaxy mocks.