As part of the effort to meet the needs of the Large Synoptic Survey Telescope Dark Energy Science Collaboration (LSST DESC) for accurate, realistically complex mock galaxy catalogs, we have developed GalSampler, an open-source python package that assists in generating large volumes of synthetic cosmological data. The key idea behind GalSampler is to recast hydrodynamical simulations and semi-analytic models as physically-motivated galaxy libraries. GalSampler populates a new, larger-volume halo catalog with galaxies drawn from the baseline library; by using weighted sampling guided by empirical modeling techniques, GalSampler inherits statistical accuracy from the empirical model and physically-motivated complexity from the baseline library. We have recently used GalSampler to produce the cosmoDC2 extragalactic catalog made for the LSST DESC Data Challenge 2. Using cosmoDC2 as a guiding example, we outline how GalSampler can continue to support ongoing and near-future galaxy surveys such as the Dark Energy Survey (DES), the Dark Energy Spectroscopic Instrument (DESI), WFIRST, and Euclid.

中文翻译：

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使用 GalSampler 生成合成宇宙学数据

为了满足大型天气巡天望远镜暗能量科学合作 (LSST DESC) 对准确、真实复杂的模拟星系目录的需求，我们开发了 GalSampler，这是一个开源 Python 包，可帮助生成大量合成宇宙学数据。GalSampler 背后的关键思想是将流体动力学模拟和半解析模型重铸为物理驱动的星系库。GalSampler 使用从基线库中提取的星系填充新的、更大容量的光环目录；通过使用由经验建模技术指导的加权采样，GalSampler 继承了经验模型的统计准确性和基线库的物理驱动复杂性。