GLACiAR，一个开源Python工具，用于模拟星系调查中的源恢复和完整性
GLACiAR, an open-source python tool for simulations of source recovery and completeness in galaxy surveys
摘要：
光度函数是描述星系形成和演化的基本观测量。从巡天中的计数得到这种测量的一个关键因素是观测的完备性和红移选择函数的特征。在本文中，我们介绍了GLACiAR，GitHub上提供的一个开放的Python工具，用于估计星系调查中的完整性和选择函数。该代码是专为多波段成像调查，旨在寻找高红移星系通过莱曼突破技术，但它可以广泛应用。该代码生成遵循具有不同索引和可定制大小，红移和光谱能量分布特性的Sersic配置文件的人造星系，将它们添加到输入图像中，并测量恢复率。为了说明这个新的软件工具，我们应用它来量化的完整性和红移选择功能的J-辍学源（红移z~10星系）在哈勃空间望远镜Brilliance的Reionizing Galerkin巡天（BoRG）。我们与以前对同一数据集的完整性分析的比较显示了总体一致性，但也强调了人工源的不同建模假设如何影响完整性估计。
英文摘要：
The luminosity function is a fundamental observable for characterizing how galaxies form and evolve throughout the cosmic history. One key ingredient to derive this measurement from the number counts in a survey is the characterization of the completeness and redshift selection functions for the observations. In this paper we present GLACiAR, an open python tool available on GitHub to estimate the completeness and selection functions in galaxy surveys. The code is tailored for multiband imaging surveys aimed at searching for high-redshift galaxies through the Lyman Break technique, but it can be applied broadly. The code generates artificial galaxies that follow Sersic profiles with different indexes and with customizable size, redshift and spectral energy distribution properties, adds them to input images, and measures the recovery rate. To illustrate this new software tool, we apply it to quantify the completeness and redshift selection functions for J-dropouts sources (redshift z~10 galaxies) in the Hubble Space Telescope Brightest of Reionizing Galaxies Survey (BoRG). Our comparison with a previous completeness analysis on the same dataset shows overall agreement, but also highlights how different modelling assumptions for artificial sources can impact completeness estimates.
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