GLACiAR, an open-source python tool for simulations of source recovery and completeness in galaxy surveys

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.