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.
