TL;DR
GLACiAR is an open-source Python tool designed to simulate galaxy observations, estimate survey completeness, and aid in analyzing high-redshift galaxy surveys, improving accuracy in galaxy formation studies.
Contribution
The paper introduces GLACiAR, a versatile software for modeling galaxy detection completeness and selection functions, tailored for high-redshift galaxy surveys, with validation on HST data.
Findings
GLACiAR effectively estimates completeness and redshift selection functions.
Application to HST BoRG data demonstrates consistency with previous analyses.
Different modeling assumptions can significantly impact completeness estimates.
Abstract
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…
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