$\mathtt{ComEst}$: a Completeness Estimator of Source Extraction on Astronomical Imaging

TL;DR

ComEst is a Python software tool that accurately estimates the detection completeness and purity of sources in astronomical CCD images by simulating and detecting synthetic sources directly on observed images.

Contribution

It provides an end-to-end completeness estimator tailored for SExtractor on FITS images, incorporating observational artifacts and noise properties for more accurate assessments.

Findings

ComEst's completeness estimates agree with traditional SNR-based methods.

It effectively captures observational artifacts affecting source detection.

The tool is publicly available and easy to integrate into astronomical data analysis workflows.

Abstract

The completeness of source detection is critical for analyzing the photometric and spatial properties of the population of interest observed by astronomical imaging. We present a software package $\mathtt{ComEst}$, which calculates the completeness of source detection on charge-coupled device (CCD) images of astronomical observations, especially for the optical and near-infrared (NIR) imaging of galaxies and point sources. The completeness estimator $\mathtt{ComEst}$ is designed for the source finder $\mathtt{SExtractor}$ used on the CCD images saved in the Flexible Image Transport System (FITS) format. Specifically, $\mathtt{ComEst}$ estimates the completeness of the source detection by deriving the detection rate of synthetic point sources and galaxies simulated on the observed CCD images. In order to capture any observational artifacts or noise properties while deriving the completeness, $\mathtt{ComEst}$ directly carries out the detection of simulated sources on the observed images. Given an observed CCD image saved in FITS format, $\mathtt{ComEst}$ derives the completeness of the source detection from end to end as a function of source flux (or magnitude) and CCD position. In addition, $\mathtt{ComEst}$ can also estimate the purity of the source detection by comparing the catalog of the detected sources to the input catalogs of the simulated sources. We run ComEst on the images from Blanco Cosmology Survey (BCS) and compare the derived completeness as a function of magnitude to the limiting magnitudes derived by using the Signal-to-Noise ratio (SNR) and number count histogram of the detected sources. $\mathtt{ComEst}$ is released as a Python package with an easy-to-use syntax and is publicly available at https://github.com/inonchiu/ComEst