The Blending ToolKit: A simulation framework for evaluation of galaxy detection and deblending

TL;DR

The paper introduces the Blending ToolKit, an open-source Python framework for simulating, evaluating, and comparing galaxy detection and deblending algorithms in cosmological surveys, facilitating systematic analysis of blending effects.

Contribution

It provides a modular, flexible simulation and evaluation framework for galaxy deblending, integrating existing tools and metrics to advance research in survey data analysis.

Findings

Enables fast simulation of blended galaxy images.

Standardizes inputs and outputs for deblending algorithms.

Provides comprehensive metrics for algorithm evaluation.

Abstract

We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons of detection and deblending algorithms based on a suite of metrics. The package, named Blending Toolkit (BTK), serves as a modular, flexible, easy-to-install, and simple-to-use interface for exploring and analyzing systematic effects related to blended galaxies in cosmological surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST). BTK has three main components: (1) a set of modules that perform fast image simulations of blended galaxies, using the open source image simulation package GalSim; (2) a module that standardizes the inputs and outputs of existing deblending algorithms; (3) a library of deblending metrics commonly defined in the galaxy deblending literature. In combination, these modules allow researchers to explore the impacts of galaxy blending in cosmological surveys. Additionally, BTK provides researchers who are developing a new deblending algorithm a framework to evaluate algorithm performance and make principled comparisons with existing deblenders. BTK includes a suite of tutorials and comprehensive documentation. The source code is publicly available on GitHub at https://github.com/LSSTDESC/BlendingToolKit.