deeplenstronomy: A dataset simulation package for strong gravitational lensing

TL;DR

deeplenstronomy is an open-source Python package designed to generate large-scale, reproducible simulated images of astronomical systems, aiding machine learning training for strong gravitational lensing detection.

Contribution

It introduces a comprehensive, efficient simulation toolkit specifically tailored for creating training datasets for gravitational lensing studies.

Findings

Enables large-scale simulation of astronomical images

Supports reproducibility and efficiency in dataset generation

Facilitates machine learning applications in lensing detection

Abstract

Automated searches for strong gravitational lensing in optical imaging survey datasets often employ machine learning and deep learning approaches. These techniques require more example systems to train the algorithms than have presently been discovered, which creates a need for simulated images as training dataset supplements. This work introduces and summarizes deeplenstronomy, an open-source Python package that enables efficient, large-scale, and reproducible simulation of images of astronomical systems. A full suite of unit tests, documentation, and example notebooks are available at https://deepskies.github.io/deeplenstronomy/ .