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/ .
@article{arxiv.2102.02830,
title = {deeplenstronomy: A dataset simulation package for strong gravitational lensing},
author = {Robert Morgan and Brian Nord and Simon Birrer and Joshua Yao-Yu Lin and Jason Poh},
journal= {arXiv preprint arXiv:2102.02830},
year = {2021}
}