TITLE: deeplenstronomy: A dataset simulation package for strong gravitational lensing 

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/ .
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/ .
deeplenstronomy: A dataset simulation package for strong gravitational lensing
http://arxiv.org/abs/2102.02830v1