deeplenstronomy: A dataset simulation package for strong gravitational lensing
arXiv:2102.02830 · doi:10.21105/joss.02854
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/ .
Published in the Journal of Open Source Software
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Cited by in corpus (8)
- lenstronomy II: A gravitational lensing software ecosystem
- Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
- DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification
- DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing Data
- Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks
- Deep inference of simulated strong lenses in ground-based surveys
- Core Mass Estimates in Strong Lensing Galaxy Clusters: a Comparison Between Masses Obtained from Detailed Lens Models, Single-Halo Lens Models, and Einstein Radii
- DeepZipper II: Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning