deeplenstronomy: A dataset simulation package for strong gravitational lensing

Autor:	Morgan, Robert, Nord, Brian, Birrer, Simon, Lin, Joshua Yao-Yu, Poh, Jason
Rok vydání:	2021
Předmět:	Astrophysics - Instrumentation and Methods for Astrophysics Astrophysics - Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology
Zdroj:	Journal of Open Source Software, 6(58), 2854 (2021)
Druh dokumentu:	Working Paper
DOI:	10.21105/joss.02854
Popis:	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/ . Comment: Published in the Journal of Open Source Software
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2102.02830 Zobrazit plný text záznamu View this record from Arxiv