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deeplenstronomy: A dataset simulation package for strong gravitational lensing

Journal Article · · Journal of Open Source Software
DOI:https://doi.org/10.21105/joss.02854· OSTI ID:1764837
 [1];  [2];  [3];  [4];  [5]
  1. Univ. of Wisconsin, Madison, WI (United States)
  2. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Univ. of Chicago, IL (United States)
  3. Stanford Univ., CA (United States)
  4. Univ. of Illinois at Urbana-Champaign, IL (United States)
  5. Univ. of Chicago, IL (United States)
+ Show Author Affiliations

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/ .

Research Organization:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25); National Science Foundation (NSF)
Grant/Contract Number:
AC02-07CH11359
OSTI ID:
1764837
Report Number(s):
FERMILAB-PUB--21-039-SCD; oai:inspirehep.net:1845159
Journal Information:
Journal of Open Source Software, Journal Name: Journal of Open Source Software Journal Issue: 58 Vol. 6; ISSN 2475-9066
Publisher:
Open Source Initiative - NumFOCUSCopyright Statement
Country of Publication:
United States
Language:
English

References (11)

jiwoncpark/baobab: v0.1.2 software January 2021
LSSTDESC/SLSprinkler: LSST DESC Strong Lensing Sprinkler for Simulations software January 2020
lenstronomy: Multi-purpose gravitational lens modelling software package journal December 2018
Array programming with NumPy journal September 2020
SciPy 1.0: fundamental algorithms for scientific computing in Python journal February 2020
Astropy: A community Python package for astronomy journal September 2013
The Sloan Digital Sky Survey: Technical Summary journal September 2000
Dark Energy Survey year 1 results: Cosmological constraints from galaxy clustering and weak lensing journal August 2018
Matplotlib: A 2D Graphics Environment journal January 2007
Data Structures for Statistical Computing in Python conference January 2010
LSST: From Science Drivers to Reference Design and Anticipated Data Products journal March 2019

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