Rubin Observatory LSST Tutorials

RESOURCE

Abstract

A collection of tutorials -- both Jupyter notebooks and documentation-based --- demonstrating data access, analysis, and visualization techniques for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). These tutorials are intended for astronomers, educators, and data scientists working with LSST data products.
Release Date:
2021-07-29
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Programming Languages:
Python
Licenses:
Apache License 2.0
Sponsoring Org.:
Code ID:
162015
Research Org.:
National Optical-Infrared Astronomy Research Laboratory (NOIRLab)
SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
NSF-DOE Vera C. Rubin Observatory
Country of Origin:
United States

RESOURCE

Citation Formats

Vera C. Rubin Observatory, NSF-DOE. Rubin Observatory LSST Tutorials. Computer Software. https://github.com/lsst/tutorial-notebooks. U.S. National Science Foundation, U.S. National Science Foundation, U.S. National Science Foundation, U.S. National Science Foundation, U.S. National Science Foundation, USDOE Office of Science (SC), High Energy Physics (HEP). 29 Jul. 2021. Web. doi:10.11578/rubin/dc.20250909.20.
Vera C. Rubin Observatory, NSF-DOE. (2021, July 29). Rubin Observatory LSST Tutorials. [Computer software]. https://github.com/lsst/tutorial-notebooks. https://doi.org/10.11578/rubin/dc.20250909.20.
Vera C. Rubin Observatory, NSF-DOE. "Rubin Observatory LSST Tutorials." Computer software. July 29, 2021. https://github.com/lsst/tutorial-notebooks. https://doi.org/10.11578/rubin/dc.20250909.20.
@misc{ doecode_162015,
title = {Rubin Observatory LSST Tutorials},
author = {Vera C. Rubin Observatory, NSF-DOE},
abstractNote = {A collection of tutorials -- both Jupyter notebooks and documentation-based --- demonstrating data access, analysis, and visualization techniques for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). These tutorials are intended for astronomers, educators, and data scientists working with LSST data products.},
doi = {10.11578/rubin/dc.20250909.20},
url = {https://doi.org/10.11578/rubin/dc.20250909.20},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/rubin/dc.20250909.20}},
year = {2021},
month = {jul}
}