The LSST Science Pipelines enable optical and near-infrared astronomy in the big data era. We are building the Science Pipelines for the Large Synoptic Survey Telescope (LSST), but our command line task and Python API can be extended for any optical or near-infrared dataset.
The latest release is : learn what's new <releases/index>.
If you're new to the LSST Science Pipelines, these tutorials will get you up and running with step-by-step data processing tutorials.
- Data processing tutorial series: Part 1
Data repositories <getting-started/data-setup>· Part 2Single frame processing <getting-started/processccd>· Part 3Image and catalog display <getting-started/display>· Part 4Image coaddition <getting-started/coaddition>· Part 5Source measurement <getting-started/photometry>· Part 6Multi-band catalog analysis <getting-started/multiband-analysis>.
Join us on the LSST Community forum to get help and share ideas.
getting-started/index
Recommended installation path:
Installing with newinstall.sh <install/newinstall>install/setupinstall/top-level-packages
Alternative distributions and installation methods:
install/dockerInstalling from source with lsstsw <install/lsstsw>- CernVM FS (contributed by CC-IN2P3)
Related topics:
Configuring Git LFS for data packages <install/git-lfs>install/package-development
To install the LSST Simulation software, such as MAF, please follow the LSST Simulations documentation.
install/index
releases/notesknown-issuesmetrics
releases/index known-issues metrics
- Join us on the LSST Community forum, community.lsst.org.
- Fork our code on GitHub at https://github.com/lsst.
- Report issues in JIRA.
- API documentation is currently published with Doxygen.
- DM Developer guidance is at https://developer.lsst.io.
- Learn more about LSST Data Management by visiting http://lsst.org/about/dm.
- Contribute to our documentation. This guide is on GitHub at lsst/pipelines_lsst_io.