Rubin Science Platform documentation

DP1 Tutorials

Tutorials for exploring the DP1 dataset on the Rubin Science Platform.

Data Preview 0.3 (DP0.3)

DP0.3 provides a catalog of solar system objects from a simulated LSST ten-year wide-fast-deep survey by the Solar System Science Collaboration.

DP0.3 Tutorials

Tutorials for exploring the DP0.3 solar system object dataset on the Rubin Science Platform.

DP0.3 Catalog Schema

Schema reference for the DP0.3 catalog dataset available through the Table Access Protocol (TAP) service.

Data Preview 0.2 (DP0.2)

DP0.2 is the second phase of the Data Preview 0 program using simulated images from the DESC DC2 data challenge processed with version 23.0 of the LSST Science Pipelines.

DP0.2 Tutorials

Tutorials for exploring the DP0.2 dataset on the Rubin Science Platform.

DP0.2 Catalog Schema

Schema reference for the DP0.2 catalog dataset available through the Table Access Protocol (TAP) service.

Tables of counterpart associations between Gaia DR3 and CatWISE2020. Uses probabilistic cross-match algorithms of macauff, as described by Wilson & Naylor (MNRAS, 2017, 2018a,b) and Wilson (RNAAS, 2022). Sources are returned either as a pairing, in which the Gaia and WISE entries are the same astrophysical source detected twice, or as non-matches, with that particular object in one of the catalogues having a corresponding flux upper limits in the opposing catalogue. Join tables are supplemented include various pieces of metadata such as the probability of the match/non-match, likelihood of match on purely position or brightness grounds, and information on the level to which objects suffer contamination due to hidden and unresolved background sources. Two sets of cross-match join tables are provided: one that includes the relative likelihoods of match association and non-match coincidence due to the photometric brightnesses of the objects in the two datasets, and hence source colour, and one that does not consider colour and solely uses the relative positions, astrometric uncertainties, and on-sky catalogue densities to determine the most likely match pairings. These are referred to as "astro_only" -- the without-photometry, astrometric-information-only match -- and "astro_photo" -- with-photometry, astrometric-and-photometric match -- respectively.

The VISTA-HSC fused dataset is designed to deliver pixel-matched images and multiband catalogues through the integration of data from the Visible and Infrared Survey Telescope for Astronomy (VISTA) in the near-infrared and the Hyper SuprimeCam (HSC) in the optical range. The LSST pipelines will handle the CCD processing of VISTA images, while the calibrated exposures from the final HSC PDR3 will be employed for merging with VISTA. This comprehensive dataset encompasses multiple VISTA surveys, with varying depths.

VISTA-HSC Tutorials

Tutorials for exploring the VISTA-HSC dataset on the Rubin Science Platform.

This dataset, available via the Notebooks service, is produced with the LSST–VISTA Fusion Pipeline for the DP1 ComCam–VISTA data in the Extended Chandra Deep Field South (ECDFS). The pipeline processes VISTA near-infrared imaging within an LSST-style framework and combines it with ComCam optical data in a common Butler environment for joint analysis. The main products are pixel-matched Coadd images and associated catalogue products across the available optical and near-infrared bands. These fused products enable consistent photometric analysis across wavelengths, making them particularly well suited for colour studies, SED-based analysis, and any science that benefits from uniform measurements over all bands. Tutorials are provided thought the Tutorials menu.

ComCam-VISTA Tutorials

Tutorials for exploring the ComCam-VISTA dataset on the Rubin Science Platform.

Phalanx

The configuration repository for Rubin's Kubernetes deployments.

Rubin Science Platform

The Notebook aspect is a powerful data analysis environment with Jupyter Notebooks and terminals in the browser. Documentation for the Rubin Science Platform, including account set up, portal, notebooks, and API aspects.

Portal

The Portal enables you to explore LSST image and table data in your browser.

LSST Science Pipelines

The Science Pipelines include the Butler for accessing LSST data and a pipeline framework for processing data. The LSST Science Pipelines Python package is preinstalled in the Notebook aspect.