Other
Scientific paper
Jan 2012
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012aas...21915603s&link_type=abstract
American Astronomical Society, AAS Meeting #219, #156.03
Other
Scientific paper
The LSST will produce the richest sets of astronomical data ever created, which will open up an unparalleled temporal discovery space. The data products will include deep imaging of half of the sky in 6 passbands; catalogs of all detected sources including stars, galaxies, solar system objects; lightcurves of variable objects; and alerts of transient sources that will be generated within a minute of their detection. The LSST Project is currently prototyping a scalable Data Management System (DMS) capable of processing, archiving, and serving these data to the astronomical community. We anticipate that individual investigators and research teams will, during the course of their analysis, generate scientific datasets using data products from LSST (possibly combined with data from other resources) that will be of great value to the LSST community. The LSST Project plans to support these community-based science activities by: providing direct compute and storage resources, use of portions of the LSST software stack, and the development of user and programmatic interfaces that enable the discovery, exploration, and analysis of LSST data products.
In addition to science data products, a number of data products will be generated to assess science quality. Although science data quality assessment will be highly automated, even the limited human interaction required to assess and diagnose problems drives the need to prototype user interfaces that enable efficient data exploration and analysis. Much of this capability is also needed for generating and evaluating calibration products, documenting survey progress, supporting science analysis for users, etc. In this presentation we describe the data products, pipeline processing, and user interface prototypes that have been developed so far to explore algorithms, validate LSST image simulations, and assess output data quality.
Axelrod Tim
Becker Andrew C.
Bickerton Steven
Data Management LSST
Juric Mario
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