A machine learning classification broker for the LSST transient database

Details A machine learning classification broker for the LSST transient database A machine learning classification broker for the LSST transient database

Mar 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008an....329..255b&link_type=abstract

Astronomische Nachrichten, Vol.329, Issue 3, p.255

Computer Science

Databases

5

Astronomical Databases: Miscellaneous, Methods: Data Analysis, Methods: Statistical

Scientific paper

We describe the largest data-producing astronomy project in the coming decade - the LSST (Large Synoptic Survey Telescope). The enormous data output, database contents, knowledge discovery, and community science expected from this project will impose massive data challenges on the astronomical research community. One of these challenge areas is the rapid machine learning, data mining, and classification of all novel astronomical events from each 3-gigapixel (6-GB) image obtained every 20 seconds throughout every night for the project duration of 10 years. We describe these challenges and a particular implementation of a classification broker for this data fire hose.

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