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Original Paper
A machine learning classification broker for the LSST transient database
Article first published online: 4 MAR 2008
DOI: 10.1002/asna.200710946
Copyright © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
1521-3994/asset/cover.gif?v=1&s=de26f4d4fd0154fbfdfa037914035a3b196547e9 "Astronomische Nachrichten")
Additional Information
How to Cite
Borne, K.D. (2008), A machine learning classification broker for the LSST transient database. Astron. Nachr., 329: 255–258. doi: 10.1002/asna.200710946
Publication History
- Issue published online: 4 MAR 2008
- Article first published online: 4 MAR 2008
- Manuscript Accepted: 30 NOV 2007
- Manuscript Received: 3 SEP 2007
Funded by
- National Science Foundation. Grant Numbers: AST-0551161, AST-0132798
- Abstract
- References
- Cited By
Keywords:
- astronomical databases: miscellaneous;
- methods: data analysis;
- methods: statistical
Abstract
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. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)