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A machine learning classification broker for the LSST transient database
Author(s) -
Borne K.D.
Publication year - 2008
Publication title -
astronomische nachrichten
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 63
eISSN - 1521-3994
pISSN - 0004-6337
DOI - 10.1002/asna.200710946
Subject(s) - large synoptic survey telescope , database , computer science , duration (music) , telescope , data science , data mining , information retrieval , astronomy , physics , acoustics
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)