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Towards an automated classification of transient events in synoptic sky surveys

Show simple item record Djorgovski, S. G. Donalek, C. Mahabal, A. A. Moghaddam, B. Turmon, M. Graham, M. J. Drake, A. J. Sharma, N. Chen, Y. 2013-09-03T14:37:16Z 2013-09-03T14:37:16Z 2011-10-20
dc.identifier.citation NASA Conference on Intelligent Data Understanding, Mountain View, California, October 20, 2011. en_US
dc.identifier.clearanceno 11-4519
dc.description.abstract We describe the development of a system for an automated, iterative, real-time classification of transient events discovered in synoptic sky surveys. The system under development incorporates a number of Machine learning techniques, mostly using Bayesian approaches, due to the sparse nature, heterogeniety, and variable incompleteness of the available data. The classifications are improved iteratively as the new measurements are obtained. One novel featrue is the development of an autmoated follow-up recommendation engine, that suggest those measruements that would be the most advantageous in terms of resolving classification ambiguitites and/or characterication of the astrophyscally most interesting objects, given a set of available follow-up assets and their cost funcations. This illustrates the symbiotic relatioship of astronomy and applied computer science through the emerging disciplne of AstroInformatics en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2011. en_US
dc.subject classification en_US
dc.subject transients en_US
dc.subject astronomy en_US
dc.subject machine learning en_US
dc.title Towards an automated classification of transient events in synoptic sky surveys en_US
dc.type Preprint en_US

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