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dc.contributor.advisorStrolger, Louis-Gregory
dc.contributor.authorShahady, Kristin
dc.date2015-04
dc.date.accessioned2015-09-08T18:39:05Z
dc.date.available2015-09-08T18:39:05Z
dc.identifier.citationShahady, K. (2015, April). Locating Supernovae via Artificial Neural Networks. Poster presented at the Northrop Grumman Engineering & Science Student Design Showcase, Florida Institute of Technology, Melbourne, FL.en_US
dc.identifier.urihttp://hdl.handle.net/11141/728
dc.description.abstractThe rate at which supernova occur at large distances with high redshifts is hard to obtain. New data collection would require several hundred orbits on the Hubble Space Telescope (HST). However, there are enough HST images of sufficiently deep, extragalactic fields available in archives and the only challenge is locating and identifying the supernovae within them to add the statistical rate analysis. There is a wealth of information on the appearance of high redshift events in relation to their host galaxies that can be used to train artificial neural networks (ANNs) to identify unique magnitude, color, and separation parameter spacesen_US
dc.language.isoen_USen_US
dc.titleLocating Supernovae via Artificial Neural Networksen_US
dc.typeposteren_US


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