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dc.contributor.advisorCaballero-Nieves, Saida
dc.contributor.authorJohnston, Kyle Burton 2019
dc.descriptionThesis (Ph.D.) - Florida Institute of Technology, 2019.en_US
dc.description.abstractThis project outlines the complete development of a variable star classification algorithm methodology. With the advent of Big-Data in astronomy, professional astronomers are left with the problem of how to manage large amounts of data, and how this deluge of information can be studied in order to improve our understanding of the universe. While our focus will be on the development of machine learning methodologies for the identification of variable star type based on light curve data and associated information, one of the goals of this work is the acknowledgment that the development of a true machine learning methodology must include not only study of what goes into the service (features, optimization methods) but a study on how we understand what comes out of the service (performance analysis). The complete development of a beginning-to-end system development strategy is presented as the following individual developments (simulation, training, feature extraction, detection, classification, and performance analysis). We propose that a complete machine learning strategy for use in the upcoming era of big data from the next generation of big telescopes, such as LSST, must consider this type of design integration.en_US
dc.rightsCC BY 4.0en_US
dc.titleAdvanced Astroinformatics for Variable Star Classificationen_US
dc.typeDissertationen_US of Philosophy in Space Sciencesen_US Sciencesen_US, Physics and Space Sciencesen_US Institute of Technologyen_US

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