Variable star analysis and classification is an important task in the understanding of stellar features and processes. While historically classifications have been done manually by highly skilled experts, the recent and rapid expansion in the quantity and quality of data has demanded new techniques, most notably automatic classification through supervised machine learning. I present a study on variable stars in the Kepler field using these techniques, and the novel work of unsupervised learning. I use new methods of characterization and multiple independent classifiers to produce an ensemble classifier that equals or matches existing classification abilities. I also explore the possibilities of unsupervised learning in making novel feature discovery in stars.
|Commitee:||Summers, Michael, Weingartner, Joe, Yang, Ruixin|
|School:||George Mason University|
|School Location:||United States -- Virginia|
|Source:||DAI-B 77/07(E), Dissertation Abstracts International|
|Keywords:||Astronomy, Data mining, Machine learning, Variable stars|
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