Using Machine Learning for Automatic Classification of Classical Cepheids

dc.contributor.advisorVilalta, Ricardo
dc.contributor.committeeMemberShah, Shishir Kirit
dc.contributor.committeeMemberKaiser, Klaus
dc.creatorKidd, Dallas 1988-
dc.creator.orcid0000-0003-2079-4752
dc.date.accessioned2017-04-17T00:41:26Z
dc.date.available2017-04-17T00:41:26Z
dc.date.createdMay 2015
dc.date.issued2015-05
dc.date.submittedMay 2015
dc.date.updated2017-04-17T00:41:27Z
dc.description.abstractWith the increasing amounts of astronomical data being gathered, it is becoming more crucial for machine learning techniques to be employed for star classification. Classical Cepheid variable stars can be grouped into several classes, such as fundamental-mode, first-overtone, and second-overtone. Each class has distinctive features, and the light curves of the stars can be analyzed for these features in order to be used in automatic classification. Here, we focus on developing a number of features to be used with the following machine learning methods: Multilayer Perceptron, Naïve Bayes, J48 Decision Trees, and Random Forest. We use the OGLE (Optical Gravitational Lensing Experiment) datasets of Classical Cepheid variable stars in the Large Magellanic Cloud and the Small Magellanic Cloud. Our findings indicate that the Multilayer Perceptron is an excellent method for approaching this problem, as it outperformed the other machine learning methods. We also identify a number of useful features using Information Gain and Gain Ratio. Specifically, the newly developed features to measure symmetry had high classification power.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10657/1705
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectMachine learning
dc.subjectClassification
dc.subjectLarge Magellanic Cloud
dc.subjectSmall Magellanic Cloud
dc.subjectCepheids
dc.subjectCepheids
dc.subjectVariable stars
dc.subjectVariable stars
dc.subjectAstronomy
dc.subjectComputer science
dc.subjectOGLE
dc.subjectData
dc.subjectFundamental mode
dc.subjectFirst overtone
dc.subjectSecond overtone
dc.subjectFundamental mode
dc.subjectFirst overtone
dc.subjectSecond overtone
dc.subjectMultilayer Perceptron
dc.subjectNeural networks
dc.subjectNeurosciences
dc.subjectInformation gain
dc.subjectGain Ratio
dc.subjectClassifiers
dc.subjectStar
dc.subjectStars
dc.titleUsing Machine Learning for Automatic Classification of Classical Cepheids
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

Files

Original bundle

Now showing 1 - 4 of 4
Loading...
Thumbnail Image
Name:
KIDD-THESIS-2015.pdf
Size:
1002.5 KB
Format:
Adobe Portable Document Format
No Thumbnail Available
Name:
Kidd_Thesis_2015April15_Final_1.docx
Size:
1.12 MB
Format:
Microsoft Word XML
No Thumbnail Available
Name:
Kidd_Thesis_2015April15_Final.docx
Size:
1.12 MB
Format:
Microsoft Word XML
No Thumbnail Available
Name:
Kidd_Thesis_2015April14_Final.docx
Size:
1.12 MB
Format:
Microsoft Word XML

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.81 KB
Format:
Plain Text
Description: