Department of Computer Science at UH

University of Houston

Department of Computer Science

In Partial Fulfillment of the Requirements for the Degree of
Master of Science

Dallas Brittany Kidd

Will defend her thesis

Using Machine Learning Methods for Automatic Classification of Classical Cepheids


With 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 in the following machine learning methods: Multilayer Perceptron, Naive 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, and we identify a number of useful features using methods like Information Gain and Gain Ratio and a series of experiments.


Date: Tuesday, March 31, 2015
Time: 10:00 AM
Place: HBS 350

Faculty, students, and the general public are invited.
Advisor: Prof. Ricardo Vilalta