Classification of Transients by Distance Measures
Open Access
- Author:
- Park, Sae Na
- Graduate Program:
- Statistics
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- May 26, 2015
- Committee Members:
- G Jogesh Babu, Dissertation Advisor/Co-Advisor
G Jogesh Babu, Committee Chair/Co-Chair
John Fricks, Committee Member
Matthew Logan Reimherr, Committee Member
Eric B Ford, Committee Member - Keywords:
- Classification
Kullback-Leibler divergence
Kernel density estimation
Transients
Variable stars - Abstract:
- Due to a rapidly increasing size of data in astronomical surveys, statistical methods which can automatically classify newly detected celestial objects in an accurate and efficient way have become essential. In this dissertation, we introduce two methodologies to classify variable stars and transients by using light curves, which are graphs of magnitude (the logarithm measure of brightness of a star) as a function of time. Our analysis focuses on characterizing light curves by using magnitude changes over time increments and developing a classifier with this information. First we present the classifier based on the difference between two distributions of magnitudes, estimated by the statistical distance measures such as the Kullback-Leibler divergence, the Jensen-Shannon divergence, and the Hellinger distance. Also, we propose a method that groups magnitudes and times by binning and uses frequencies in each bin as the variables for classification. Along with these two methods, a way to incorporate other measures into our classifiers, which have been used for classification of light curves, is presented. Finally, the proposed methods are demonstrated with real data and compared with the past classification methods of variable stars and transients.