In Partial Fulfillment of the Requirements for the Degree of Master of Science
will defend his thesis
Hierarchical Classification of Variable Stars Using Neural Networks
Variable Stars play a prominent role in our study of the universe and are essential to estimate cosmological parameters. They are considered as ‘standard candles’ due to their intrinsic variability, which is useful to calculate their distance. With the advent of large-scale sky surveys generating over 20 Terabytes of light-curve observations every day, automated methods are necessary to reduce the manual effort when classifying variable stars. To automate such classification, astronomers have developed various machine learning algorithms. Existing algorithms exploit star properties, but fail to use the hierarchical structure known to exist in a specific family of stars. We believe embedding hierarchical information of stars into a learning algorithm can lead to more robust and efficient machine learning models. The goal of this thesis is to explore various approaches that exploit the hierarchical structure of stars within a neural network architecture. Results show the conditions under which adding information regarding the intrinsic hierarchical structure helps increase generalization performance.
Date: Friday, June 28, 2019
Time: 4:00 PM
Place: MREB 222
Advisors: Dr. Ricardo Vilalta
Faculty, students, and the general public are invited.