Date of Award

9-15-2016

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Mathematics and Statistics

First Advisor

Christine M. Schubert Kabban, PhD.

Abstract

The growing size of cosmological data sets is causing the current human-centric approach to cosmology to become impractical. Autonomous data analysis techniques need to be developed in order to advance the field of cosmology. This research examines the benefits of combining two signal analysis techniques, namely phase folding and wavelet denoising, into a newly-developed suite of autonomous light curve analysis tools which includes aspects of component extraction and period detection. The improvements these tools provide, with respect to autonomy and signal quality, are demonstrated using both simulated and real-world light curve data. Although applied to light curve data, the suite of tools developed in this dissertation are advantageous to the processing, modeling, or extractions to any periodic signal analysis.

AFIT Designator

AFIT-ENC-DS-16-S-001

DTIC Accession Number

AD1017864

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