Date of Award

Spring 2020

Document Type

Thesis - Restricted

Degree Name

Master of Science (MS)


Mathematics, Statistics and Computer Science

First Advisor

Rowe, Daniel

Second Advisor

Traylor, Rachel

Third Advisor

Maadooliat, Mehdi


This paper explores statistical and dimension reduction methodology in the context of detecting and parametrizing gravitational wave signal from core collapse supernovae. Gravitational wave interferometer signals are simulated using a noise model which seeks to match the true detector noise. Using template matching, these noisy signals are tested to see if the underlying waveform can be detected. Then, using PCR the locations of interest within the signals are reconstructed and hypothesis tests for significance of coefficients are performed. While parameter estimation by way of hypothesis tests on significance of constituent waveforms shows limited usefulness, template matching is shown to be a useful and efficient method even within this context.


Restricted Access Item

Having trouble?