Statistical and Dimension Reduction Methodology for Detecting and Parametrizing Core-Collapse Supernovae
Date of Award
Thesis - Restricted
Master of Science (MS)
Mathematics, Statistics and Computer Science
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.