Off-campus UMass Amherst users: To download campus access dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.

Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan.

Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.

Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Physics

Year Degree Awarded

2015

Month Degree Awarded

September

First Advisor

Laura Cadonati

Subject Categories

Cosmology, Relativity, and Gravity

Abstract

Among of the wide range of potentially interesting astrophysical sources for gravitational wave detectors Advanced LIGO and Advanced Virgo are galactic core-collapse supernovae. Although detectable core-collapse supernovae have a low expected rate (a few per century, or less) these signals would yield a wealth of new physics. Of particular interest is the insight into the explosion mechanism driving core-collapse supernovae that can be gleaned from the reconstructed gravitational wave signal. A well-reconstructed waveform will allow us to assess the likelihood of different explosion models, perform model selection, and potentially map unexpected features to new physics. This dissertation presents a series of studies evaluating the current performance of burst parameter estimation algorithms in reconstructing core-collapse supernovae gravitational wave signals in both simple Gaussian noise and realistic non-Gaussian detector noise. The introduction of non-Gaussian noise has a significant impact on the recovery of core-collapse supernova models from the data.

Terrestrial noise is also an important factor in the recovery of any gravitational wave search. This work also details a series of studies that enable the characterization of ground motion local to the Advanced LIGO inteferometers and the ability of the installed active seismic isolation to mitigate it.

Share

COinS