Title:
On gravitational wave modeling: numerical relativity data analysis, the excitation of kerr quasinormal modes, and the unsupervised machine learning of waveform morphology
On gravitational wave modeling: numerical relativity data analysis, the excitation of kerr quasinormal modes, and the unsupervised machine learning of waveform morphology
Author(s)
London, Lionel
Advisor(s)
Bogdanović, Tamara
Laguna, Pablo
Laguna, Pablo
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Abstract
The expectation that light waves are the only way to gather information about the distant
universe dominated scientific thought, without serious alternative, until Einstein’s 1916
proposal that gravitational waves are generated by the dynamics of massive objects. Now,
after nearly a century of speculation, theoretical development, observational support, and
finally, tremendous experimental preparation, there are good reasons to believe that we will
soon directly detect gravitational waves. One of the most important of these good reasons
is the fact that matched filtering enables us to dig gravitational wave signals out of noisy
data, if we have prior information about the signal’s morphology. Thus, at the interface of
Numerical Relativity simulation, and data analysis for experiment, there is a central effort
to model likely gravitational wave signals. In this context, I present my contributions to
the modeling of Gravitational Ringdown (Kerr Quasinormal Modes). Specifically by ap-
propriately interfacing black hole perturbation theory with Numerical Relativity, I present
the first robust models for Quasinormal Mode excitation. I present the first systematic de-
scription of Quasinormal Mode overtones in simulated binary black hole mergers. I present
the first systematic description of nonlinear Quasinormal Mode excitation in simulated bi-
nary black hole mergers. Lastly, it is suggested that by analyzing the phase of black hole
Quasinormal Modes, we may learn information about the black hole’s motion with respect
to the line of sight. Moreover, I present ongoing work at the intersection of gravitational
wave modeling and machine learning. This work shows promise for the automated and near
optimal placement of Numerical Relativity simulations concurrent with the near optimal
linear modeling of gravitational output.
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Date Issued
2015-08-05
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Resource Type
Text
Resource Subtype
Dissertation