On gravitational wave modeling: numerical relativity data analysis, the excitation of kerr quasinormal modes, and the unsupervised machine learning of waveform morphology
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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.