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


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 - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

The expectation that light waves are the only way to gather information about the distantuniverse dominated scientific thought, without serious alternative, until Einstein’s 1916proposal that gravitational waves are generated by the dynamics of massive objects. Now,after nearly a century of speculation, theoretical development, observational support, andfinally, tremendous experimental preparation, there are good reasons to believe that we willsoon directly detect gravitational waves. One of the most important of these good reasonsis the fact that matched filtering enables us to dig gravitational wave signals out of noisydata, if we have prior information about the signal’s morphology. Thus, at the interface ofNumerical Relativity simulation, and data analysis for experiment, there is a central effortto model likely gravitational wave signals. In this context, I present my contributions tothe modeling of Gravitational Ringdown Kerr Quasinormal Modes. Specifically by ap-propriately interfacing black hole perturbation theory with Numerical Relativity, I presentthe 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 presentthe 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 holeQuasinormal Modes, we may learn information about the black hole’s motion with respectto the line of sight. Moreover, I present ongoing work at the intersection of gravitationalwave modeling and machine learning. This work shows promise for the automated and nearoptimal placement of Numerical Relativity simulations concurrent with the near optimallinear modeling of gravitational output.



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Autor: London, Lionel - -

Fuente: https://smartech.gatech.edu/







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