Date of Award

August 2014

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Physics

First Advisor

Xavier Siemens

Committee Members

Jolien Creighton, Dawn Erb, John Friedman, David Kaplan

Keywords

Data Analysis, Gravitation, Gravitational Waves, Pulsars

Abstract

Gravitational Waves (GWs) are tiny ripples in the fabric of spacetime predicted by Einstein's theory of General Relativity. Pulsar timing arrays (PTAs) offer a unique opportunity to detect low frequency GWs in the near future. Such a detection would be complementary to both LISA and LIGO GW efforts. In this frequency band, the expected source of GWs are Supermassive Black Hole Binaries (SMBHBs) that will most likely form an ensemble creating a stochastic GW background with possibly a few nearby/massive sources that will be individually resolvable. A direct detection of GWs will open a new window into the fields of astronomy and astrophysics by allowing us to constrain the coalescence rate of SMBHBs, providing us with further tests on the theory of General Relativity, and giving us access to properties of black holes not accessible by current astronomical techniques.

This dissertation work focuses primarily on the development of several robust data analysis pipelines for the detection and characterization of continuous GWs and a stochastic GW background. The data analysis problem for PTAs is quite difficult as one must fully take into account the timing model that must be fit in order to obtain the residuals, uneven sampling (including large gaps), and potential red noise processes. The data analysis techniques presented here handle all of these effects completely while allowing additional freedom in parameterizing the noise present in the data. The accumulation of work from this dissertation has resulted in a fully functional, robust, and efficient data analysis pipeline that has been successfully applied to the 5- and 9-year NANOGrav data releases.

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