Jessica Page

Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Space Science

Committee Chair

Qiang Hu

Committee Member

Richard Lieu

Committee Member

Michael Briggs

Committee Member

Peter Veres


Gravitational waves, Markov processes, Monte Carlo method, Space interferometry


Gravitational wave (GW) data analysis often necessarily includes limiting assumptions about detector noise and its response function during astrophysical parameter estimation. Here we take a broad look at the impact these assumptions can have across the GW spectrum, with an emphasis on Bayesian techniques for the future space-based Laser Interferometer Space Antenna (LISA) mission. Laser frequency noise (LFN) will be the dominant source of noise in the LISA mission at ~7 orders of magnitude greater amplitude than typical GWs. Time-delay interferometry (TDI) is the method for suppressing LFN to acceptable levels by linearly combining time-shifted measurements to digitally represent an equal-arm interferometer. Knowledge of these time shifts at ~ nanosecond accuracy is crucial for TDI effectiveness. TDI ranging (TDIR) infers the necessary spacecraft (S/C) separations from the raw data instead of using pre-combined TDI channels. This work uses a Markov chain Monte Carlo (MCMC) algorithm for estimating the S/C separations, and is designed to be included as part of the LISA data model for a ``global fit'' analysis that is simultaneous with GW characterization. Having uncertainty estimates for the arm lengths in the LISA detector response function can potentially eliminate astrophysical parameter bias, while providing a near real-time estimate of the spacecraft positions and velocities independent from mission ephemeris data. The foundation for these efforts are provided using a rigid rotation approximation of the LISA constellation, and then a full heliocentric orbital treatment with time-varying arm lengths is considered. Related work on LIGO (Laser Interferometer Gravitational Wave Observatory) data from the first observing run explores a Bayesian analysis tool that models non-stationary deterministic noise. Education and public outreach efforts done in collaboration with NANOGrav (North American Nanohertz Observatory for Gravitational Waves) GW pulsar timing detection are also described.



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