Theses and Dissertations at Montana State University (MSU)
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Item Studies in alternative theories of gravity and advanced data analysis(Montana State University - Bozeman, College of Letters & Science, 2024) Gupta, Toral; Chairperson, Graduate Committee: Neil J. Cornish; This is a manuscript style paper that includes co-authored chapters.The field of gravitational wave astronomy is generating groundbreaking findings, yielding unique insights on some of the most extraordinary phenomena in the universe and providing invaluable information on testing the principles of general relativity. All gravitational wave signals detected so far appear to come from compact binaries - black holes and neutron stars. We use information from these sources to probe strong fields of gravity and to constrain modified theories of gravity. However, solely relying on template- based searches for known astrophysical sources biases our gravitational wave signal search towards well-modeled systems, potentially overlooking unpredicted sources with limited theoretical models, hindering the extraction of new physics. Further work in this thesis focuses on building improved signal and noise models to enhance our capability of detecting gravitational signals of all within and beyond the constraints of theoretical predictions. This includes introduction of new basis functions with added modifications to develop a signal-agnostic waveform reconstruction model using Bayesian inference. Additionally, this study discusses improvements in the speed and performance of the BayesWave trans-dimensional Bayesian spectral estimation algorithm, which includes implementing a low-latency analysis and various enhancements to the algorithm itself. In essence, this study is centered on developing a comprehensive understanding, both theoretical and observational, of astrophysical objects along with the spacetime that governs their dynamics.Item A comprehensive Bayesian approach to gravitational wave astronomy(Montana State University - Bozeman, College of Letters & Science, 2009) Littenberg, Tyson Bailey; Chairperson, Graduate Committee: Neil J. CornishThe challenge of determining whether data from a gravitational wave detector contains signals which are cosmic in origin is the central problem in gravitational wave astronomy. The "detection problem" is particularly challenging for low amplitude signals embedded in "glitchy" instrument noise. It is imperative that we can robustly distinguish between the data being consistent with instrument noise alone, or noise and a weak gravitational wave signal. In response to this challenge we have set out to develop a robust, general purpose approach that can locate and characterize gravitational wave signals, and provided odds that the signal is of cosmic origin. Our approach employs the Markov Chain Monte Carlo family of algorithms to construct a fully Bayesian solution to the challenge - the Parallel Tempered Markov Chain Monte Carlo (PTMCMC) detection algorithm. The PTMCMC detection algorithm establishes which regions of parameter space contain the highest posterior weight, efficiently explores the posterior distribution function of the model parameters, and calculates the marginalized likelihood, or evidence, for the models under consideration. We illustrate our approach using simulated LISA and LIGO-Virgo data.