A comprehensive Bayesian approach to gravitational wave astronomy

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Montana State University - Bozeman, College of Letters & Science


The 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.




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