Barrett, James William (2018). Topics in astrostatistics: stellar binary evolution, gravitational – wave source modelling and stochastic processes. University of Birmingham. Ph.D.
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Barrett18PhD.pdf
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Abstract
The effective use of statistical techniques is one of the cornerstones of modern astrophysics. In this thesis we use sophisticated statistical methodology to expand our understanding of astrophysics. In particular, we focus on the physics of coalescing binary black holes, and the observation of these events using gravitational-wave astronomy. We use Fisher matrices to explore how much we expect to learn from gravitational-wave observations, and then use machine learning techniques, including random forests and Gaussian processes, to facilitate an otherwise intractable Bayesian comparison of real observations to our model. Finally, we develop a technique based on Gaussian processes for characterising stochastic variability in time series data.
| Type of Work: | Thesis (Doctorates > Ph.D.) | |||||||||
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| Award Type: | Doctorates > Ph.D. | |||||||||
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| College/Faculty: | Colleges (2008 onwards) > College of Engineering & Physical Sciences | |||||||||
| School or Department: | School of Physics and Astronomy | |||||||||
| Funders: | Science and Technology Facilities Council | |||||||||
| Subjects: | Q Science > QB Astronomy | |||||||||
| URI: | http://etheses.bham.ac.uk/id/eprint/8203 |
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