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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:752970 |
| Date | January 2018 |
| Creators | Barrett, James William |
| Publisher | University of Birmingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.bham.ac.uk//id/eprint/8203/ |
Page generated in 0.002 seconds