Use this URL to cite or link to this record in EThOS:
Title: Topics in astrostatistics : stellar binary evolution, gravitational-wave source modelling and stochastic processes
Author: Barrett, James William
ISNI:       0000 0004 7426 077X
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Access from Institution:
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.
Supervisor: Not available Sponsor: Science and Technology Facilities Council (STFC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QB Astronomy