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Topics in astrostatistics: stellar binary evolution, gravitational – wave source modelling and stochastic processes

Barrett, James William (2018)
Ph.D. thesis, University of Birmingham.

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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:Ph.D. thesis.
Supervisor(s):Farr, Will and Mandel, Ilya
School/Faculty:Colleges (2008 onwards) > College of Engineering & Physical Sciences
Department:School of Physics and Astronomy
Subjects:QB Astronomy
Institution:University of Birmingham
ID Code:8203
This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.
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