Applications of machine learning for gravitational wave astrophysics

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The aim of the project is to apply and develop machine learning techniques to the data analysis problems associated with gravitational wave astrophysics. The student will initially tackle the compact binary coalescence detection problem and show that a deep neural network can be used to replicate the performance of traditional matched-filtering approaches. This will be followed by an investigation into the feasibility of performing parameter estimation for well modelled gravitational wave signals with the aim of comparing the machine learning with the optimal Bayesian approach. With these tools in hand the student will then extend the new machine learning tools to other modelled gravitational wave signals and detectors (e.g., 3rd generation and space-based). In addition the student will investigate the use of un-supervised (or weakly supervised) machine learning algorithms for the search and characterisation of un-modelled transient gravitational wave signals (bursts).