Extracting weak Gravitional Wave events

Amongst the strong gravitational wave detections, such as GW150914 (the first event), there will be a host of marginal signals. These weaker events will contain a wealth of information about the population of binary mergers in the Universe, and may even outnumber the strong signals.

Advanced data-processing and machine learning techniques will be deployed to better separate signals from the background noise and maximize the number of signals that can be extracted from the data. These additional signals will then be used to better understand the underlying population of black holes and neutron stars.

As the number of gravitational wave signals increases, machine learning and classification techniques will be used to understand the properties of the observed populations and uncover details of the formation and evolution of massive stars.

Supervisors

Stephen Fairhurst

Professor Stephen Fairhurst

Professor of Physics and Astronomy
Director, Data Innovation Research Institute

Email:
fairhursts@cardiff.ac.uk
Telephone:
+44 (0)29 2087 0166

Programme information

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