There's more than one way to ride the wave: A Multi-Disciplinary Approach to Gravitational Wave Data Analysis

Lead Research Organisation: Cardiff University
Department Name: School of Physics and Astronomy


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.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
ST/P006779/1 01/10/2017 30/09/2024
1945971 Studentship ST/P006779/1 01/10/2017 30/09/2021 Rhys Green
Description OzGrav Gravitational Wave Parameter estimation face to face meeting
Amount $1,500 (AUD)
Organisation Australian Research Council 
Department Centre of Excellence for Gravitational Wave Discovery
Sector Public
Country Australia
Start 02/2019 
End 03/2019
Description LIGO Scientific Collaboration 
Organisation LIGO Scientific Collaboration
Country United States 
Sector Academic/University 
PI Contribution Regularly carrying out analysis for the collaboration and contributing to LSC publications
Collaborator Contribution Regular training, discussions and meetings
Impact Detection of Gravitational Waves, multiple published papers in academic journals
Description Oracle Internship 
Organisation Oracle Corporation
Country United States 
Sector Private 
PI Contribution I contributed Code to the Oracle AI apps team over a 6 month placement
Collaborator Contribution Training, regular meetings and transfer of knowledge
Impact Development of Oracle AI Apps software
Start Year 2017