Convolutional Neural Network for Analysing Gravitational Wave Signals

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dc.contributor.advisorOttaway, David
dc.contributor.advisorLiu, Lingqiao
dc.contributor.advisorLasky, Paul (Monash University)
dc.contributor.authorJenner, Kendall Louise
dc.contributor.schoolSchool of Physics, Chemistry, and Earth Sciences
dc.date.issued2024
dc.description.abstractCurrent analysis methods for estimating the source parameters of gravitational wave signals can take from days to weeks. As gravitational wave detectors improve, current analysis methods will be insufficient to keep up with the increasing number of events detected. We investigate the use of convolutional neural networks (CNNs) and normalising flows to estimate the gravitational wave source parameters from the spectrogram of the signal. We find that a CNN can estimate the component masses of a simulated binary black hole (BBH) signal with a maximum 40% error; and that the accuracy decreases when also trying to estimate the luminosity distance and the angle between the line-of-sight and the total angular momentum of the binary system. We introduce a new architecture that uses a normalising flow conditioned on the CNNembedded spectrogram. This proves to be a much more robust approach, with the learned posteriors of the component masses capturing the injected values a majority of the time. When learning the posteriors of the component masses, luminosity distance, and θJN the model recovers the injected primary mass and θJN consistently, but under constrains the luminosity distance and over constrains the secondary mass. Overall, the posteriors produced using the convolutional neural network embedding model with a normalising flow (CNN+n.flow) were less accurate and much broader than those produced using matched filtering techniques with the Bayesian Inference Library (BILBY), but the inference time was significantly faster. This could open up opportunities for using the deep learning method to provide a proposal distribution for likelihood reweighting, offering both higher accuracy and quicker speeds for inference of gravitational wave signals.
dc.description.dissertationThesis (MPhil.) -- University of Adelaide, School of Physics, Chemistry, and Earth Sciences, 2024en
dc.identifier.urihttps://hdl.handle.net/2440/144699
dc.language.isoen
dc.provenanceThis electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legalsen
dc.subjectGravitational wave astronomy
dc.subjectconvolutional neural networks
dc.subjectmachine learning
dc.titleConvolutional Neural Network for Analysing Gravitational Wave Signals
dc.typeThesisen

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