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Machine learning to extract gravitational wave transients

Skliris, Vasileios 2021. Machine learning to extract gravitational wave transients. PhD Thesis, Cardiff University.
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Convolutional Neural Networks (CNNs) have demonstrated potential for the real-time analysis of data from gravitational-wave detector networks for the specific case of signals from coalescing compact-object binaries such as black-hole binaries. In this thesis we present the development of machine learning pipeline named MLy. We demonstrate a CNN with the ability to detect generic signals - those without a precise model - with sensitivity across a wide parameter space. In this endeavour we utilised the information of correlation between detectors, rather than signal morphologies, to distinguish correlated gravitational-wave signals from uncorrelated noise transients. We demonstrate the efficacy of our CNN using data from the second LIGO-Virgo observing run. We show that it has sensitivity approaching that of the "gold-standard" unmodeled transient searches currently used by LIGO-Virgo, at extremely low (order of 1 second) latency and using only a fraction of the computing power required by existing searches, allowing our models the possibility of true real-time detection of gravitational-wave transients associated with gamma-ray bursts, core-collapse supernovae, and other relativistic astrophysical phenomena.

Item Type: Thesis (PhD)
Date Type: Completion
Status: Unpublished
Schools: Physics and Astronomy
Subjects: Q Science > QB Astronomy
Q Science > QC Physics
Uncontrolled Keywords: GW, Gravitational Waves, Gravitational wave burst, Gravitational wave transients, Machine Learning, CNN, Convolutional Neural Networks, Gravitational Wave Astrophysics, Multi-messenger Astrophysics
Funders: STFC CDT
Date of First Compliant Deposit: 21 April 2022
Last Modified: 21 Apr 2022 11:09

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