Machine learning to extract gravitational wave transients

Skliris, Vasileios 2021. Machine learning to extract gravitational wave transients. PhD Thesis, Cardiff University. Item availability restricted.

Preview	PDF - Accepted Post-Print Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (5MB) | Preview
	PDF (Cardiff University Electronic Publication Form) - Supplemental Material Restricted to Repository staff only Download (1MB)

Abstract

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Status:	Unpublished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
URI:	https://orca.cardiff.ac.uk/id/eprint/149256

Actions (repository staff only)

Edit Item