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Bayesian techniques for astrophysical inference from gravitational-waves of compact binary coalescences: an application to the Third LIGO-Virgo-KAGRA observing run

D'Emilio, Virginia 2023. Bayesian techniques for astrophysical inference from gravitational-waves of compact binary coalescences: an application to the Third LIGO-Virgo-KAGRA observing run. PhD Thesis, Cardiff University.
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A major challenge in gravitational-wave astrophysics is the interpretation of observations, which requires accurate inference of the astrophysical parameters and a rigorous statistical framework. The main focus of this thesis is the analysis of modelled gravitational-wave sources and its enhancement with machine learning and other statistical techniques. Bayesian statistics is at the base of gravitational-wave analysis and interpretation since each observation is unique and can often be assumed to be independent of all others. The unifying thread of this thesis is Bayes’s theorem: how it is routinely leveraged for gravitational-wave analysis, allowing much of the work presented here, and how its use can be extended to develop new analysis techniques. The most notable application of Bayesian statistics in the field is the parameter estimation of compact binary coalescence. Chapter 2 reports the work done to reproduce the first Gravitational-Wave Transients Catalogue (GWTC-1) with the Bayesian Inference Library: bilby. The rigorous comparison between previous GWTC-1 results and the one presented here allowed bilby’s specific tuning towards the gravitational-wave inference problem. Chapter 3, presents the author’s work related to the discovery of the first neutronstar black-hole (NSBH) mergers GW200105 and GW200115, where bilby was used to estimate the parameter of the observed sources. This chapter also illustrates the role of gravitational-wave observations in our understanding of the astrophysical origins of binary sources. Chapter 4 describes a novel effective likelihood method to quantitively compare astrophysical distributions inferred from gravitational-wave observations and distributions obtained with theoretical simulations. This method, which is driven by a Bayesian philosophy, is applied to a set of globular cluster simulations and real data from the third Gravitational-Wave Transients Catalogue (GWTC-3). Chapter 5, presents a novel density estimation tool for parameter estimation products from gravitational-wave observations, based on Gaussian Processes which are a Bayesian machine learning technique. This density estimation method was found to be advantageous over other traditional methods for several gravitational wave applications since we need both the accurate treatment of individual event samples, e.g. standard siren analysis, but also robust propagation of systematics when combining multiple observations, e.g. measure of systematic errors. Finally, Chapter 6 presents a study that makes use of bilby to re-analyse the binary neutron star (BNS) event GW190425, in light of its potential electromagnetic counterpart FRB20190425A, and makes use of a Gaussian Process density estimator to calculate the Bayesian odds of the claimed association. This work is extended by performing a standard siren measurement for GW190425 and its potential host galaxy to determine the value of the Hubble constant.

Item Type: Thesis (PhD)
Date Type: Acceptance
Status: Unpublished
Schools: Physics and Astronomy
Subjects: Q Science > QB Astronomy
Q Science > QC Physics
Uncontrolled Keywords: parameter estimation, bayesian statistical methods, gravitational wave data analysis, compact binaries coalescences, gaussian processes, interpreting observations
Funders: Science & Technology Facilities Council (STFC)
Date of First Compliant Deposit: 10 August 2023
Date of Acceptance: 28 April 2023
Last Modified: 10 Aug 2023 11:04

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