Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.819063
Title: Mapping the gravitational-wave background
Author: Renzini, Arianna
ISNI:       0000 0004 9357 0757
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2020
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Abstract:
Given the recent detection of gravitational waves (GWs) from individual sources it is almost a certainty that some form of background of gravitational waves will be detected in future. The most promising candidates for such a detection are backgrounds made up of the incoherent superposition of the signal of unresolved astrophysical or, possibly, earlier primordial sources. Such backgrounds will also be anisotropic as they would trace the distribution of the underlying sources and the anisotropy may also be detected. To this end, this thesis presents Gravitational Wave Background (GWB) map-making algorithms that use the cross-correlation of multiple data streams. The resulting maps are maximum likelihood representations of the GWB intensity on the sky. Two different methods are developed, depending on whether the detector timestreams may be considered uncorrelated or not. These are then tailored to two GW detector classes, ground-based interferometers and the Laser Interferometer Space Antenna (LISA), and tested on mock data as proof of concept. The mapping method optimised for the Laser Interferometer Gravitational-wave Observatories (LIGOs) is applied to LIGO open data sets, providing the most stringent upper limits to the GWB yet. These are obtained assuming a spectral model for the signal which weights the data and optimises the search. A novel, model-independent estimation scheme is also presented. The method applied to LISA provides the first full-likelihood map estimator from LISA data. The aim of this work is to deliver the framework of the LISA GWB map-making pipeline which will be progressively refined and updated until data become available.
Supervisor: Contaldi, Carlo Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.819063  DOI:
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