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Bayesian inference analysis of unmodelled gravitational-wave transients

Pannarale Greco, Francesco ORCID:, Macas, Ronaldas and Sutton, Patrick J ORCID: 2019. Bayesian inference analysis of unmodelled gravitational-wave transients. Classical and Quantum Gravity 36 (3) , 035011. 10.1088/1361-6382/aaf76d

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We report the results of an in-depth analysis of the parameter estimation capabilities of BayesWave, an algorithm for the reconstruction of gravitational-wave signals without reference to a specific signal model. Using binary black hole signals, we compare BayesWave's performance to the theoretical best achievable performance in three key areas: sky localisation accuracy, signal/noise discrimination, and waveform reconstruction accuracy. BayesWave is most effective for signals that have very compact time-frequency representations. For binaries, where the signal time-frequency volume decreases with mass as the system mass increases, we find that BayesWave's performance reaches or approaches theoretical optimal limits for system masses above approximately 50 M<sub>⊙</sub>. For such systems BayesWave is able to localise the source on the sky as well as templated Bayesian analyses that rely on a precise signal model, and it is better than timing-only triangulation in all cases. We also show that the discrimination of signals against glitches and noise closely follow analytical predictions, and that only a small fraction of signals are discarded as glitches at a false alarm rate of 1/100 y. Finally, the match between BayesWave-reconstructed signals and injected signals is broadly consistent with first-principles estimates of the maximum possible accuracy, peaking at about 0.95 for high mass systems and decreasing for lower-mass systems. These results demonstrate the potential of unmodelled signal reconstruction techniques for gravitational-wave astronomy.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Physics and Astronomy
Publisher: IOP Publishing
ISSN: 0264-9381
Date of First Compliant Deposit: 14 January 2019
Date of Acceptance: 10 December 2018
Last Modified: 06 Nov 2023 21:41

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