Bayesian inference analysis of unmodelled gravitational-wave transients

Pannarale Greco, Francesco ORCID: https://orcid.org/0000-0002-7537-3210, Macas, Ronaldas and Sutton, Patrick J ORCID: https://orcid.org/0000-0003-1614-3922 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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Abstract

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_⊙. 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:	02 Dec 2024 08:00
URI:	https://orca.cardiff.ac.uk/id/eprint/118391

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