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<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: | 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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