TY - JOUR
T1 - Bayesian inference analysis of unmodelled gravitational-wave transients
AU - Pannarale, Francesco
AU - Macas, Ronaldas
AU - Sutton, Patrick J.
PY - 2019/1/18
Y1 - 2019/1/18
N2 - 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 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 follows 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.
AB - 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 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 follows 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.
KW - data analysis
KW - gravitational waves
KW - gravitational-wave detection
KW - UKRI
KW - STFC
KW - ST/L000962/1
KW - ST/N005430/1
UR - http://www.scopus.com/inward/record.url?scp=85060187381&partnerID=8YFLogxK
U2 - 10.1088/1361-6382/aaf76d
DO - 10.1088/1361-6382/aaf76d
M3 - Article
AN - SCOPUS:85060187381
SN - 0264-9381
VL - 36
JO - Classical and Quantum Gravity
JF - Classical and Quantum Gravity
IS - 3
M1 - 035011
ER -