Presentation #326.07 in the session Gravitational Wave Cosmology and Methodologies.
We currently lack good waveform models for many gravitational wave sources. Examples include neutron star post merger signal, core collapse supernovae, and signals of unknown origin. Previously, wavelet based techniques have been used to detect and characterize these signals. Here we introduce a new method that uses collections of evolving harmonics, or “voices”, to model generic gravitational wave signals. The analysis is implemented using trans-dimensional Bayesian inference, building on the earlier wavelet-based BayesWave algorithm. The new algorithm, BayesWaveVoices, outperforms the original for a wide range on astrophysically interesting signal types.