Bayesian reconstruction of gravitational wave bursts using chirplets

Abstract

The LIGO-Virgo Collaboration uses a variety of techniques to detect and characterize gravitational waves. One approach is to use templates-models for the signals derived from Einstein\'s equations. Another approach is to extract the signals directly from the coherent response of the detectors in the LIGO-Virgo network. Both approaches played an important role in the first gravitational wave detections. Here we extend the BAYESWAVE analysis algorithm, which reconstructs gravitational wave signals using a collection of continuous wavelets, to use a generalized wavelet family, known as chirplets, that have time-evolving frequency content. Since generic gravitational wave signals have frequency content that evolves in time, a collection of chirplets provides a more compact representation of the signal, resulting in more accurate waveform reconstructions, especially for low signal-to-noise events, and events that occupy a large time-frequency volume.