Bayesian reconstruction of gravitational wave bursts using chirplets

TL;DR

This paper extends the BayesWave algorithm to use chirplets, a generalized wavelet family, for more accurate reconstruction of gravitational wave signals with evolving frequency content, especially in low SNR cases.

Contribution

It introduces chirplets into the Bayesian reconstruction framework, improving waveform accuracy for signals with time-varying frequencies.

Findings

More accurate waveform reconstructions for low SNR events.

Better representation of signals with large time-frequency volume.

Enhanced detection capabilities for evolving gravitational wave signals.

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 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.