Detection of gravitational-wave bursts with chirplet-like template families

TL;DR

This paper introduces an extension to gravitational-wave burst detection algorithms using chirplet-like templates, which better capture rapidly evolving frequency signals such as black hole mergers, improving detection sensitivity.

Contribution

The paper presents a novel extension of the Omega pipeline that incorporates chirplet templates to handle non-stationary signals with rapid frequency changes.

Findings

Chirplet templates improve signal-to-noise ratio in simulated data.

The extended pipeline effectively detects binary black-hole coalescence waveforms.

Chirplet-based detection outperforms sine-Gaussian templates for rapidly evolving signals.

Abstract

Gravitational Wave (GW) burst detection algorithms typically rely on the hypothesis that the burst signal is "locally stationary", that is it changes slowly with frequency. Under this assumption, the signal can be decomposed into a small number of wavelets with constant frequency. This justifies the use of a family of sine-Gaussian templates in the Omega pipeline, one of the algorithms used in LIGO-Virgo burst searches. However there are plausible scenarios where the burst frequency evolves rapidly, such as in the merger phase of a binary black hole and/or neutron star coalescence. In those cases, the local stationarity of sine-Gaussians induces performance losses, due to the mismatch between the template and the actual signal. We propose an extension of the Omega pipeline based on chirplet-like templates. Chirplets incorporate an additional parameter, the chirp rate, to control the frequency variation. In this paper, we show that the Omega pipeline can easily be extended to include a chirplet template bank. We illustrate the method on a simulated data set, with a family of phenomenological binary black-hole coalescence waveforms embedded into Gaussian LIGO/Virgo-like noise. Chirplet-like templates result in an enhancement of the measured signal-to-noise ratio.