Detection of GW bursts with chirplet-like template families

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 wavelets 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. 1. Motivations Current searches for gravitational wave transients in LIGO-Virgo data focus on two signal classes: short unmodelled bursts and longer quasi-periodic signals from inspiralling black hole and/or neutron star binaries as predicted by post-Newtonian approximations. To account for intermediate scenarios, we consider “chirping burst” GW target signals that exhibit characteristics from both the above categories: a short duration and a “sweeping” frequency. We propose here an extension of the Omega pipeline [3] (originally known as Q−pipeline) that searches for chirping bursts. The Omega pipeline projects the data over a family of sineGaussian wavelets with fixed frequency. The idea is to replace these templates by frequency varying waveforms, referred to as chirplets. In this paper, we first define chirplets and the related chirplet transform. We discuss the implementation of the chirplet transform and its insertion into the Omega pipeline, with attention to how the chirplet template bank is built. Finally, we present a few examples using simulated data. 0 200 400 600 80