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arXiv · 1804.03239

Bayesian reconstruction of gravitational wave bursts using chirplets

Margaret Millhouse·Neil J. Cornish·Tyson Littenberg
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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.

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Margaret Millhouse, Neil J. Cornish, Tyson Littenberg. 2018-04-09. Bayesian reconstruction of gravitational wave bursts using chirplets. https://doi.org/10.1103/physrevd.97.104057

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