Bayesian reconstruction of gravitational wave bursts using chirplets
Cornish, Neil J.
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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.
Millhouse, Margaret, Neil J. Cornish, and Tyson Littenberg. "Bayesian reconstruction of gravitational wave bursts using chirplets." Physical Review D 97, no. 10 (May 2018). DOI:10.1103/PhysRevD.97.104057.