Bayesian reconstruction of gravitational wave bursts using chirplets

Margaret Millhouse, Neil J. Cornish, and Tyson Littenberg
Phys. Rev. D 97, 104057 – Published 29 May 2018

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 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.

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  • Received 4 April 2018

DOI:https://doi.org/10.1103/PhysRevD.97.104057

© 2018 American Physical Society

Physics Subject Headings (PhySH)

Gravitation, Cosmology & Astrophysics

Authors & Affiliations

Margaret Millhouse and Neil J. Cornish

  • eXtreme Gravity Institute, Department of Physics, Montana State University, Bozeman, Montana 59717, USA

Tyson Littenberg

  • NASA Marshall Space Flight Center, Huntsville, Alabama 35812, USA

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Issue

Vol. 97, Iss. 10 — 15 May 2018

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