Search strategies for long gravitational-wave transients: Hidden Markov model tracking and seedless clustering

Sharan Banagiri, Ling Sun, Michael W. Coughlin, and Andrew Melatos
Phys. Rev. D 100, 024034 – Published 16 July 2019

Abstract

A number of detections have been made in the past few years of gravitational waves from compact binary coalescences. While there exist well-understood waveform models for signals from compact binary coalescences, many sources of gravitational waves are not well modeled, including potential long-transient signals from a binary neutron star postmerger remnant. Searching for these sources requires robust detection algorithms that make minimal assumptions about any potential signals. In this paper, we compare two unmodeled search schemes for long-transient gravitational waves, operating on cross-power spectrograms. One is an efficient algorithm first implemented for continuous wave searches, based on a hidden Markov model. The other is a seedless clustering method, which has been used in transient gravitational wave analysis in the past. We quantify the performance of both algorithms, including sensitivity and computational cost, by simulating synthetic signals with a special focus on sources like binary neutron star postmerger remnants. We demonstrate that the hidden Markov model tracking is a good option in model-agnostic searches for low signal-to-noise ratio signals. We also show that it can outperform the seedless method for certain categories of signals while also being computationally more efficient.

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  • Received 11 March 2019

DOI:https://doi.org/10.1103/PhysRevD.100.024034

© 2019 American Physical Society

Physics Subject Headings (PhySH)

Gravitation, Cosmology & Astrophysics

Authors & Affiliations

Sharan Banagiri1,*, Ling Sun2,†, Michael W. Coughlin2, and Andrew Melatos3

  • 1School of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota 55455, USA
  • 2LIGO Laboratory, California Institute of Technology, Pasadena, California 91125, USA
  • 3OzGrav, University of Melbourne, Parkville, Victoria 3010, Australia

  • *banag002@umn.edu
  • lssun@caltech.edu

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Issue

Vol. 100, Iss. 2 — 15 July 2019

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