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Detection and parameter estimation of binary neutron star merger remnants

Easter, Paul J. and Ghonge, Sudarshan and Lasky, Paul D. and Casey, Andrew R. and Clark, James A. and Hernandez Vivanco, Francisco and Chatziioannou, Katerina (2020) Detection and parameter estimation of binary neutron star merger remnants. . (Unpublished)

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Detection and parameter estimation of binary neutron star merger remnants can shed light on the physics of hot matter at supranuclear densities. Here we develop a fast, simple model that can generate gravitational waveforms, and show it can be used for both detection and parameter estimation of post-merger remnants. The model consists of three exponentially-damped sinusoids with a linear frequency-drift term. The median fitting factors between the model waveforms and numerical-relativity simulations exceed 0.90. We detect remnants at a post-merger signal-to-noise ratio of ≥7 using a Bayes-factor detection statistic with a threshold of 3000. We can constrain the primary post-merger frequency to ±^(1.4)_(1.2)% at post-merger signal-to-noise ratios of 15 with an increase in precision to ±^(0.3)_(0.2)% for post-merger signal-to-noise ratios of 50. The tidal coupling constant can be constrained to ±⁹₁₂% at post-merger signal-to-noise ratios of 15, and ±5% at post-merger signal-to-noise ratios of 50 using a hierarchical inference model.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Lasky, Paul D.0000-0003-3763-1386
Casey, Andrew R.0000-0003-0174-0564
Hernandez Vivanco, Francisco0000-0002-1942-7608
Chatziioannou, Katerina0000-0002-5833-413X
Additional Information:P.D.L. is supported through Australian Research Council (ARC) Future Fellowship FT160100112, ARC Discovery Project DP180103155, and ARC Centre of Excellence CE170100004. A.R.C. is supported by ARC grant DE190100656. We are grateful to Sukanta Bose for valuable comments on the manuscript.
Funding AgencyGrant Number
Australian Research CouncilFT160100112
Australian Research CouncilDP180103155
Australian Research CouncilCE170100004
Australian Research CouncilDE190100656
Record Number:CaltechAUTHORS:20200731-150337553
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104687
Deposited By: Tony Diaz
Deposited On:31 Jul 2020 22:39
Last Modified:31 Jul 2020 22:39

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