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Published January 15, 2023 | Published
Journal Article Open

Gravitational wave inference on a numerical-relativity simulation of a black hole merger beyond general relativity

  • 1. ROR icon California Institute of Technology

Abstract

We apply common gravitational wave inference procedures on binary black hole merger waveforms beyond general relativity. We consider dynamical Chern-Simons gravity, a modified theory of gravity with origins in string theory and loop quantum gravity. This theory introduces an additional parameter ℓ, corresponding to the length-scale below which beyond-general-relativity effects become important. We simulate data based on numerical relativity waveforms produced under an approximation to this theory, which differ from those of general relativity in the strongly nonlinear merger regime. We consider a system with parameters similar to GW150914 with different values of ℓ and signal-to-noise ratios. We perform two analyses of the simulated data. The first is a template-based analysis that uses waveforms derived under general relativity and allows us to identify degeneracies between the two waveform morphologies. The second is a morphology-independent analysis based on bayeswave that does not assume that the signal is consistent with general relativity. The bayeswave analysis faithfully reconstructs the simulated signals. However, waveform models derived under general relativity are unable to fully mimic the simulated modified-gravity signals and such a deviation would be identifiable with existing inference tools. Depending on the magnitude of the deviation, we find that the templated analysis can under perform the morphology-independent analysis in fully recovering simulated beyond-GR waveforms even for achievable signal-to-noise ratios ≳20–30.

Copyright and License

© 2023 American Physical Society.

Acknowledgement

The authors are grateful for computational resources provided by the LIGO Laboratory and supported by NSF Grants No. PHY-0757058 and No. PHY-0823459. The Flatiron Institute is supported by the Simons Foundation. K. C. was supported by NSF Grant No. PHY-2110111. Waveform injections were performed using pycbc [89,90]. The morphology-independent analysis was performed using the bayeswave package [37,38], and the GR-template analysis was performed using the bilby package [40]. Postprocessing was performed using the pesummary package [91] and the igwn-wave-compare library [92]. We thank Sudarshan Ghonge for help using the igwn-wave-compare [92]. Corner plots were produced using the corner python package [93].

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Additional details

Created:
July 9, 2024
Modified:
July 9, 2024