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Results from an injection and recovery study involving 35 numerical relativity simulations and three waveform models

Sarp Akçay1, Charlie Hoy2, and Jake Mac Uilliam1,3

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Phys. Rev. D 112, 084032 – Published 14 October, 2025

DOI: https://doi.org/10.1103/rkgr-psrt

Abstract

We present Bayesian inference results from an extensive injection-recovery campaign to test the validity of three state of the art quasicircular gravitational waveform models: SEOBNRv5PHM, imrphenomtphm, imrphenomxphm, the latter with the SpinTaylorT4 implementation for its precession dynamics. We analyze 35 strongly precessing binary black hole numerical relativity simulations with all available harmonic content. Ten simulations have a mass ratio of 4:1 and five, mass ratio of 8:1. Overall, we find that SEOBNRv5PHM is the most consistent model to numerical relativity, with the majority of true source properties lying within the inferred 90% credible interval. However, we find that none of the models can reliably infer the true source properties for binaries with mass ratio 8:1 systems. We additionally conduct inspiral-merger-ringdown (IMR) consistency tests to determine if our chosen state of the art waveform models infer consistent properties when analysing only the inspiral (low frequency) and ringdown (high frequency) portions of the signal. For the simulations considered in this work, we find that the IMR consistency test depends on the frequency that separates the inspiral and ringdown regimes. For two sensible choices of the cutoff frequency, we report that imrphenomxphm can produce false GR deviations. Meanwhile, we find that imrphenomtphm is the most reliable model under the IMR consistency test. Finally, we reanalyze the same 35 simulations, but this time we incorporate model accuracy into our Bayesian inference. Consistent with the work in Hoy et al. Incorporating model accuracy into gravitational-wave Bayesian inference, Nat. Astron. 9, 1256 (2025)., we find that this approach generally yields more accurate inferred properties for binary black holes with less biases compared to methods that combine model-dependent posterior distributions based on their evidence, or with equal weight.

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Phys. Rev. D 112, 084032– Published 14 October, 2025
  • Received 27 June 2025
  • Accepted 15 August 2025
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