Papers
Topics
Authors
Recent
Search
2000 character limit reached

Waging a Campaign: Results from an Injection-Recovery Study involving 35 numerical Relativity Simulations and three Waveform Models

Published 24 Jun 2025 in gr-qc and astro-ph.IM | (2506.19990v1)

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: \textsc{SEOBNRv5PHM}, \textsc{IMRPhenomTPHM}, \textsc{IMRPhenomXPHM}, the latter with the \textsc{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 \textsc{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 \textsc{IMRPhenomXPHM} can produce false GR deviations. Meanwhile, we find that \textsc{IMRPhenomTPHM} is the most reliable model under the IMR consistency test. Finally, we re-analyze the same 35 simulations, but this time we incorporate model accuracy into our Bayesian inference. Consistent with the work in Hoy et al. 2024 [arXiv: 2409.19404], we find 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.

Summary

No one has generated a summary of this paper yet.

Paper to Video (Beta)

No one has generated a video about this paper yet.

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Continue Learning

We haven't generated follow-up questions for this paper yet.

Collections

Sign up for free to add this paper to one or more collections.

Tweets

Sign up for free to view the 1 tweet with 0 likes about this paper.