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Comparing gravitational waveform models for binary black hole mergers through a hypermodels approach

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Dietrich,  Tim
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;
Multi-messenger Astrophysics of Compact Binaries, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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Citation

Puecher, A., Samajdar, A., Ashton, G., Broeck, C. V. D., & Dietrich, T. (2024). Comparing gravitational waveform models for binary black hole mergers through a hypermodels approach. Physical Review D, 109(2): 023019. doi:10.1103/PhysRevD.109.023019.


Cite as: https://hdl.handle.net/21.11116/0000-000D-CC6E-3
Abstract
The inference of source parameters from gravitational-wave signals relies on
theoretical models that describe the emitted waveform. Different model
assumptions on which the computation of these models is based could lead to
biases in the analysis of gravitational-wave data. In this work, we sample
directly on four state-of-the-art binary black hole waveform models from
different families, in order to investigate these systematic biases from the 13
heaviest gravitational-wave sources with moderate to high signal-to-noise
ratios in the third Gravitational-Wave Transient Catalog (GWTC- 3). All models
include spin-precession as well as higher-order modes. Using the "hypermodels"
technique, we treat the waveform models as one of the sampled parameters,
therefore directly getting the odds ratio of one waveform model over another
from a single parameter estimation run. From the joint odds ratio over all 13
sources, we find the model NRSur7dq4 to be favoured over SEOBNRv4PHM, with an
odds ratio of 29.43; IMRPhenomXPHM and IMRPhenomTPHM have an odds ratio,
respectively, of 4.70 and 5.09 over SEOBNRv4PHM. However, this result is mainly
determined by three events that show a strong preference for some of the models
and that are all affected by possible data quality issues. If we do not
consider these potentially problematic events, the odds ratio do not exhibit a
significant preference for any of the models. Although further work studying a
larger set of signals will be needed for robust quantitative results, the
presented method highlights one possible avenue for future waveform model
development.