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

    Anna Puecher1,2, Anuradha Samajdar2,3, Gregory Ashton4, Chris Van Den Broeck1,2, and Tim Dietrich3,5

    • 1Nikhef—National Institute for Subatomic Physics, Science Park 105, 1098 XG Amsterdam, Netherlands
    • 2Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University, Princetonplein 1, 3584 CC Utrecht, Netherlands
    • 3Institut für Physik und Astronomie, Universität Potsdam, Haus 28, Karl-Liebknecht-Str. 24/25, 14476, Potsdam, Germany
    • 4Royal Holloway, University of London, London TW20 0EX, United Kingdom
    • 5Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Am Mühlenberg 1, Potsdam 14476, Germany

    Phys. Rev. D 109, 023019 – Published 11 January, 2024

    DOI: https://doi.org/10.1103/PhysRevD.109.023019

    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 favored 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. We also highlight that the models are not used at their full capabilities since, in order to compare them, we consider only the subdominant modes present in all of them. 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.

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