The use of hypermodels to understand binary neutron star collisions

Ashton, Gregory and Dietrich, Tim (2022) The use of hypermodels to understand binary neutron star collisions. Nature Astronomy, 6, 961-967. (doi:10.48550/arXiv.2111.09214).

Record type: Article

Abstract

Gravitational waves from the collision of binary neutron stars provide a unique opportunity to study the behaviour of supranuclear matter, the fundamental properties of gravity and the cosmic history of our Universe. However, given the complexity of Einstein's field equations, theoretical models that enable source–property inference suffer from systematic uncertainties due to simplifying assumptions. We develop a hypermodel approach to compare and measure the uncertainty of gravitational-wave approximants. Using state-of-the-art models, we apply this new technique to the binary neutron star observations GW170817 and GW190425 and to the sub-threshold candidate GW200311_103121. Our analysis reveals subtle systematic differences (with Bayesian odds of ~2) between waveform models. A frequency-dependence study suggests that this may be due to the treatment of the tidal sector. This new technique provides a proving ground for model development and a means to identify waveform systematics in future observing runs where detector improvements will increase the number and clarity of binary neutron star collisions we observe.

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More information

Accepted/In Press date: 11 May 2022

e-pub ahead of print date: 4 August 2022

Published date: August 2022

Additional Information: Funding Information: We thank S. Akcay for valuable comments on the manuscript and comments about the tidal sector of the TEOBResumS model. We are also grateful for discussions with S. Bernuzzi, R. Gamba and A. Nagar. Finally, we thank J. Tissino for pointing out a mistake in equation () in an early draft of this work. All nested sampling analyses made use of the dynesty package and the higher-order mode analysis of TEOBResumS additionally used the massively parallelized software Parallel Bilby. G.A. thanks the UKRI Future Leaders Fellowship for support through the grant MR/T01881X/1. T.D. thanks the Max Planck Society for financial support. We are grateful for computational resources provided by Cardiff University and funded by an STFC grant ST/I006285/1 supporting UK Involvement in the Operation of Advanced LIGO. This work makes use of the SciPy and NumPy packages for data analysis and visualization. – Publisher Copyright: © 2022, The Author(s), under exclusive licence to Springer Nature Limited.