Biased parameter inference of eccentric, spin-precessing binary black holes

Divyajyoti, Divyajyoti ORCID: https://orcid.org/0000-0002-2787-1012, Romero-Shaw, Isobel M. ORCID: https://orcid.org/0000-0002-4181-8090, Prasad, Vaishak, Paul, Kaushik, Mishra, Chandra Kant, Kumar, Prayush, Maurya, Akash, Boyle, Michael, Kidder, Lawrence E., Pfeiffer, Harald P. and Scheel, Mark A. 2026. Biased parameter inference of eccentric, spin-precessing binary black holes. Physical Review D (particles, fields, gravitation, and cosmology) 113 (10) , 103022. 10.1103/jdkc-65lz

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Abstract

While the majority of gravitational wave (GW) events observed by the LIGO and Virgo detectors are consistent with mergers of binary black holes (BBHs) on quasicircular orbits, some events are also consistent with nonzero orbital eccentricity, indicating that the binaries could have formed via dynamical interactions. Moreover, there may be GW events which show support for spin precession, eccentricity, or both. In this work, we study the interplay of spins and eccentricity on the parameter estimation of GW signals from BBH mergers. We inject eccentric signals with no spins, aligned spins, and precessing spins using hybrids, -, and new numerical relativity simulations, respectively, and study the biases in the posteriors of source parameters when these signals are recovered with a quasicircular precessing-spin waveform model, as opposed to an aligned-spin eccentric waveform model. We find significant biases in the source parameters, such as chirp mass and spin precession ( χ p ), when signals from highly eccentric BBHs are recovered with a quasicircular waveform model. Moreover, we find that for signals with both eccentricity and spin-precession effects, Bayes factor calculations confirm that an eccentric, aligned-spin model is preferred over a quasicircular precessing-spin model. Our study highlights the complex nature of GW signals from eccentric, precessing-spin binaries and the need for readily usable inspiral-merger-ringdown eccentric, spin-precessing waveform models for unbiased parameter estimation.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Physics and Astronomy
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Start Date: 2026-05-12
Publisher:	American Physical Society
ISSN:	2470-0010
Date of First Compliant Deposit:	27 May 2026
Date of Acceptance:	13 April 2026
Last Modified:	07 Aug 2026 22:31
URI:	https://orca.cardiff.ac.uk/id/eprint/187242

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