A parametrized model for gravitational waves from eccentric, precessing binary black holes: theory-agnostic tests of General Relativity with pTEOBResumS

Abstract

Gravitational waves from binary black hole (BBH) mergers allow us to test general relativity in the strong-field, high-curvature regime. However, existing gravitational wave-based tests have so far assumed non-eccentric signal sources, limiting their applicability to more general astrophysical scenarios. In this work, we present pTEOBResumS, a new parametrized inspiral-merger-ringdown model for null tests of GR that incorporates both orbital eccentricity and spin precession. Building on the effective-one-body model TEOBResumS-Dal\'i, we introduce parametrized deviations from GR both in the inspiral and the merger-ringdown regimes. We validate the model via parameter estimation of synthetic signals, including from numerical relativity simulations of BBHs and a boson star binary. These allow us to establish the model's consistency, demonstrate its capability to identify beyond-GR effects, and gauge the impact of eccentricity in tests of GR. We then analyze a set of BBH events from the first three LIGO-Virgo-KAGRA observing runs, testing whether they are best explained by a GR or non-GR waveform, under either the eccentric, spin-aligned or precessing, quasi-circular hypotheses. We find no significant statistical evidence in favor of deviations from GR. Consistent with previous works, we infer a mild preference for longer remnant quasi-normal mode damping times than expected in GR, though the limited sample and potential systematics reduce its significance. In addition, when weighting by signal strength, joint posteriors combining the individual events are still compatible with GR. We find no strong evidence for imprints of orbital eccentricity in the analyzed events, with the exception of GW200129. For this, our analysis finds a strong preference for an eccentric, GR-consistent description, although as previous works have noted this result could be influenced by data quality issues.