Bayesian analysis of GW230627 and GW250114 finds no deviation from GR at 4PN and 4.5PN orders, setting the first empirical baseline with 90% intervals of order O(1)-O(10).
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Tests of general relativity with gravitational-wave observations us- ing a flexible theory-independent method
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abstract
We perform tests of General Relativity (GR) with gravitational waves (GWs) from the inspiral stage of compact binaries using a theory-independent framework, which adds generic phase corrections to each multipole of a GR waveform model in frequency domain. This method has been demonstrated on LIGO-Virgo observations to provide stringent constraints on post-Newtonian predictions of the inspiral and to assess systematic biases that may arise in such parameterized tests. Here, we detail the anatomy of our framework for aligned-spin waveform models. We explore the effects of higher modes in the underlying signal on tests of GR through analyses of two unequal-mass, simulated binary signals similar to GW190412 and GW190814. We show that the inclusion of higher modes improves both the precision and the accuracy of the measurement of the deviation parameters. Our testing framework also allows us to vary the underlying baseline GR waveform model and the frequency at which the non-GR inspiral corrections are tapered off. We find that to optimize the GR test of high-mass binaries, comprehensive studies would need to be done to determine the best choice of the tapering frequency as a function of the binary's properties. We also carry out an analysis on the binary neutron-star event GW170817 to set bounds on the coupling constant $\alpha_0$ of Jordan-Fierz-Brans-Dicke gravity. We take two plausible approaches; in the first \emph{theory-agnostic} approach we find a bound $\alpha_0 \lesssim 2\times 10^{-1}$ from measuring the dipole-radiation for different neutron-star equations of state, while in the second \emph{theory-specific} approach we obtain $\alpha_0 \lesssim 4\times 10^{-1}$, both at $68\%$ credible level. These differences arise mainly due to different statistical hypotheses used for the analysis.
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gr-qc 14representative citing papers
A mixture-model framework for combining gravitational-wave tests of General Relativity yields Bayes factors of 10-20 favoring GR, substantially lower than existing methods that assume uniform deviations across events.
GW250114 data constrains GR deviations in merger amplitude to 10% and frequency to 4% at 90% CL, with first bounds on the (4,4) mode frequency at 6%.
GW250114 data confirm the remnant is consistent with a Kerr black hole and bound the dominant quadrupolar mode frequency to within a few percent of the GR prediction, with constraints tighter than prior multi-event catalogs.
Bayesian inference on LVK O1-O3 events with eccentric aligned-spin waveforms yields log10 Bayes factors of 1.77-4.75 favoring eccentricity for GW200129, GW190701 and GW200208_22, and >99.5% probability that at least one of 57 events is eccentric under an astrophysically motivated rate prior.
A CNN framework using response functions from gravitational wave mismatches classifies signals as GR or beyond-GR with 33 times better sensitivity than raw waveforms and detects massive gravity deviations at graviton masses around 10^{-23} eV/c².
The prompt response is ~1.2 times stronger than quasinormal mode excitation during inspiral and enables 99% accurate reconstruction of the full inspiral-merger-ringdown waveform when combined with other components.
Neural post-Einsteinian analysis of GWTC-3 finds no GR violation and sets constraints covering both post-Newtonian and beyond-post-Newtonian deviations in a single theory-agnostic setup.
Parameterized inspiral tests on GW230529 find consistency with GR, with |δφ̂_{-2}| ≲ 8×10^{-5} and ℓ_GB ≲ 0.51 M_⊙ in ESGB theories.
Relative binning accelerates TIGER parameterized GR tests by factors of 10-100 while recovering unbiased posteriors on simulated signals and real events like GW150914.
GWKokab is a new modular JAX framework that uses normalizing flow samplers for efficient inference on subpopulations of compact binary mergers.
No evidence for physics beyond general relativity is found in the analysis of 15 GW events from GWTC-3, with consistency in residuals, PN parameters, and remnant properties.
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Testing General Relativity Through Gravitational Wave Classification: A Convolutional Neural Network Framework
A CNN framework using response functions from gravitational wave mismatches classifies signals as GR or beyond-GR with 33 times better sensitivity than raw waveforms and detects massive gravity deviations at graviton masses around 10^{-23} eV/c².