The first informative astrophysical calibration of gravitational-wave detectors is reported using GW240925 and GW250207.
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Tests of general relativity with gravitational-wave observations us- ing a flexible theory-independent method
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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.