Multiband observations of eccentric binary black holes can constrain dipole-radiation deviations from general relativity to |b| ≲ 10^{-7} for a GW231123-like event when combining one year of space-based data with ground-informed priors.
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GreyRing model based on greybody factors reproduces numerical relativity ringdown signals with mismatches of order 10^{-6} and enables a new post-merger consistency test of general relativity applied to GW250114.
Unmodeled point-mass lensing produces a spurious nonzero graviton mass posterior in GW231123 that vanishes when lensing is included in the analysis.
Bilby introduces a user-friendly Python library for accurate Bayesian inference on gravitational-wave signals from compact binaries and other sources, including hierarchical population modeling.
Parametric models incorporating waveform phase and amplitude uncertainties mitigate systematic errors in gravitational wave parameter estimation, producing consistent results across models and raw/deglitched data for events like GW191109_010717 and GW200129_065458.
citing papers explorer
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Constraining Dipole Radiation with Multiband Gravitational Waves from Eccentric Binary Black Holes
Multiband observations of eccentric binary black holes can constrain dipole-radiation deviations from general relativity to |b| ≲ 10^{-7} for a GW231123-like event when combining one year of space-based data with ground-informed priors.
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Novel ringdown tests of general relativity with black hole greybody factors
GreyRing model based on greybody factors reproduces numerical relativity ringdown signals with mismatches of order 10^{-6} and enables a new post-merger consistency test of general relativity applied to GW250114.
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GW231123: False Massive Graviton Signatures from Unmodeled Point-Mass Lensing
Unmodeled point-mass lensing produces a spurious nonzero graviton mass posterior in GW231123 that vanishes when lensing is included in the analysis.
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Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy
Bilby introduces a user-friendly Python library for accurate Bayesian inference on gravitational-wave signals from compact binaries and other sources, including hierarchical population modeling.
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Mitigating Systematic Errors in Parameter Estimation of Binary Black Hole Mergers in O1-O3 LIGO-Virgo Data
Parametric models incorporating waveform phase and amplitude uncertainties mitigate systematic errors in gravitational wave parameter estimation, producing consistent results across models and raw/deglitched data for events like GW191109_010717 and GW200129_065458.