SEOBNRv6EHM is a multipolar EOB model for eccentric planar-orbit BBHs calibrated to NR simulations, showing low waveform mismatches up to eccentricity 0.9.
Mixed citations
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Mixed citation behavior. Most common role is background (62%).
9
Pith papers citing it
Background
62% of classified citations
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background 5
method 2
extension 1
citation-polarity summary
representative citing papers
The gwNRHME framework constructs a multi-modal non-spinning eccentric gravitational waveform surrogate by modulating quasi-circular models with universal eccentric functions, achieving median mismatches of ~9e-5 against 156 NR waveforms.
Polynomial models for the (2,2) post-merger waveform amplitudes of eccentric non-spinning binary black holes are constructed from numerical-relativity data as functions of symmetric mass ratio and two merger-time dynamical parameters.
Two new surrogate models, trained on NR simulations, predict remnant properties and eccentricity dynamics for nonspinning eccentric black hole binaries with q ≤ 4 and e < 0.23.
Neural network surrogate approximates precessing compact binary gravitational waveforms up to 1000x faster than the base EOB model with validated accuracy.
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.
Eccentric BBH signals recovered with quasi-circular precessing models show biases in chirp mass and χ_p; Bayes factors favor eccentric aligned-spin models when both eccentricity and precession are present.
Two asymmetric BBH mergers are characterized with mass ratios 0.35 and ≤0.20; one shows high spins, negative χ_eff, and strong precession, suggesting an emerging population of massive rapidly spinning systems.
Residuals after subtracting best-fit waveforms from GWTC-3 events show no significant deviation from noise according to three standard goodness-of-fit tests.
citing papers explorer
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Fast neural network surrogate for multimodal effective-one-body gravitational waveforms from generically precessing compact binaries
Neural network surrogate approximates precessing compact binary gravitational waveforms up to 1000x faster than the base EOB model with validated accuracy.
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GW190711_030756 and GW200114_020818: astrophysical interpretation of two asymmetric binary black hole mergers in the IAS catalog
Two asymmetric BBH mergers are characterized with mass ratios 0.35 and ≤0.20; one shows high spins, negative χ_eff, and strong precession, suggesting an emerging population of massive rapidly spinning systems.