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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IMRPhenomXPHM is a new computationally efficient phenomenological model for precessing binary black hole gravitational-wave signals that incorporates higher-order modes via twisting-up maps from non-precessing waveforms.
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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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Computationally efficient models for the dominant and sub-dominant harmonic modes of precessing binary black holes
IMRPhenomXPHM is a new computationally efficient phenomenological model for precessing binary black hole gravitational-wave signals that incorporates higher-order modes via twisting-up maps from non-precessing waveforms.