Learning Inspiral-Merger-Ringdown Waveforms from a Post-Newtonian Baseline

arXiv:2609.29115 · gr-qc, astro-ph.HE · Submitted 2026-09-24 · Read on arXiv

gr-qc, astro-ph.HE

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Accuracy — 8× in this paper · explained in 1 episode
• Calibration — 6× in this paper · explained in 7 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Frequency Dependence — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Gauge Freedom — 1× in this paper · explained in 2 episodes
• Kolmogorov-Arnold Networks (KANs) — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• Effective one-body approach to general relativistic two-body dynamics
• Transition from inspiral to plunge in binary black hole coalescences
• Inspiral-merger-ringdown multipolar waveforms of nonspinning black-hole binaries using the effective-one-body formalism
• Inspiral-merger-ringdown waveforms for black-hole binaries with non-precessing spins
• Frequency-domain gravitational waves from non-precessing black-hole binaries. I. New numerical waveforms and anatomy of the signal
• Frequency-domain gravitational waves from non-precessing black-hole binaries. II. A phenomenological model for the advanced detector era
• Fast and accurate prediction of numerical relativity waveforms from binary black hole coalescences using surrogate models
• A Numerical Relativity Waveform Surrogate Model for Generically Precessing Binary Black Hole Mergers
• Surrogate models for precessing binary black hole simulations with unequal masses
• Statistical Gravitational Waveform Models: What to Simulate Next?
• Reduced-order modeling with artificial neurons for gravitational-wave inference
• Regression methods in waveform modeling: a comparative study
• Machine Learning Gravitational Waves from Binary Black Hole Mergers
• Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation
• Accelerating multimodal gravitational waveforms from precessing compact binaries with artificial neural networks
• Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers
• A Deep Learning Powered Numerical Relativity Surrogate for Binary Black Hole Waveforms
• Learning orbital dynamics of binary black hole systems from gravitational wave measurements
• Learning Post-Newtonian Corrections from Numerical Relativity
• KAN: Kolmogorov-Arnold Networks

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