A Deep Learning Powered Numerical Relativity Surrogate for Binary Black Hole Waveforms
This work provides a computationally efficient tool for gravitational-wave data analysis, enabling faster parameter estimation, but it is incremental as it builds on existing surrogate modeling approaches.
The paper tackles the problem of generating accurate binary black hole waveforms for gravitational-wave astronomy by introducing a dual-stage neural network surrogate model that reduces the accuracy-efficiency trade-off, achieving mean mismatches with numerical relativity around 10^-4 and generating millions of waveforms in under 20ms on a GPU.
Gravitational-wave approximants are essential for gravitational-wave astronomy, allowing the coverage binary black hole parameter space for inference or match filtering without costly numerical relativity (NR) simulations, but generally trading some accuracy for computational efficiency. To reduce this trade-off, NR surrogate models can be constructed using interpolation within NR waveform space. We present a 2-stage training approach for neural network-based NR surrogate models. Initially trained on approximant-generated waveforms and then fine-tuned with NR data, these dual-stage artificial neural surrogate (\texttt{DANSur}) models offer rapid and competitively accurate waveform generation, generating millions in under 20ms on a GPU while keeping mean mismatches with NR around $10^{-4}$. Implemented in the \textsc{bilby} framework, we show they can be used for parameter estimation tasks.