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Statistical Gravitational Waveform Models: What to Simulate Next?

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Models of gravitational waveforms play a critical role in detecting and characterizing the gravitational waves (GWs) from compact binary coalescences.  Waveforms from numerical relativity (NR), while highly accurate, are too computationally expensive to produce to be directly used in parameter estimation. We propose a Gaussian process regression (GPR) method to generate accurate reduced-order-model waveforms based only on existing accurate (e.g. NR) simulations. Using a training set of simulated waveforms, our GPR approach produces interpolated waveforms along with uncertainties across the parameter space. Beyond interpolation of waveforms, we also present the ``Minimization of the Maximum Estimated Error Placement'' (MMEEP) method, which utilizes the errors provided by our GPR model to optimize the placement of future simulations.