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Simulating a binary black hole (BBH) coalescence by solving Einstein'sequations is computationally expensive, requiring days to months ofsupercomputing time. Using reduced order modeling techniques, we construct anaccurate surrogate model, which is evaluated in a millisecond to a second, fornumerical relativity (NR) waveforms from non-spinning BBH coalescences withmass ratios in $[1, 10]$ and durations corresponding to about $15$ orbitsbefore merger. We assess the model's uncertainty and show that our modelingstrategy predicts NR waveforms {\em not} used for the surrogate's training witherrors nearly as small as the numerical error of the NR code. Our modelincludes all spherical-harmonic ${}_{-2}Y_{\ell m}$ waveform modes resolved bythe NR code up to $\ell=8.$ We compare our surrogate model to Effective OneBody waveforms from $50$-$300 M_\odot$ for advanced LIGO detectors and findthat the surrogate is always more faithful (by at least an order of magnitudein most cases).

Fast and accurate prediction of numerical relativity waveforms from binary black hole coalescences using surrogate models