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Abstract

Gravitational-wave detectors have begun to observe coalescences of heavy
black holes at a consistent pace for the past few years. Accurate models of
gravitational waveforms are essential for unbiased and precise estimation of
the source parameters, such as masses and spins of component black holes.
Recently developed surrogate models based on high-accuracy numerical relativity
simulations provide ideal models for constraining physical parameters
describing these heavy black hole merger events. In this paper we demonstrate
the viability of these surrogate models as reliable parameter estimation tools,
and show that within a fully Bayesian framework surrogates can help us extract
more information from gravitational wave observations than traditional models.
We demonstrate this by analyzing a set of synthetic signals and showing the
improvement that the use of numerical relativity surrogates bring to our
parameter estimates. We then consider the case of two of the earliest binary
black holes detected by the LIGO observatories, GW150914 and GW170104, and
reanalyze their data with a generically precessing numerical-relativity-based
surrogate model. For these systems we find that overall results are
quantitatively consistent with inferences performed with conventional models,
except that our refined analysis estimates the sources of both GW150914 and
GW170104 to be $10-20\%$ further away than previously estimated and constrain
their orientation to be closer to either face-on or face-off configurations
more strongly than in the past. Additionally, for GW150914 we constrain the
effective spin parameter to be closer to zero. This work is a first step toward
eliminating the approximations used in semi-analytic waveform models from GW
parameter estimation.