  Numerical-relativity surrogate models for both black-hole merger waveforms
and remnants have emerged as important tools in gravitational-wave astronomy.
While producing very accurate predictions, their applicability is limited to
the region of the parameter space where numerical-relativity simulations are
available and computationally feasible. Notably, this excludes extreme mass
ratios. We present a machine-learning approach to extend the validity of
existing and future numerical-relativity surrogate models toward the
test-particle limit, targeting in particular the mass and spin of post-merger
black-hole remnants. Our model is trained on both numerical-relativity
simulations at comparable masses and analytical predictions at extreme mass
ratios. We extend the gaussian-process-regression model NRSur7dq4Remnant,
validate its performance via cross validation, and test its accuracy against
additional numerical-relativity runs. Our fit, which we dub
NRSur7dq4EmriRemnant, reaches an accuracy that is comparable to or higher than
that of existing remnant models while providing robust predictions for
arbitrary mass ratios.
