  Likelihood surfaces in the parameter space of gravitational wave signals can
contain many secondary maxima, which can prevent search algorithms from finding
the global peak and correctly mapping the distribution. Traditional schemes to
mitigate this problem maintain the number of secondary maxima and thus retain
the possibility that the global maximum will remain undiscovered. By contrast,
the recently proposed technique of likelihood transform can modify the
structure of the likelihood surface to reduce its complexity. We present a
practical method to carry out a likelihood transform using a Gaussian smoothing
kernel, utilising gravitational wave surrogate models to perform the smoothing
operation analytically. We demonstrate the approach with Newtonian and
post-Newtonian waveform models for an inspiralling circular compact binary.
