  Many optimization techniques have been invented to reduce the noise that is
inherent in Monte Carlo radiative transfer simulations. As the typical
detectors used in Monte Carlo simulations do not take into account all the
information contained in the impacting photon packages, there is still room to
optimize this detection process and the corresponding estimate of the surface
brightness distributions. We want to investigate how all the information
contained in the distribution of impacting photon packages can be optimally
used to decrease the noise in the surface brightness distributions and hence to
increase the efficiency of Monte Carlo radiative transfer simulations.
  We demonstrate that the estimate of the surface brightness distribution in a
Monte Carlo radiative transfer simulation is similar to the estimate of the
density distribution in an SPH simulation. Based on this similarity, a recipe
is constructed for smart detectors that take full advantage of the exact
location of the impact of the photon packages. Several types of smart
detectors, each corresponding to a different smoothing kernel, are presented.
We show that smart detectors, while preserving the same effective resolution,
reduce the noise in the surface brightness distributions compared to the
classical detectors. The most efficient smart detector realizes a noise
reduction of about 10%, which corresponds to a reduction of the required number
of photon packages (i.e. a reduction of the simulation run time) of 20%. As the
practical implementation of the smart detectors is straightforward and the
additional computational cost is completely negligible, we recommend the use of
smart detectors in Monte Carlo radiative transfer simulations.
