Smart detectors for Monte Carlo radiative transfer
Abstract
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 a smoothed particle hydrodynamics 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 per cent, which corresponds to a reduction of the required number of photon packages (i.e. a reduction of the simulation run time) of 20 per cent. 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.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 December 2008
 DOI:
 10.1111/j.13652966.2008.13941.x
 arXiv:
 arXiv:0809.1928
 Bibcode:
 2008MNRAS.391..617B
 Keywords:

 radiative transfer;
 methods: numerical;
 Astrophysics
 EPrint:
 7 pages, 5 figures, accepted for publication in MNRAS