Composite biasing in Monte Carlo radiative transfer
Journal article, 2016

Biasing or importance sampling is a powerful technique in Monte Carlo radiative transfer, and can be applied in different forms to increase the accuracy and efficiency of simulations. One of the drawbacks of the use of biasing is the potential introduction of large weight factors. We discuss a general strategy, composite biasing, to suppress the appearance of large weight factors. We use this composite biasing approach for two different problems faced by current state-of-the-art Monte Carlo radiative transfer codes: the generation of photon packages from multiple components, and the penetration of radiation through high optical depth barriers. In both cases, the implementation of the relevant algorithms is trivial and does not interfere with any other optimisation techniques. Through simple test models, we demonstrate the general applicability, accuracy and efficiency of the composite biasing approach. In particular, for the penetration of high optical depths, the gain in efficiency is spectacular for the specific problems that we consider: in simulations with composite path length stretching, high accuracy results are obtained even for simulations with modest numbers of photon packages, while simulations without biasing cannot reach convergence, even with a huge number of photon packages.

Radiative transfer


Maarten Baes

Ghent university

K. D. Gordon

Ghent university

Space Telescope Science Institute (STScI)

Tuomas Lunttila

Chalmers, Earth and Space Sciences, Radio Astronomy and Astrophysics

Simone Bianchi

Arcetri Astrophysical Observatory

Peter Camps

Ghent university

Mika Juvela

University of Helsinki

Rolf Kuiper

University of Tübingen

Astronomy and Astrophysics

0004-6361 (ISSN) 1432-0746 (eISSN)

Vol. 590 Art. no. A55- A55

Subject Categories

Astronomy, Astrophysics and Cosmology


Basic sciences



More information

Latest update