Composite biasing in Monte Carlo radiative transfer

TL;DR

This paper introduces composite biasing in Monte Carlo radiative transfer to reduce large weight factors, improving accuracy and efficiency in simulations involving multiple components and high optical depths.

Contribution

It presents a simple, effective composite biasing strategy applicable to various radiative transfer problems, enhancing simulation accuracy and efficiency.

Findings

Significant efficiency gains in high optical depth simulations.

Effective suppression of large weight factors.

Applicability to multiple radiative transfer scenarios.

Abstract

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.