Abstract
The theory and numerical modelling of radiation processes and radiative transfer play a key role in astrophysics: they provide the link between the physical properties of an object and the radiation it emits. In the modern era of increasingly high-quality observational data and sophisticated physical theories, development and exploitation of a variety of approaches to the modelling of radiative transfer is needed. In this article, we focus on one remarkably versatile approach: Monte Carlo radiative transfer (MCRT). We describe the principles behind this approach, and highlight the relative ease with which they can (and have) been implemented for application to a range of astrophysical problems. All MCRT methods have in common a need to consider the adverse consequences of Monte Carlo noise in simulation results. We overview a range of methods used to suppress this noise and comment on their relative merits for a variety of applications. We conclude with a brief review of specific applications for which MCRT methods are currently popular and comment on the prospects for future developments.
Highlights
1.1 The role of radiative transfer in astrophysicsMuch of astrophysics is at a disadvantage compared to other fields of physics
Adopting the parameters suggested by Abdikamalov et al (2012) and listed in Appendix A.1, we perform a simple time-independent Monte Carlo radiative transfer (MCRT) simulation in spherical symmetry, injecting packets according to the local emissivity and following them until they either escape from the computational domain or are absorbed
We provide an overview of some of the MCRT techniques used in astrophysics
Summary
Much of astrophysics is at a disadvantage compared to other fields of physics. While normally theories can be tested and phenomena studied by performing repeatable experiments in the controlled environment of a lab, astrophysics generally lacks this luxury. Instead of discretizing the RT equations, the underlying RT process is “simulated” by introducing a large number of “test particles” (later referred to as “packets” in this article) These test particles behave in a manner similar to their physical counterparts, namely real photons. The most severe disadvantage is a direct consequence of the probabilistic nature of MC techniques: inevitably, any physical quantity extracted from MC calculations will be subject to stochastic fluctuations This MC noise can be decreased by increasing the number of particles, which naturally requires more computational resources.
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