# Monte Carlo Radiative Transfer

## TL;DR

Monte Carlo Radiative Transfer (MCRT) is a versatile computational approach in astrophysics that models radiation processes, with ongoing developments to reduce noise and expand applications in high-quality observational data analysis.

## Contribution

This paper reviews the principles, implementation, noise reduction techniques, and diverse astrophysical applications of Monte Carlo Radiative Transfer methods.

## Key findings

- MCRT effectively models complex astrophysical radiation processes.
- Various noise suppression techniques improve simulation accuracy.
- MCRT is widely applied in current astrophysical research.

## 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.