Radiative transfer on hierarchial grids

TL;DR

This paper introduces new hierarchical grid methods for radiative transfer, improving computational efficiency and accuracy in modeling complex astrophysical structures like molecular clouds and circumstellar discs.

Contribution

The paper presents novel algorithms for radiative transfer on hierarchical grids, including a new scattered flux calculation method and a subiteration algorithm for faster convergence.

Findings

Adaptive models achieve <8% cell usage with good accuracy.

Speed-up factors of 4 for scattered flux calculation and 2 for subiteration.

High accuracy with <4% RMS error in surface brightness.

Abstract

We present new methods for radiative transfer on hierarchial grids. We develop a new method for calculating the scattered flux that employs the grid structure to speed up the computation. We describe a novel subiteration algorithm that can be used to accelerate calculations with strong dust temperature self-coupling. We compute two test models, a molecular cloud and a circumstellar disc, and compare the accuracy and speed of the new algorithms against existing methods. An adaptive model of the molecular cloud with less than 8 % of the cells in the uniform grid produced results in good agreement with the full resolution model. The relative RMS error of the surface brightness <4 % at all wavelengths, and in regions of high column density the relative RMS error was only 10^{-4}. Computation with the adaptive model was faster by a factor of ~5. The new method for calculating the scattered flux is faster by a factor of ~4 in large models with a deep hierarchy structure, when images of the scattered light are computed towards several observing directions. The efficiency of the subiteration algorithm is highly dependent on the details of the model. In the circumstellar disc test the speed-up was a factor of two, but much larger gains are possible. The algorithm is expected to be most beneficial in models where a large number of small, dense regions are embedded in an environment with a lower mean density.