DIES: Parallel dust radiative transfer program with the immediate re-emission method

Department of Physics, PO Box 64, University of Helsinki, 00014 Helsinki, Finland

Received: 8 July 2024
Accepted: 10 August 2024

Abstract

Context. Radiative transfer (RT) modelling is a necessary tool in the interpretation of observations of the thermal emission of interstellar dust. It is also often part of multi-physics modelling. In this context, the efficiency of radiative transfer calculations is important, even for one-dimensional models.

Aims. We investigate the use of the so-called immediate re-emission (IRE) method for fast calculation of one-dimensional spherical cloud models. We wish to determine whether weighting methods similar to those used in traditional Monte Carlo simulations can speed up the estimation of dust temperature.

Methods. We present the program DIES, a parallel implementation of the IRE method, which makes it possible to do the calculations also on graphics processing units (GPUs). We tested the program with externally and internally heated cloud models, and examined the potential improvements from the use of different weighted sampling schemes.

Results. The execution times of the program compare favourably with previous programs, especially when run on GPUs. On the other hand, weighting schemes produce only limited improvements. In the case of an internal radiation source, the basic IRE method samples the re-emission well, while traditional Monte Carlo requires the use of spatial importance sampling. Some noise reduction could be achieved for externally heated models by weighting the initial photon directions. Only in optically very thin models does weighting – such as the proposed method of forced first interaction – result in noise reduction by a factor of several.

Conclusions. The IRE method performs well for both internally and externally heated models, typically without the need for any additional weighting schemes. With run times of the order of one second for our test models, the DIES program is suitable even for larger parameter studies.