ARlogo Annu. Rev. Astron. Astrophys. 2013. 51:63-104
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8.1. Present Status

3D dust RT is a rich and diverse field, with applications across a broad range of astrophysical topics from dust near stars to entire galaxies. Correctly modeling the effects of dust on the transfer of radiation is critical to studying many astrophysical objects, including the dust itself. Recent years have seen an impressive improvement in observational capabilities across the electromagnetic spectrum, and this has shown that the dust distribution in many regions is strongly 3D. This requires methods to compute the dust RT that can handle 3D structures and return solutions in a reasonable amount of time. The most common 3D dust RT solver is based on MC techniques, with RayT features in its modern accelerated form. A few applications have used pure RayT solvers. Both methods face the challenges of grid discretization, determination of uncertainties in solutions, and accurate comparison between observations and the model calculations. Almost 30 codes are currently able to deal with the full 3D dust RT, with code variations arising from the prime field of application. There is no 3D dust RT benchmark; currently, code comparisons are done using 2D benchmarks.

8.2. General Trends

Several trends indicate that the future of 3D dust RT is bright. The number of people actively involved in 3D dust RT is growing, and the number of new published codes has increased significantly in recent years. A 3D approach to modeling complex distributions is becoming common in many fields requiring 3D dust distributions. The continuing increase in available computing power and storage will support this trend, allowing a full transition from 2D to 3D dust RT for all objects showing 3D signatures. A prominent example of this trend is circumstellar disks with (proto)planets, where the MHD simulations have been 3D for years, dust RT modeling often was 2D, and observations are now reaching the resolution necessary to identify the 3D signatures of disk deformation due to a planet. In addition, modern online tools are expected to support the access to the codes by users through sophisticated interfaces.

8.3. Future Benchmarks

For progress in 3D dust RT to continue, 3D dust RT benchmarks need to be established. Given the complexity of the codes, increasing number of acceleration algorithms, and large number of specific applications, it is critical to provide a quantitative comparison between codes. Experience with existing dust RT benchmarks and similar efforts in other areas indicate a suite of 3D dust RT benchmarks is needed. Ideally, each benchmark would focus on a particular part of the RT solution (e.g., scattering, polarization, equilibrium dust emission, or nonequilibrium dust emission) in a 3D geometry. This would provide a clear test of different aspects of 3D dust RT and support the participation of all codes in at least part of the suite.

8.4. Data Modeling Future

Given the impressive flow of new data from ground- and space-based observatories now and projected for the coming years, it is clear that the demand to accurately model 3D dusty structures will rise strongly. Interfaces that can simulate observations with different telescope properties will become necessary to perform modeling. We expect a rise of 3D dust RT modeling efforts that rely on automated fitting processes rather than by-hand explorations of the model parameter space. Because the number of sources of multiwavelength data will rise, collaborations between observers and modelers will become more frequent. The ultimate goal of 3D dust calculations is to model multiwavelength images and derive quantitative and statistically sound information about 3D structures, embedded sources, and the dust itself.

8.5. Future Connections to Nondust Radiative Transfer Codes

Another future direction is the coupling of 3D dust RT codes with codes describing other physical effects in astrophysical objects. This trend is already happening with 2D dust RT codes, and the extension to 3D dust RT codes is clearly the next step. A variant of this type of connection is already happening where 3D dust RT is used to calculate the radiation field in a dust distribution generated with an MHD code; furthermore, MHD codes that make use of simple dust RT could be tested or the simple algorithms improved by comparison with full 3D dust RT solutions. Chemical network calculations could be based on a more realistic estimate of the incoming radiation calculated from 3D dust codes. Finally, a combined calculation for 3D line and dust RT would enable line and continuum data to be simultaneously investigated using the same underlying physical model.

8.6. Future Algorithms

Conferences and keyword-related publication searches have often been used in the past to improve the unfortunately rare communication of new numerical algorithms from applied mathematics to astrophysics. The basic issue is the sheer flow of new findings and the different languages of the two communities. Recent MC improvements have been developed mainly by coders working in the field, and additional efforts should be made to enable community-crossing exchange on algorithms and error control. As a result of communications between coders preferring different solvers, we expect hybrid solvers making use of the advantages of the various approaches to appear more frequently. Given the increase in complexity in the modeled objects, we expect future activities to establish grid generation algorithms that are optimized for 3D dust RT; besides the octree or AMR-style grids that are now routinely implemented in 3D dust RT codes, unstructured grids as used in line RT (Paardekooper, Kruip & Icke 2010), and MHD codes (Springel 2010) are an interesting alternative. The inclusion of time dependence in the 3D RT problem, which could be important in the context of star formation or episodic accretion, will also need to be tackled with new algorithms (see, e.g., Harries 2011). The increasing availability of massively parallel machines will support algorithms that are optimized to run on many processors.

8.7. Input Physics Improvements

The improvement of the solvers is not restricted to developing algorithms that provide accelerated solutions. The interaction of radiation with cosmic dust is still not fully understood, and the variation of the dust properties with environment is an area of active research. The various continuum radiation sources such as stars, PDRs, AGN accretion discs, and the interstellar radiation field are areas of vigorous investigation. For example, efforts based on existing and upcoming large-scale surveys are being made to update the 3D structure of the stars in the Milky Way. Consequently, we expect to achieve a better understanding of the observed radiation from future research on the optical properties of dust and improved data on the stellar and nonstellar sources that enter the 3D dust RT equation.

8.8. Challenges

A major challenge in 3D dust RT that this review highlights is how to account for and mitigate systematic uncertainties in the dust RT solution. They arise from under-resolving grids, not propagating rays/photons to important cells, and/or uncertainties in the underlying dust grain models. As under-resolving of the dust and radiation field grid is often a result of constraints on computer memory and speed, improvements in algorithms to implicitly handle optimal grids are needed. The preprocessing steps necessary for the RayT solver address some of these issues, but need further automating. The issue of not propagating enough rays/photons into particular cells has been solved for both RayT (placement of rays) and MC (biased emission), but both currently require hand-tuning. An algorithm to automatically add additional rays/photons similar to that used for AMR would clearly be useful. Finally, uncertainties in the assumed dust grain model provide a systematic uncertainty in the dust RT modeling that is difficult to quantify. Different dust grain models can be used to provide an estimate of this uncertainty, but the best way to reduce this uncertainty is to support the improvement of dust grain models through the use of improved laboratory and observational data.


The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.


The authors acknowledge the support of the Ghent University for two excellent week-long meetings in Ghent, Belgium, where a large portion of the work on this review was done. We thank Simon Bruderer, Jacopo Fritz, Gianfranco Gentile, Michiel Min, Kirill Tchernyshyov, Ewine van Dishoeck, and Adolf Witt for providing comments on this review that significantly improved it.

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