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Register a new userLOC program for line radiative transfer
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M. Juvela
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Radiative transfer modelling is part of many astrophysical simulations and is used to make synthetic observations and to assist analysis of observations. We concentrate on the modelling of the radio lines emitted by the interstellar medium. In connection with high-resolution models, this can be significant computationally challenge. Our goal is a line radiative transfer (RT) program that makes good use of multi-core CPUs and GPUs. Parallelisation is essential to speed up computations and to enable the tackling of large modelling tasks with personal computers. The program LOC is based on ray-tracing and uses standard accelerated lambda iteration (ALI) methods for faster convergence. The program works on 1D and 3D grids. The 1D version makes use of symmetries to speed up the RT calculations. The 3D version works with octree grids and, to enable calculations with large models, is optimised for low memory usage. Tests show that LOC gives results that are in agreement with other RT codes to within ~2%. This is typical of code-to-code differences, which often are related to different interpretations of the model set-up. LOC run times compare favourably with those of Monte Carlo codes. In 1D tests, LOC runs were by up to a factor ~20 faster on a GPU than on a single CPU core. In spite of the complex path calculations, up to ~10 speed-up was observed also for 3D models using octree discretisation. GPUs enable calculations of models with hundreds of millions of cells, as encountered in the context of large-scale simulations of interstellar clouds. LOC shows good performance and accuracy and and is able to handle many RT modelling tasks on personal computers. Being written in Python, with the computing-intensive parts implemented as compiled OpenCL kernels, it can also a serve as a platform for further experimentation with alternative RT implementations.
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Thermal dust emission carries information on physical conditions and dust properties in many astronomical sources. Because observations represent a sum of emission along the line of sight, their interpretation often requires radiative transfer modelling. We describe a new radiative transfer program SOC for computations of dust emission and examine its performance in simulations of interstellar clouds with external and internal heating. SOC implements the Monte Carlo radiative transfer method as a parallel program for shared memory computers. It can be used to study dust extinction, scattering, and emission. We tested SOC with realistic cloud models and examined the convergence and noise of the dust temperature estimates and of the resulting surface brightness maps. SOC has been demonstrated to produces accurate estimates for dust scattering and for thermal dust emission. It performs well with both with CPUs and with GPUs, the latter providing up to an order of magnitude speed-up. In the test cases, ALI improved the convergence rates but also was sensitive to Monte Carlo noise. Run-time refinement of the hierarchical-grid models did not help in reducing the run times required for a given accuracy of solution. The use of a reference field, without ALI, works more robustly. It also allows the run time to be optimised if the number of photon packages is increased only as the iterations progress. The use of GPUs in radiative transfer computations should be investigated further.
Context. Magnetic fields are important to the dynamics of many astrophysical processes and can typically be studied through polarization observations. Polarimetric interferometry capabilities of modern (sub)millimeter telescope facilities have made it possible to obtain detailed velocity resolved maps of molecular line polarization. To properly analyze these for the information they carry regarding the magnetic field, the development of adaptive three-dimensional polarized line radiative transfer models is necessary. Aims. We aim to develop an easy-to-use program to simulate the polarization maps of molecular and atomic (sub)millimeter lines in magnetized astrophysical regions, such as protostellar disks, circumstellar envelopes, or molecular clouds. Methods. By considering the local anisotropy of the radiation field as the only alignment mechanism, we can model the alignment of molecular or atomic species inside a regular line radiative transfer simulation by only making use of the converged output of this simulation. Calculations of the aligned molecular or atomic states can subsequently be used to ray trace the polarized maps of the three-dimensional simulation. Results. We present a three-dimensional radiative transfer code, POlarized Radiative Transfer Adapted to Lines (PORTAL), that can simulate the emergence of polarization in line emission through a magnetic field of arbitrary morphology. Our model can be used in stand-alone mode, assuming LTE excitation, but it is best used when processing the output of regular three-dimensional (nonpolarized) line radiative transfer modeling codes. We present the spectral polarization map of test cases of a collapsing sphere and protoplanetary disk for multiple three-dimensional magnetic field morphologies.
We present a novel Lyman alpha (Ly$alpha$) radiative transfer code, SEURAT, where line scatterings are solved adaptively with the resolution of the smoothed particle hydrodynamics (SPH). The radiative transfer method implemented in SEURAT is based on a Monte Carlo algorithm in which the scattering and absorption by dust are also incorporated. We perform standard test calculations to verify the validity of the code; (i) emergent spectra from a static uniform sphere, (ii) emergent spectra from an expanding uniform sphere, and (iii) escape fraction from a dusty slab. Thereby we demonstrate that our code solves the Ly$alpha$ radiative transfer with sufficient accuracy. We emphasise that SEURAT can treat the transfer of Ly$alpha$ photons even in highly complex systems that have significantly inhomogeneous density fields. The high adaptivity of SEURAT is desirable to solve the propagation of Ly$alpha$ photons in the interstellar medium of young star-forming galaxies like Ly$alpha$ emitters (LAEs). Thus, SEURAT provides a powerful tool to model the emergent spectra of Ly$alpha$ emission, which can be compared to the observations of LAEs.
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.
Molecular line-transition lists are an essential ingredient for radiative-transfer calculations. With recent databases now surpassing the billion-lines mark, handling them has become computationally prohibitive, due to both the required processing power and memory. Here I present a temperature-dependent algorithm to separate strong from weak line transitions, reformatting the large majority of the weaker lines into a cross-section data file, and retaining the detailed line-by-line information of the fewer strong lines. For any given molecule over the 0.3--30 {micron} range, this algorithm reduces the number of lines to a few million, enabling faster radiative-transfer computations without a significant loss of information. The final compression rate depends on how densely populated is the spectrum. I validate this algorithm by comparing Exomols HCN extinction-coefficient spectra between the complete (65 million line transitions) and compressed (7.7 million) line lists. Over the 0.6--33 {micron} range, the average difference between extinction-coefficient values is less than 1%. A Python/C implementation of this algorithm is open-source and available at https://github.com/pcubillos/repack . So far, this code handles the Exomol and HITRAN line-transition format.
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