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arXiv · 2212.11016

Progress towards a 3D Monte Carlo radiative transfer code for outflow wind modelling

Jakub Fišák·Jiří Kubát·Brankica Kubátová·Markus Kromer·Jiří Krtička
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Abstract

Context: Radiative transfer modelling of expanding stellar envelopes is an important task in their analysis. To account for inhomogeneities and deviations from spherical symmetry, it is necessary to develop a 3D approach to radiative transfer modelling. Aims: We present a 3D Monte Carlo code for radiative transfer modelling, which is aimed to calculate the plasma ionisation and excitation state with the statistical equilibrium equations, moreover, to implement photon-matter coupling. As a first step, we present our Monte Carlo radiation transfer routines developed and tested from scratch. Methods: The background model atmosphere (the temperature, density, and velocity structure) can use an arbitrary grid referred to as the model grid (modGrid). The radiative transfer was solved using the Monte Carlo method in a Cartesian grid, referred to as the propagation grid (propgrid). This Cartesian grid was created based on the structure of the modgrid; correspondence between these two grids was set at the beginning of the calculations and then kept fixed. The propgrid can be either regular or adaptive; two modes of adaptive grids were tested. The accuracy and calculation speed for different propgrids was analysed. Photon interaction with matter was handled using the Lucy's macroatom approach. Test calculations using our code were compared with the results obtained by a different Monte Carlo radiative transfer code. Results: Our method and the related code for the 3D radiative transfer using the Monte Carlo and macroatom methods offer an accurate and reliable solution for the radiative transfer problem, and are especially promising for the inclusion and treatment of 3D inhomogeneities.

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Jakub Fišák, Jiří Kubát, Brankica Kubátová, Markus Kromer, Jiří Krtička. 2022-12-21. Progress towards a 3D Monte Carlo radiative transfer code for outflow wind modelling. https://doi.org/10.1051/0004-6361%2F202243997

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