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Cited by 290

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

SUNRISE: polychromatic dust radiative transfer in arbitrary geometries

Open access
Jonsson, P.·2006·Monthly Notices of the Royal Astronomical Society

Abstract

This paper describes Sunrise, a parallel, free Monte-Carlo code for the calculation of radiation transfer through astronomical dust. Sunrise uses an adaptive-mesh refinement grid to describe arbitrary geometries of emitting and absorbing/scattering media, with spatial dynamical range exceeding 10^4, and it can efficiently generate images of the emerging radiation at arbitrary points in space. In addition to the monochromatic radiative transfer typically used by Monte-Carlo codes, Sunrise is capable of propagating a range of wavelengths simultaneously. This ``polychromatic'' algorithm gives significant improvements in efficiency and accuracy when spectral features are calculated. Sunrise is used to study the effects of dust in hydrodynamic simulations of interacting galaxies, and the procedure for this is described. The code is tested against previously published results.

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Crossref · 10.1111/j.1365-2966.2006.10884.x

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Metadata

Authors
Jonsson, P.
Venue
Monthly Notices of the Royal Astronomical Society
Published
2006-10-11
Volume
372
Issue
1
Pages
2-20
ISSN
  • 0035-8711 (ISSN-L)
  • 1365-2966
OpenAlex
W2064468860
Cited by
290
References
26
FWCI
5.98
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: SUNRISE: polychromatic dust radiative transfer in arbitrary geometries. https://app.cochat.ai/papers/10.1111/j.1365-2966.2006.10884.x
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Jonsson, P. (2006). SUNRISE: polychromatic dust radiative transfer in arbitrary geometries. Monthly Notices of the Royal Astronomical Society. https://doi.org/10.1111/j.1365-2966.2006.10884.x

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.