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Composite biasing in Monte Carlo radiative transfer

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Abstract
Biasing or importance sampling is a powerful technique in Monte Carlo radiative transfer, and can be applied in different forms to increase the accuracy and efficiency of simulations. One of the drawbacks of the use of biasing is the potential introduction of large weight factors. We discuss a general strategy, composite biasing, to suppress the appearance of large weight factors. We use this composite biasing approach for two different problems faced by current state-of-the-art Monte Carlo radiative transfer codes: the generation of photon packages from multiple components, and the penetration of radiation through high optical depth barriers. In both cases, the implementation of the relevant algorithms is trivial and does not interfere with any other optimisation techniques. Through simple test models, we demonstrate the general applicability, accuracy and efficiency of the composite biasing approach. In particular, for the penetration of high optical depths, the gain in efficiency is spectacular for the specific problems that we consider: in simulations with composite path length stretching, high accuracy results are obtained even for simulations with modest numbers of photon packages, while simulations without biasing cannot reach convergence, even with a huge number of photon packages.
Keywords
SCATTERING, ALGORITHM, DISTRIBUTIONS, DIRTY MODEL, REFLECTION NEBULAE, TRANSFER CODE, TRANSFER SIMULATIONS, YOUNG STELLAR OBJECTS, SKIRT, radiative transfer, DUST

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Citation

Please use this url to cite or link to this publication:

MLA
Baes, Maarten et al. “Composite Biasing in Monte Carlo Radiative Transfer.” ASTRONOMY & ASTROPHYSICS 590 (2016): n. pag. Print.
APA
Baes, M., Gordon, K., Lunttila, T., Bianchi, S., Camps, P., Juvela, M., & Kuiper, R. (2016). Composite biasing in Monte Carlo radiative transfer. ASTRONOMY & ASTROPHYSICS, 590.
Chicago author-date
Baes, Maarten, Karl Gordon, Tuomas Lunttila, Simone Bianchi, Peter Camps, Mika Juvela, and Rolf Kuiper. 2016. “Composite Biasing in Monte Carlo Radiative Transfer.” Astronomy & Astrophysics 590.
Chicago author-date (all authors)
Baes, Maarten, Karl Gordon, Tuomas Lunttila, Simone Bianchi, Peter Camps, Mika Juvela, and Rolf Kuiper. 2016. “Composite Biasing in Monte Carlo Radiative Transfer.” Astronomy & Astrophysics 590.
Vancouver
1.
Baes M, Gordon K, Lunttila T, Bianchi S, Camps P, Juvela M, et al. Composite biasing in Monte Carlo radiative transfer. ASTRONOMY & ASTROPHYSICS. 2016;590.
IEEE
[1]
M. Baes et al., “Composite biasing in Monte Carlo radiative transfer,” ASTRONOMY & ASTROPHYSICS, vol. 590, 2016.
@article{8096891,
  abstract     = {Biasing or importance sampling is a powerful technique in Monte Carlo radiative transfer, and can be applied in different forms to increase the accuracy and efficiency of simulations. One of the drawbacks of the use of biasing is the potential introduction of large weight factors. We discuss a general strategy, composite biasing, to suppress the appearance of large weight factors. We use this composite biasing approach for two different problems faced by current state-of-the-art Monte Carlo radiative transfer codes: the generation of photon packages from multiple components, and the penetration of radiation through high optical depth barriers. In both cases, the implementation of the relevant algorithms is trivial and does not interfere with any other optimisation techniques. Through simple test models, we demonstrate the general applicability, accuracy and efficiency of the composite biasing approach. In particular, for the penetration of high optical depths, the gain in efficiency is spectacular for the specific problems that we consider: in simulations with composite path length stretching, high accuracy results are obtained even for simulations with modest numbers of photon packages, while simulations without biasing cannot reach convergence, even with a huge number of photon packages.},
  articleno    = {A55},
  author       = {Baes, Maarten and Gordon, Karl and Lunttila, Tuomas and Bianchi, Simone and Camps, Peter and Juvela, Mika and Kuiper, Rolf},
  issn         = {1432-0746},
  journal      = {ASTRONOMY & ASTROPHYSICS},
  keywords     = {SCATTERING,ALGORITHM,DISTRIBUTIONS,DIRTY MODEL,REFLECTION NEBULAE,TRANSFER CODE,TRANSFER SIMULATIONS,YOUNG STELLAR OBJECTS,SKIRT,radiative transfer,DUST},
  language     = {eng},
  pages        = {12},
  title        = {Composite biasing in Monte Carlo radiative transfer},
  url          = {http://dx.doi.org/10.1051/0004-6361/201528063},
  volume       = {590},
  year         = {2016},
}

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