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      성간먼지 산란 연구를 위한 효율적인 몬테카를로 알고리즘 = AN EFFICIENT MONTE-CARLO ALGORITHM FOR DUST-SCATTERING STUDY

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      https://www.riss.kr/link?id=A101343191

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      다국어 초록 (Multilingual Abstract)

      We developed an efficient Monte-Carlo algorithm to solve dust-scattering radiative transfer problems for continuum radiation. The method calculates the scattered intensities for various anisotropic factors ($g_i$) all at once, while actual photon packets are tracked following a scattering phase function given by a single anisotropic factor ($g_0$). The algorithm was tested by applying the method to a dust cloud embedding a star at the cloud center and found to provide accurate results within the statistical fluctuation that is intrinsic in Monte-Carlo simulations. It was found that adopting $g_0$ = 0.4 - 0.5 in the algorithm is most efficient. The method would be efficient in estimating the anisotropic factor of the interstellar dust by comparing the observed data with radiative transfer models.
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      We developed an efficient Monte-Carlo algorithm to solve dust-scattering radiative transfer problems for continuum radiation. The method calculates the scattered intensities for various anisotropic factors ($g_i$) all at once, while actual photon pack...

      We developed an efficient Monte-Carlo algorithm to solve dust-scattering radiative transfer problems for continuum radiation. The method calculates the scattered intensities for various anisotropic factors ($g_i$) all at once, while actual photon packets are tracked following a scattering phase function given by a single anisotropic factor ($g_0$). The algorithm was tested by applying the method to a dust cloud embedding a star at the cloud center and found to provide accurate results within the statistical fluctuation that is intrinsic in Monte-Carlo simulations. It was found that adopting $g_0$ = 0.4 - 0.5 in the algorithm is most efficient. The method would be efficient in estimating the anisotropic factor of the interstellar dust by comparing the observed data with radiative transfer models.

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