FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code

被引:52
|
作者
De Geyter, G. [1 ]
Baes, M. [1 ]
Fritz, J. [1 ]
Camps, P. [1 ]
机构
[1] Univ Ghent, Sterrenkundig Observ, B-9000 Ghent, Belgium
来源
ASTRONOMY & ASTROPHYSICS | 2013年 / 550卷
关键词
radiative transfer; dust; extinction; galaxies: structure; galaxies: individual: NGC 4013; SPECTRAL ENERGY-DISTRIBUTION; HERSCHEL REFERENCE SURVEY; LARGE-MAGELLANIC-CLOUD; EXTRAGALACTIC EXTINCTION LAW; DEPROJECTED SERSIC MODEL; GRAIN-SIZE DISTRIBUTIONS; CLUMPY 2-PHASE MEDIUM; ON SPIRAL GALAXIES; INTERSTELLAR DUST; DISC GALAXIES;
D O I
10.1051/0004-6361/201220126
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
We present FitSKIRT, a method to efficiently fit radiative transfer models to UV/optical images of dusty galaxies. These images have the advantage that they have better spatial resolution compared to FIR/submm data. FitSKIRT uses the GAlib genetic algorithm library to optimize the output of the SKIRT Monte Carlo radiative transfer code. Genetic algorithms prove to be a valuable tool in handling the multi-dimensional search space as well as the noise induced by the random nature of the Monte Carlo radiative transfer code. FitSKIRT is tested on artificial images of a simulated edge-on spiral galaxy, where we gradually increase the number of fitted parameters. We find that we can recover all model parameters, even if all 11 model parameters are left unconstrained. Finally, we apply the FitSKIRT code to a V-band image of the edge-on spiral galaxy NGC 4013. This galaxy has been modeled previously by other authors using different combinations of radiative transfer codes and optimization methods. Given the different models and techniques and the complexity and degeneracies in the parameter space, we find reasonable agreement between the different models. We conclude that the FitSKIRT method allows comparison between different models and geometries in a quantitative manner and minimizes the need of human intervention and biasing. The high level of automation makes it an ideal tool to use on larger sets of observed data.
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页数:14
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