FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code
FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code
复制标题
FitSKIRT：遗传算法使用蒙特卡罗辐射传输代码自动拟合尘埃星系
DOI：
--
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
P. Camps
中科院分区：
文献类型：
--
作者：
G. D. Geyter;M. Baes;J. Fritz;P. Camps
我们提出了FitSKIRT，一种有效地将辐射传递模型拟合到尘埃星系的紫外/光学图像的方法。与FIR/submm数据相比，这些图像具有更好的空间分辨率。FitSKIRT使用GAlib遗传算法库对SKIRT蒙特卡罗辐射传输代码的输出进行优化。遗传算法被证明是处理多维搜索空间以及蒙特卡洛辐射传输码随机特性所引起的噪声的一种有价值的工具。FitSKIRT在模拟螺旋星系的人工图像上进行了测试，我们逐渐增加了拟合参数的数量。我们发现，即使所有11个模型参数都不受约束，我们也可以恢复所有模型参数。最后，我们将FitSKIRT代码应用于边缘螺旋星系NGC 4013的v波段图像。这个星系以前已经由其他作者使用不同的辐射传输代码和优化方法组合建模。考虑到不同的模型和技术，以及参数空间的复杂性和简并性，我们发现了不同模型之间的合理一致性。我们得出的结论是，FitSKIRT方法可以定量地比较不同的模型和几何形状，并最大限度地减少人为干预和偏差的需要。高水平的自动化使其成为用于大型观察数据集的理想工具。
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.