GalPaK3D: A BAYESIAN PARAMETRIC TOOL FOR EXTRACTING MORPHOKINEMATICS OF GALAXIES FROM 3D DATA
GalPaK3D: A BAYESIAN PARAMETRIC TOOL FOR EXTRACTING MORPHOKINEMATICS OF GALAXIES FROM 3D DATA
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DOI：
10.1088/0004-6256/150/3/92
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发表时间：
2015-01
期刊：
影响因子：
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通讯作者：
N. Bouch'e;H. Carfantan;I. Schroetter;L. Michel-Dansac;T. C. IRAP-Toulouse;CRAL-Lyon
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文献类型：
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作者：
N. Bouch'e;H. Carfantan;I. Schroetter;L. Michel-Dansac;T. C. IRAP-Toulouse;CRAL-Lyon
我们提出了一种直接从三维数据立方体约束星系参数的方法。该算法直接将数据与映射在x，y，λ？>坐标它使用谱线扩展函数和空间点扩展函数（PSF）来生成三维内核，其特征是特定于仪器或用户生成的。该算法返回固有的建模属性沿着“固有”模型数据立方体和与3D内核卷积的建模星系。该算法使用马尔可夫链蒙特卡罗方法与非传统的建议分布，以有效地探测参数空间。我们使用1728个模拟星系和流体动力学模拟产生的星系在各种视宁度条件下从0.″6到1.″证明了算法的鲁棒性。我们发现该算法可以恢复形态学参数（倾角、位置角）控制在10%以内，（最大旋转速度）在20%以内，只要最大信噪比（S/N）大于1/3 pixel−1，且星系半光半径与视宁半径之比大于约1.5，则视宁度与PSF无关（最大1.“2）。人们可以使用这样的算法，同时约束的运动学和形态参数（nonmerging）观察到的星系在非最佳看到的条件。该算法也可用于自适应光学数据或高质量，高S/N数据寻找非轴对称结构的残差。
We present a method to constrain galaxy parameters directly from three-dimensional data cubes. The algorithm compares directly the data with a parametric model mapped in x , y , λ ?> coordinates. It uses the spectral line-spread function and the spatial point-spread function (PSF) to generate a three-dimensional kernel whose characteristics are instrument specific or user generated. The algorithm returns the intrinsic modeled properties along with both an “intrinsic” model data cube and the modeled galaxy convolved with the 3D kernel. The algorithm uses a Markov Chain Monte Carlo approach with a nontraditional proposal distribution in order to efficiently probe the parameter space. We demonstrate the robustness of the algorithm using 1728 mock galaxies and galaxies generated from hydrodynamical simulations in various seeing conditions from 0.″6 to 1.″2. We find that the algorithm can recover the morphological parameters (inclination, position angle) to within 10% and the kinematic parameters (maximum rotation velocity) to within 20%, irrespectively of the PSF in seeing (up to 1.″2) provided that the maximum signal-to-noise ratio (S/N) is greater than ∼3 pixel−1 and that the ratio of galaxy half-light radius to seeing radius is greater than about 1.5. One can use such an algorithm to constrain simultaneously the kinematics and morphological parameters of (nonmerging) galaxies observed in nonoptimal seeing conditions. The algorithm can also be used on adaptive optics data or on high-quality, high-S/N data to look for nonaxisymmetric structures in the residuals.