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arXiv 1807.02097astro-ph.GA
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星系图像的贝叶斯球盘分解

Bayesian bulge-disc decomposition of galaxy images

J. J. Argyle, J. Méndez-Abreu, V. Wild, D. J. Mortlock

摘要:

我们介绍PHI,一个完全贝叶斯马尔可夫链蒙特卡罗算法设计的星系图像的结构分解。PHI使用三层方法来有效地探索复杂的参数空间。结合使用先验知识,以防止非物理模型,PHI提供了许多显着的优势,估计表面亮度轮廓参数超过传统的优化算法。我们应用PHI的合成星系的样本与SDSS的图像属性,我们的能力恢复无偏和约束良好的结构参数的星系属性的影响进行调查。在两个组件的凸起+盘星系,我们发现,凸起的结构参数恢复得不如那些光盘,特别是当凸起贡献了较低的分数的光度,或几乎没有解决相对于像素尺度或PSF。除了具有大凸起Sersic参数n的凸起+盘星系外,几乎没有系统偏差。在SDSS图像的应用程序,我们发现与其他代码,当运行在相同的图像具有相同的面具,权重,和PSF良好的协议。同样,我们发现,凸起参数是最难约束鲁棒性。最后,我们探讨了使用贝叶斯信息准则(BIC)的方法来决定一个星系是否有一个或两个组件。

英文摘要:

We introduce PHI, a fully Bayesian Markov-chain Monte Carlo algorithm designed for the structural decomposition of galaxy images. PHI uses a triple layer approach to effectively and efficiently explore the complex parameter space. Combining this with the use of priors to prevent nonphysical models, PHI offers a number of significant advantages for estimating surface brightness profile parameters over traditional optimisation algorithms. We apply PHI to a sample of synthetic galaxies with SDSS-like image properties to investigate the effect of galaxy properties on our ability to recover unbiased and well constrained structural parameters. In two-component bulge+disc galaxies we find that the bulge structural parameters are recovered less well than those of the disc, particularly when the bulge contributes a lower fraction to the luminosity, or is barely resolved with respect to the pixel scale or PSF. There are few systematic biases, apart from for bulge+disc galaxies with large bulge Sersic parameter, n. On application to SDSS images, we find good agreement with other codes, when run on the same images with the same masks, weights, and PSF. Again, we find that bulge parameters are the most difficult to constrain robustly. Finally, we explore the use of a Bayesian Information Criterion (BIC) method for deciding whether a galaxy has one- or two-components.

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