Blind foreground subtraction for intensity mapping experiments
Blind foreground subtraction for intensity mapping experiments
复制标题
DOI：
10.1093/mnras/stu2474
复制
发表时间：
2014-09
影响因子：
4.8
通讯作者：
D. Alonso;P. Bull;P. Ferreira;Mário G. Santos
中科院分区：
文献类型：
--
作者：
D. Alonso;P. Bull;P. Ferreira;Mário G. Santos
我们利用一个大型的快速模拟的强度映射实验的特点类似于预期的平方公里阵列（SKA），以研究盲目的前景减法技术的可行性和局限性。特别是，我们考虑不同的方法：多项式拟合，主成分分析（PCA）和独立成分分析（伊卡）。我们回顾的动机和算法的三种方法，并表明，他们都可以被描述，使用相同的数学框架，作为不同的方法的盲源分离问题。我们研究了前景减法的效率在角度和径向（频率）方向，以及这种效率对不同的仪器和建模参数的依赖。对于表现良好的前景和工具的影响，我们发现，前景减法可以成功地在大多数规模的利益，以合理的水平。我们还量化了清洗对恢复的信号和功率谱的影响。有趣的是，我们发现，这三种方法产生定量相似的结果，PCA和伊卡几乎是等效的。
We make use of a large set of fast simulations of an intensity mapping experiment with characteristics similar to those expected of the Square Kilometre Array (SKA) in order to study the viability and limits of blind foreground subtraction techniques. In particular, we consider different approaches: polynomial fitting, principal component analysis (PCA) and independent component analysis (ICA). We review the motivations and algorithms for the three methods, and show that they can all be described, using the same mathematical framework, as different approaches to the blind source separation problem. We study the efficiency of foreground subtraction both in the angular and radial (frequency) directions, as well as the dependence of this efficiency on different instrumental and modelling parameters. For well-behaved foregrounds and instrumental effects we find that foreground subtraction can be successful to a reasonable level on most scales of interest. We also quantify the effect that the cleaning has on the recovered signal and power spectra. Interestingly, we find that the three methods yield quantitatively similar results, with PCA and ICA being almost equivalent.