A novel CMB component separation method: hierarchical generalized morphological component analysis
A novel CMB component separation method: hierarchical generalized morphological component analysis
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DOI：
10.1093/mnras/staa744
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发表时间：
2020-05-01
影响因子：
4.8
通讯作者：
Dvorkin, Cora
中科院分区：
文献类型：
--
作者：
Wagner-Carena, Sebastian;Hopkins, Max;Dvorkin, Cora
提出了一种基于盲源分离框架的宇宙微波背景辐射前景扣除新方法。受以往工作的启发，将局部变异广义形态成分分析（GMCA），我们介绍了层次GMCA（HGMCA），贝叶斯层次图模型的源分离。我们测试我们的方法在N侧= 256模拟天空地图，包括灰尘，同步加速器，自由，和异常微波发射，并表明，HGMCA减少了25%的前景污染超过GMCA在这两个地区包括和排除的普朗克UT 78掩模，在l > 200 ℃时，将CMB温度功率谱测量误差降低到0.02%~ 0.03%的水平（所有l <0.26%），并降低了与所有前景的相关性。我们发现等同或改进的性能相比，国家的最先进的内部线性组合型算法在这些模拟，这表明HGMCA可能是一个有竞争力的替代前景分离技术以前应用于观察到的CMB数据。此外，我们表明，我们的性能不会受到影响，当我们扰动模型参数或改变CMB实现，这表明我们的算法推广远远超出我们的简化模拟。我们的研究结果开辟了一个新的途径，通过贝叶斯层次分析构建CMB地图。
We present a novel technique for cosmic microwave background (CMB) foreground subtraction based on the framework of blind source separation. Inspired by previous work incorporating local variation to generalized morphological component analysis (GMCA), we introduce hierarchical GMCA (HGMCA), a Bayesian hierarchical graphical model for source separation. We test our method on N-side = 256 simulated sky maps that include dust, synchrotron, free-free, and anomalous microwave emission, and show that HGMCA reduces foreground contamination by 25 per cent over GMCA in both the regions included and excluded by the Planck UT78 mask, decreases the error in the measurement of the CMB temperature power spectrum to the 0.02-0.03 per cent level at l > 200 (and < 0.26 per cent for all l), and reduces correlation to all the foregrounds. We find equivalent or improved performance when compared to state-of-the-art internal linear combination type algorithms on these simulations, suggesting that HGMCA may be a competitive alternative to foreground separation techniques previously applied to observed CMB data. Additionally, we show that our performance does not suffer when we perturb model parameters or alter the CMB realization, which suggests that our algorithm generalizes well beyond our simplified simulations. Our results open a new avenue for constructing CMB maps through Bayesian hierarchical analysis.