问题的规模：用GMCA恢复再电离图像
The Scale of the Problem : Recovering Images of Reionization with GMCA
摘要：
如果我们要深入了解一个未探索的宇宙学时代，从再电离时代的红移21厘米发射数据中准确和精确地去除21厘米前景是必不可少的。我们应用非参数技术，广义形态成分分析或GMCA，模拟LOFAR-EoR数据，并表明它有能力清理前景，具有高精度。我们在令人印象深刻的频率和尺度范围内以高精度恢复了21 cm的1D，2D和3D功率谱。我们发现，GMCA保留了21厘米的相位信息，特别是当最小的空间尺度数据被丢弃。虽然它已被证明，LOFAR-EoR图像恢复理论上是可能的，使用图像平滑，我们补充说，小波分解是一种有效的方式恢复21厘米的信号映射到相同或更高的精度与更大的灵活性。通过将GMCA输出残差图（等于噪声、21 cm信号和任何前景拟合误差）与一个频率下的21 cm图进行比较，并丢弃较小的小波尺度信息，我们发现相关系数为0.689，而等效平滑图像的相关系数为0.588。仅考虑中央50%的地图，这些系数分别提高到0.905和0.605，我们得出结论，小波分解是一个更强大的方法去噪重建21厘米的地图比平滑。
英文摘要：
The accurate and precise removal of 21-cm foregrounds from Epoch of Reionization redshifted 21-cm emission data is essential if we are to gain insight into an unexplored cosmological era. We apply a non-parametric technique, Generalized Morphological Component Analysis or GMCA, to simulated LOFAR-EoR data and show that it has the ability to clean the foregrounds with high accuracy. We recover the 21-cm 1D, 2D and 3D power spectra with high accuracy across an impressive range of frequencies and scales. We show that GMCA preserves the 21-cm phase information, especially when the smallest spatial scale data is discarded. While it has been shown that LOFAR-EoR image recovery is theoretically possible using image smoothing, we add that wavelet decomposition is an efficient way of recovering 21-cm signal maps to the same or greater order of accuracy with more flexibility. By comparing the GMCA output residual maps (equal to the noise, 21-cm signal and any foreground fitting errors) with the 21-cm maps at one frequency and discarding the smaller wavelet scale information, we find a correlation coefficient of 0.689, compared to 0.588 for the equivalently smoothed image. Considering only the central 50% of the maps, these coefficients improve to 0.905 and 0.605 respectively and we conclude that wavelet decomposition is a significantly more powerful method to denoise reconstructed 21-cm maps than smoothing.
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