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Sparsity and cosmology: inverse problems in cosmic microwave background experiments

机译:稀疏性和宇宙学:宇宙微波背景实验中的逆问题

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We propose a new method to better estimate and subtract the contribution of detected compact sources to the microwave sky. These bright compact source emissions contaminate the full-sky data over a significant fraction of the sky, and should therefore be accurately removed if a high resolution and full-sky estimate of the components is sought after. However the point source spectral variability hampers accurate blind source separation, even with state-of-the-art localized source separation techniques. In this work, we rather propose to estimate the flux of the brightest compact sources using a morphological separation approach, relying on a more sophisticated model for the background than in standard approaches. Essentially, this amounts to separate point sources with known support and shape from a background assumed sparse in the spherical harmonic domain. This approach is compared to standard local x~2 minimization modeling the background as a low order polynomial on WMAP realistic simulations. If in noisy situations estimating more than a few parameter does not improve flux recovery, in the first WMAP channels the proposed method leads to lower biases (typically by factors of 2) and increased robustness.
机译:我们提出了一种新的方法来更好地估计和减去检测到的紧凑源与微波天空的贡献。这些明亮的紧凑型源排放在大量的天空中污染了全天数据,因此如果追求高分辨率和组件的全天估计,则应准确地拆除。然而,点源光谱变异性妨碍了准确的盲源分离,即使具有最先进的局部源分离技术。在这项工作中,我们宁愿使用形态分离方法估算最亮的紧凑态源的通量,依赖于背景的更复杂的模型而不是标准方法。本质上,这量到了具有已知支持和形状的分离点来源,从背景中的背景稀疏在球形谐波域中的稀疏。将这种方法与标准本地X〜2最小化进行比较,在WMAP逼真模拟上将背景设计为低阶多项式。如果在估计超过几个参数的噪声情况下不改善磁通恢复,则在第一个WMAP信道中,所提出的方法导致较低的偏置(通常为2)和增加的鲁棒性。

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