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arXiv 2102.04327astro-ph.COcs.LGgr-qchep-phhep-th
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用神经网络无偏估计大尺度全天CMB角功率谱

An Unbiased Estimator of the Full-sky CMB Angular Power Spectrum at Large Scales using Neural Networks

Indian Institute of Science Education and Research Bhopal(印度博帕尔科学教育与研究学院)

Pallav Chanda, Rajib Saha

摘要:

精确估计宇宙微波背景(CMB)角功率谱是一个诱人的问题,因为它为精确宇宙学提供了前景。然而,银河系前景辐射污染了CMB信号,需要可靠地减去,以减少CMB温度估计的系统误差。通常,即使在执行了一些前景去除技术之后,区域中的明亮前景也会导致该区域中的温度估计的进一步不确定性,因此确定潜在的全天空角功率谱构成了挑战。我们探讨了利用人工神经网络的可行性预测的角功率谱的全天空CMB温度图从观测的角功率谱的部分天空,其中CMB温度在一些明亮的前景区域被掩盖。我们提出了我们的分析在大角度尺度上有两个不同的面具。我们产生无偏预测的全天空角功率谱和恢复的基本理论功率谱使用神经网络。我们的预测在很大程度上也是不相关的。我们进一步表明,多极空间的协方差的预测全天空光谱的人工神经网络是远远小于使用伪$C_\ell$方法获得的估计。

英文摘要:

Accurate estimation of the Cosmic Microwave Background (CMB) angular power spectrum is enticing due to the prospect for precision cosmology it presents. Galactic foreground emissions, however, contaminate the CMB signal and need to be subtracted reliably in order to lessen systematic errors on the CMB temperature estimates. Typically bright foregrounds in a region lead to further uncertainty in temperature estimates in the area even after some foreground removal technique is performed and hence determining the underlying full-sky angular power spectrum poses a challenge. We explore the feasibility of utilizing artificial neural networks to predict the angular power spectrum of the full sky CMB temperature maps from the observed angular power spectrum of the partial sky in which CMB temperatures in some bright foreground regions are masked. We present our analysis at large angular scales with two different masks. We produce unbiased predictions of the full-sky angular power spectrum and recover the underlying theoretical power spectrum using neural networks. Our predictions are also uncorrelated to a large extent. We further show that the multipole-space covariances of the predictions of full-sky spectra made by the ANNs are much smaller than those of the estimates obtained using the pseudo-$C_\ell$ method.

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