论文标题
使用人工神经网络估算从部分天空CMB各向异性的中间到大的角度尺度之间的全天空功率谱
Estimation of Full Sky Power Spectrum between Intermediate to Large Angular Scales from Partial Sky CMB Anisotropies using Artificial Neural Network
论文作者
论文摘要
从观察到的宇宙微波背景（CMB）地图中可靠提取宇宙学信息可能需要从分析中清除强烈前景污染的区域。在本文中，我们采用人工神经网络（ANN）来预测从掩盖的CMB温度各向异性图获得的部分天空光谱中的中间到大角度尺度之间的全天空CMB角功率谱。我们使用一个简单的ANN体系结构，其中一个隐藏层包含$ 895 $的神经元。使用$ 1.2 \ times 10^{5} $训练全天空的样本和相应的部分天空CMB角度CMB角度谱图在Healpix Pixel分辨率参数$ n_ {side} = 256 $中，我们表明，我们的ANN预测了我们的ANN的频谱，并且在每个实现的目标谱系中都很好地同意$ 2 \ leq leq leq leq leq 51 $ leq leq 51的目标频谱。预测的光谱在统计上是公正的，它们可以准确地保留宇宙方差。从统计上讲，预测和基础理论光谱之间的差异约为3σ$。此外，从预测的角功率光谱获得的概率密度与从每个多极的“实际”全天空CMB角幂谱获得的概率非常吻合。有趣的是，我们的工作表明，由于ANN了解了部分天空和完整的天空光谱之间保留了整个统计属性，因此有效删除了由于部分天空中引入的模式模式耦合而导致的输入剪水光谱的显着相关性。预测和地面真相之间统计特性的极好一致性表明，在宇宙学分析中使用人工智能系统的重要性。
Reliable extraction of cosmological information from observed cosmic microwave background (CMB) maps may require removal of strongly foreground contaminated regions from the analysis. In this article, we employ an artificial neural network (ANN) to predict the full sky CMB angular power spectrum between intermediate to large angular scales from the partial sky spectrum obtained from masked CMB temperature anisotropy map. We use a simple ANN architecture with one hidden layer containing $895$ neurons. Using $1.2 \times 10^{5}$ training samples of full sky and corresponding partial sky CMB angular power spectra at Healpix pixel resolution parameter $N_{side} = 256$, we show that predicted spectrum by our ANN agrees well with the target spectrum at each realization for the multipole range $2 \leq l \leq 512$. The predicted spectra are statistically unbiased and they preserve the cosmic variance accurately. Statistically, the differences between the mean predicted and underlying theoretical spectra are within approximately $3σ$. Moreover, the probability densities obtained from predicted angular power spectra agree very well with those obtained from `actual' full sky CMB angular power spectra for each multipole. Interestingly, our work shows that the significant correlations in input cut-sky spectra, due to mode-mode coupling introduced on the partial sky, are effectively removed since the ANN learns the hidden pattern between the partial sky and full sky spectra preserving the entire statistical properties. The excellent agreement of statistical properties between the predicted and the ground-truth demonstrates the importance of using artificial intelligence systems in cosmological analysis more widely.
