论文标题
通过CMB B模式观测的神经网络的前景模型识别
Foreground model recognition through Neural Networks for CMB B-mode observations
论文作者
论文摘要
在这项工作中，我们提出了一种神经网络（NN）算法，用于鉴定在宇宙微波背景（CMB）$ b $ mode多频率观测值的情况下，弥散极化银河排放的适当参数化。特别是，我们将分析集中在与极化观察相关的低频前景上：即银河同步加速器和异常微波发射（AME）。我们已经在一组模拟地图上实施并测试了我们的方法，该图对应于未来的卫星和低频基于地面探针表示的频率覆盖率和灵敏度。识别不同天空区域前景发射的正确参数化的NN效率的准确度约为90美元\％$。我们已经将这种性能与使用多频拟合的参数前景估算后的$χ^{2} $信息进行了比较，并量化了NN方法提供的增益。我们的结果表明，模型识别在CMB $ b $ mode观测值中的相关性，并突出了针对此目的的专用过程的开发。
In this work we present a Neural Network (NN) algorithm for the identification of the appropriate parametrization of diffuse polarized Galactic emissions in the context of Cosmic Microwave Background (CMB) $B$-mode multi-frequency observations. In particular, we have focused our analysis on low frequency foregrounds relevant for polarization observation: namely Galactic Synchrotron and Anomalous Microwave Emission (AME). We have implemented and tested our approach on a set of simulated maps corresponding to the frequency coverage and sensitivity represented by future satellite and low frequency ground based probes. The NN efficiency in recognizing the right parametrization of foreground emission in different sky regions reaches an accuracy of about $90\%$. We have compared this performance with the $χ^{2}$ information following parametric foreground estimation using multi-frequency fitting, and quantify the gain provided by a NN approach. Our results show the relevance of model recognition in CMB $B$-mode observations, and highlight the exploitation of dedicated procedures to this purpose.
