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 $\chi^{2}$ 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.

中文翻译：

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通过神经网络识别 CMB B 模式观测的前景模型

在这项工作中，我们提出了一种神经网络 (NN) 算法，用于在宇宙微波背景 (CMB) $B$ 模式多频观测的背景下识别漫极化银河发射的适当参数化。特别是，我们将分析重点放在与偏振观测相关的低频前景上：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，这些地图对应于未来卫星和低频地面探测器所代表的频率覆盖范围和灵敏度。在识别不同天空区域前景发射的正确参数化方面，NN 效率达到了约 $90\%$ 的准确度。我们已经将此性能与使用多频拟合进行参数前景估计后的 $\chi^{2}$ 信息进行了比较，并量化了神经网络方法提供的增益。我们的结果显示了模型识别在 CMB $B$ 模式观测中的相关性，并强调了为此目的而开发的专用程序。