arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.02278astro-ph.COcs.LG
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Foreground model recognition through Neural Networks for CMB B-mode observations

Scuola Internazionale Superiore di Studi Avanzati (SISSA)(国际高等研究院 (SISSA));Istituto Nazionale di Fisica Nucleare (INFN)(国家核物理研究院 (INFN));Institute for Fundamental Physics of the Universe (IFPU)(宇宙基础物理研究所 (IFPU))

Farida Farsian, Nicoletta Krachmalnicoff, Carlo Baccigalupi

英文摘要:

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.

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