Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Foreground model recognition through Neural Networks for CMB B-mode
  observations   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.

13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio