Excess B-modes extracted from the Planck polarization maps
H. U. N{\o}rgaard-Nielsen

TL;DR
This paper demonstrates that neural networks can effectively extract the CMB B-mode polarization signal from Planck data, revealing a significant excess with high signal-to-noise ratio that differs from canonical models.
Contribution
It shows the feasibility of using neural networks to accurately extract the CMB B-mode spectrum from Planck polarization maps, including the removal of residual systematics.
Findings
Extracted a BB power spectrum with SNR ≈ 4.5 between l=200 and 250.
Detected a bright feature in the spectrum that differs from standard models.
Confirmed neural networks' capability to reduce systematic errors in polarization data.
Abstract
One of the main obstacles for extracting the Cosmic Microwave Background (CMB) from mm/submm observations is the pollution from the main Galactic components: synchrotron, free-free and thermal dust emission. The feasibility of using simple neural networks to extract CMB has been demonstrated on both temperature and polarization data obtained by the WMAP satellite. The main goal of this paper is to demonstrate the feasibility of neural networks for extracting the CMB signal from the Planck polarization data with high precision. Both auto-correlation and cross-correlation power spectra within a mask covering about 63 percent of the sky have been used together with a 'high pass filter' in order to minimize the influence of the remaining systematic errors in the Planck Q and U maps. Using the Planck 2015 released polarization maps, a BB power spectrum have been extracted by…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
