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Gulati, Puranjay Rohan

University of British Columbia

The polarization of the cosmic microwave background (CMB) can be split into two coordinate independent components: the gradient-like E-mode, and the curl-like B-mode. Primordial B-modes are of particular interest as they do not arise from scalar density perturbations and serve as a direct probe of primordial gravitational waves theorized to be generated during inflation. A direct detection of these elusive B-modes would help determine the energy scale of inflation and constrain possible inflationary models. However, the E-B decomposition of polarization on a partial or cut sky is non-unique and introduces modes that cannot conclusively be classified as pure E or B. This is a source of ambiguity for all CMB experiments probing polarization as a Galactic cut is required even for purported full-sky missions, such as the European Space Agency’s (ESA) Planck mission. We present a map-based technique using machine learning (ML) methods to perform this partial sky decomposition. In particular, we use a deep residual convolutional neural network (CNN) based on the U-Net architecture to perform this decomposition on small patches of the sky 400 square degrees in area, allowing the targeting of regions with low Galactic foreground. Our deep residual U-Net performs exceptionally well at angular scales of a few degrees, corresponding to spherical harmonic Fourier modes l (ell) ≃ 50 to 110, a range of scales ideal for probing the recombination bump of the primordial B-mode power spectrum. Additionally, the map-based nature of our technique allows visual inspection of the E-B separation. This may be especially useful for identifying residual Galactic contamination in polarization data.

Text

2022-07-20

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/82178

Physics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/