A CNN trained on needlet-filtered Planck-like simulations recovers CMB temperature maps with lower foreground residuals than NILC and power spectra accurate to ell about 1100.
Accurate and Unbiased Reconstruction of CMB B Mode using Deep Learning
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An ingeniously designed autoencoder (PrimeNet) using simulated observations of future generation ECHO satellite mission recovers CMB B mode map, angular spectrum for multipoles $\ell \lesssim 9$ and tensor to scalar ratio $r$ {\it limited only by cosmic variance down to $r= 0.0001$ and below}. We use diverse, realistically complex and detailed foreground models. PrimeNet predicts accurate results even when data with $r=0$ are tested which were not used in training, implying robust and efficient predictive power. The work eliminates a major bottleneck of weak CMB B mode reconstruction and takes a leap forward for understanding fundamental physics of the primordial Universe.
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Deep Needlet: A CNN based full sky component separation method in Needlet space
A CNN trained on needlet-filtered Planck-like simulations recovers CMB temperature maps with lower foreground residuals than NILC and power spectra accurate to ell about 1100.