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Accurate and Unbiased Reconstruction of CMB B Mode using Deep Learning

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arxiv 2404.18100 v1 pith:YVL6WNMZ submitted 2024-04-28 astro-ph.CO

classification astro-ph.CO
keywords modeaccurateprimenetreconstructionangularautoencoderbelowbottleneck
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abstract

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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  1. Deep Needlet: A CNN based full sky component separation method in Needlet space

    astro-ph.CO 2025-01 conditional novelty 5.0 of 10

    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.

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