High-Resolution CMB Lensing Reconstruction with Deep Learning

• URL: http://arxiv.org/abs/2205.07368v1
• Date: Sun, 15 May 2022 19:58:39 GMT
• Title: High-Resolution CMB Lensing Reconstruction with Deep Learning
• Authors: Peikai Li and Ipek Ilayda Onur and Scott Dodelson and Shreyas Chaudhari
• Abstract summary: We apply a generative adversarial network (GAN) to reconstruct the lensing convergence field. In the process, we use training sets generated by a variety of power spectra, rather than the one used in testing the methods.
• Score: 4.6453787256723365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Next-generation cosmic microwave background (CMB) surveys are expected to provide valuable information about the primordial universe by creating maps of the mass along the line of sight. Traditional tools for creating these lensing convergence maps include the quadratic estimator and the maximum likelihood based iterative estimator. Here, we apply a generative adversarial network (GAN) to reconstruct the lensing convergence field. We compare our results with a previous deep learning approach -- Residual-UNet -- and discuss the pros and cons of each. In the process, we use training sets generated by a variety of power spectra, rather than the one used in testing the methods.

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