High-Resolution CMB Lensing Reconstruction with Deep Learning

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 lik...

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Bibliographic Details
Main Authors Li, Peikai, Onur, Ipek Ilayda, Dodelson, Scott, Chaudhari, Shreyas
Format Journal Article
LanguageEnglish
Published 15.05.2022
Subjects
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Learning
Physics - Cosmology and Nongalactic Astrophysics
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Summary: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.
DOI:10.48550/arxiv.2205.07368