High-Resolution CMB Lensing Reconstruction with Deep Learning
This work addresses the challenge of improving lensing map reconstruction for cosmology, but it appears incremental as it builds on existing deep learning approaches without claiming major breakthroughs.
The paper tackled the problem of reconstructing high-resolution cosmic microwave background lensing convergence maps by applying a generative adversarial network (GAN), comparing it to a previous deep learning method (Residual-UNet) and using varied training sets.
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.