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We present a new method which leverages conditional Generative AdversarialNetworks (cGAN) to reconstruct galaxy cluster convergence from lensed CMBtemperature maps. Our model is constructed to emphasize structure andhigh-frequency correctness relative to the Residual U-Net approach presented byCaldeira, et. al. (2019). Ultimately, we demonstrate that while both modelsperform similarly in the no-noise regime (as well as after random off-centeringof the cluster center), cGAN outperforms ResUNet when processing CMB mapsnoised with 5uK/arcmin white noise or astrophysical foregrounds (tSZ and kSZ);this out-performance is especially pronounced at high l, which is exactly theregime in which the ResUNet under-performs traditional methods.

Recovering Galaxy Cluster Convergence from Lensed CMB with Generative Adversarial Networks