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Workshop: Machine Learning and the Physical Sciences

Recovering Galaxy Cluster Convergence from Lensed CMB with Generative Adversarial Networks

Liam Parker · Dongwon Han · Shirley Ho · Pablo Lemos


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

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