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Recovering Galaxy Cluster Convergence from Lensed CMB with Generative Adversarial Networks
Liam Parker · Dongwon Han · Shirley Ho · Pablo Lemos

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in Machine Learning and the Physical Sciences »

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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Author Information

Liam Parker (Princeton University)

Undergraduate at Princeton University majoring in physics with an active interest in machine learning and computer science.

Dongwon Han (DAMTP, University of Cambridge)
Shirley Ho (Flatiron Institute)

Shirley Ho is a group leader and acting director at Flatiron Institute at Simons foundation, a research professor of physics and an affiliated faculty at Center for Data Science at NYU. Ho also holds associate (adjunct) professorship at Carnegie Mellon University and visiting appointment at Princeton University. She was a senior scientist at Berkeley National Lab from 2016-2018 and a Cooper-Siegel Development chair professor at Carnegie Mellon University before that. Ho was a Seaborg and Chamberlain Fellow from 2008-2011 at Berkeley Lab, after receiving her PhD in Astrophysics from Princeton University in 2008 under supervision of David Spergel. Ho graduated summa cum laude with a B.A. in Physics and a B.A. in Computer Science from UC Berkeley. A cited expert in cosmology, machine learning applications in astrophysics and data science,her interests are using deep learning accelerated simulations to understand the Universe, and other astrophysical phenomena. She tries her best to balance her love for the Universe, the machine and life especially during these crazy times.

Pablo Lemos (University of Sussex)

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