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Poster
in
Workshop: Machine Learning for Astrophysics

Full-Sky Gravitational Lensing Simulations Using Generative Adversarial Networks

Pier Fiedorowicz · Eduardo Rozo · Supranta Boruah · William Coulton · Shirley Ho · Giulio Fabbian


Abstract:

We present a new method that uses a generative adversarial network to learn how to locally redistribute the mass in lognormal mass maps to achieve N-body quality full-sky weak lensing maps. Our mass maps reproduce a broad range of weak lensing summary statistics with percent level accuracy. Producing a single full-sky map requires ~10 seconds on an average compute node with no GPU acceleration. Relative to running a dark matter simulation, our algorithm reduces run time by more than four orders of magnitude.

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