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Celeste is a procedure for inferring astronomical catalogs that attainsstate-of-the-art scientific results. To date, Celeste has been scaled to atmost hundreds of megabytes of astronomical images: Bayesian posterior inferenceis notoriously demanding computationally. In this paper, we report on ascalable, parallel version of Celeste, suitable for learning catalogs frommodern large-scale astronomical datasets. Our algorithmic innovations include afast numerical optimization routine for Bayesian posterior inference and astatistically efficient scheme for decomposing astronomical optimizationproblems into subproblems.  Our scalable implementation is written entirely in Julia, a new high-leveldynamic programming language designed for scientific and numerical computing.We use Julia's high-level constructs for shared and distributed memoryparallelism, and demonstrate effective load balancing and efficient scaling onup to 8192 Xeon cores on the NERSC Cori supercomputer.

Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference