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

Understand

Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results.

• To date, Celeste has been scaled to at most hundreds of megabytes of astronomical images: Bayesian posterior inference is notoriously demanding computationally.
• In this paper, we report on a scalable, parallel version of Celeste, suitable for learning catalogs from modern large-scale astronomical datasets.
• Our algorithmic innovations include a fast numerical optimization routine for Bayesian posterior inference and a statistically efficient scheme for decomposing astronomical optimization problems into subproblems.