Approximate Inference for Constructing Astronomical Catalogs from Images
This work addresses the challenge of efficiently creating catalogs for astronomers from massive image datasets, with incremental improvements in speed and scalability.
The authors tackled the problem of constructing astronomical catalogs from large telescope image sets by developing a fully generative model and comparing two inference procedures, achieving a 1000x speedup with variational inference that processed 50 terabytes of images in 14.6 minutes using 665,000 CPU cores.
We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets. Each pixel intensity is treated as a random variable with parameters that depend on the latent properties of stars and galaxies. These latent properties are themselves modeled as random. We compare two procedures for posterior inference. One procedure is based on Markov chain Monte Carlo (MCMC) while the other is based on variational inference (VI). The MCMC procedure excels at quantifying uncertainty, while the VI procedure is 1000 times faster. On a supercomputer, the VI procedure efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50 terabytes of images in 14.6 minutes, demonstrating the scaling characteristics necessary to construct catalogs for upcoming astronomical surveys.