Variational Inference for Deblending Crowded Starfields

Runjing Liu, Jon D. McAuliffe, Jeffrey Regier

In the image data collected by astronomical surveys, stars and galaxies often overlap. Deblending is the task of distinguishing and characterizing individual light sources from survey images. We propose StarNet, a fully Bayesian method to deblend sources in astronomical images of crowded star fields. StarNet leverages recent advances in variational inference, including amortized variational distributions and the wake-sleep algorithm. Wake-sleep, which minimizes forward KL divergence, has significant benefits compared to traditional variational inference, which minimizes a reverse KL divergence. In our experiments with SDSS images of the M2 globular cluster, StarNet is substantially more accurate than two competing methods: Probablistic Cataloging (PCAT), a method that uses MCMC for inference, and a software pipeline employed by SDSS for deblending (DAOPHOT). In addition, StarNet is as much as $100,000$ times faster than PCAT, exhibiting the scaling characteristics necessary to perform fully Bayesian inference on modern astronomical surveys.

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