Variational Inference for Deblending Crowded Starfields

Understand

In images collected by astronomical surveys, stars and galaxies often overlap visually.

• Deblending is the task of distinguishing and characterizing individual light sources in survey images.
• We propose StarNet, a Bayesian method to deblend sources in astronomical images of crowded star fields.
• StarNet leverages recent advances in variational inference, including amortized variational distributions and an optimization objective targeting an expectation of the forward KL divergence.