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Scalable Bayesian Inference for Detection and Deblending in Astronomical Images
Ismael Mendoza · Derek Hansen · Runjing Liu · Ziteng Pang · Zhe Zhao · Camille Avestruz · Jeffrey Regier
Event URL: https://ml4astro.github.io/icml2022/assets/27.pdf »

We present a new probabilistic method for detecting, deblending, and cataloging astronomical sources called the Bayesian Light Source Separator (BLISS). BLISS is based on deep generative models, which embed neural networks within a Bayesian model. For posterior inference, BLISS uses a new form of variational inference known as Forward Amortized Variational Inference. The BLISS inference routine is fast, requiring a single forward pass of the encoder networks on a GPU once the encoder networks are trained. BLISS can perform fully Bayesian inference on megapixel images in seconds, and produces highly accurate catalogs. BLISS is highly extensible, and has the potential to directly answer downstream scientific questions in addition to producing probabilistic catalogs.

Author Information

Ismael Mendoza (University of Michigan)
Derek Hansen (University of Michigan)
Runjing Liu (UC Berkeley)
Ziteng Pang (University of Michigan)
Zhe Zhao (University of Michigan)
Camille Avestruz (University of Michigan)
Jeffrey Regier (University of Michigan)

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