# Scalable Bayesian Inference for Detection and Deblending in Astronomical Images

arXiv: https://arxiv.org/abs/2207.05642 · HTML: https://mlsanity.com/paper/2207.05642/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Advances in Variational Inference](https://mlsanity.com/paper/1711.05597/) (2017)
- [Forward Amortized Inference for Likelihood-Free Variational Marginalization](https://mlsanity.com/paper/1805.11542/) (2018)
- [Variational Inference for Deblending Crowded Starfields](https://mlsanity.com/paper/2102.02409/) (2021)
- [Approximate Inference for Constructing Astronomical Catalogs from Images](https://mlsanity.com/paper/1803.00113/) (2018)

## Built on by (most cited first, 3 of 3)

- [Neural Posterior Estimation with Autoregressive Tiling for Detecting Objects in Astronomical Images](https://mlsanity.com/paper/2510.03074/) (2025)
- [Towards solving model bias in cosmic shear forward modeling](https://mlsanity.com/paper/2210.16243/) (2022)
- [Statistical Inference for Coadded Astronomical Images](https://mlsanity.com/paper/2211.09300/) (2022)

## Abstract (arXiv)

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.