# Variational Inference for Deblending Crowded Starfields

arXiv: https://arxiv.org/abs/2102.02409 · HTML: https://mlsanity.com/paper/2102.02409/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 4 Feb 2021.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [Black Box Variational Inference](https://mlsanity.com/paper/1401.0118/) (2013)
- [Advances in Variational Inference](https://mlsanity.com/paper/1711.05597/) (2017)
- [Automatic Posterior Transformation for Likelihood-Free Inference](https://mlsanity.com/paper/1905.07488/) (2019)
- [Automatic Differentiation Variational Inference](https://mlsanity.com/paper/1603.00788/) (2016)
- [Reweighted Wake-Sleep](https://mlsanity.com/paper/1406.2751/) (2014)
- [Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation](https://mlsanity.com/paper/1605.06376/) (2016)
- [Forward Amortized Inference for Likelihood-Free Variational Marginalization](https://mlsanity.com/paper/1805.11542/) (2018)
- [Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model](https://mlsanity.com/paper/1807.07706/) (2018)
- [Approximate Inference for Constructing Astronomical Catalogs from Images](https://mlsanity.com/paper/1803.00113/) (2018)
- [Deblending galaxy superpositions with branched generative adversarial networks](https://mlsanity.com/paper/1810.10098/) (2018)

## Built on by (most cited first, 5 of 5)

- [Machine Learning and Cosmology](https://mlsanity.com/paper/2203.08056/) (2022)
- [Scalable Bayesian Inference for Detection and Deblending in Astronomical Images](https://mlsanity.com/paper/2207.05642/) (2022)
- [Globally Convergent Variational Inference](https://mlsanity.com/paper/2501.08201/) (2025)
- [Neural Posterior Estimation with Autoregressive Tiling for Detecting Objects in Astronomical Images](https://mlsanity.com/paper/2510.03074/) (2025)
- [Statistical Inference for Coadded Astronomical Images](https://mlsanity.com/paper/2211.09300/) (2022)

## Abstract (arXiv)

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. In our experiments with SDSS images of the M2 globular cluster, StarNet is substantially more accurate than two competing methods: Probabilistic Cataloging (PCAT), a method that uses MCMC for inference, and DAOPHOT, a software pipeline employed by SDSS for deblending. In addition, the amortized approach to inference gives StarNet the scaling characteristics necessary to perform Bayesian inference on modern astronomical surveys.