Bayesian Light Source Separator (BLISS)
========================================
[](https://prob-ml.github.io/bliss/)
[](http://codecov.io/github/prob-ml/bliss?branch=master)
# Introduction
BLISS is a Bayesian method for deblending and cataloging light sources. BLISS provides
- __Accurate estimation__ of parameters in blended field.
- __Calibrated uncertainties__ through fitting an approximate Bayesian posterior.
- __Scalability__ of Bayesian inference to entire astronomical surveys.
BLISS uses state-of-the-art variational inference techniques including
- __Amortized inference__, in which a neural network maps telescope images to an approximate Bayesian posterior on parameters of interest.
- __Variational auto-encoders__ (VAEs) to fit a flexible model for galaxy morphology and deblend galaxies.
- __Wake-sleep algorithm__ to jointly fit the approximate posterior and model parameters such as the PSF and the galaxy VAE.
# Installation
BLISS is pip installable with the following command:
```bash
pip install bliss-deblender
```
and the required dependencies are listed in the ``[tool.poetry.dependencies]`` block of the ``pyproject.toml`` file.
# Installation (Developers)
1. To use and install `bliss` you first need to install [poetry](https://python-poetry.org/docs/).
2. Then, install the [fftw](http://www.fftw.org) library (which is used by `galsim`). With Ubuntu you can install it by running
```bash
sudo apt-get install libfftw3-dev
```
3. Install git-lfs if you haven't already installed it for another project:
```bash
git-lfs install
```
4. Now download the bliss repo and fetch some pre-trained models and test data from git-lfs:
```bash
git clone git@github.com:prob-ml/bliss.git
```
5. To create a poetry environment with the `bliss` dependencies satisified, run
```bash
cd bliss
poetry install
poetry shell
```
6. Verify that bliss is installed correctly by running the tests both on your CPU (default) and on your GPU:
```bash
pytest
pytest --gpu
```
7. Finally, if you are planning to contribute code to this repository, consider installing our pre-commit hooks so that your code commits will be checked locally for compliance with our coding conventions:
```bash
pre-commit install
```
# Latest updates
## Galaxies
- BLISS now includes a galaxy model based on a VAE that was trained on Galsim galaxies.
- BLISS now includes an algorithm for detecting, measuring, and deblending galaxies.
## Stars
- BLISS already includes the StarNet functionality from its predecessor repo: [DeblendingStarFields](https://github.com/Runjing-Liu120/DeblendingStarfields).
# References
Mallory Wang, Ismael Mendoza, Cheng Wang, Camille Avestruz, and Jeffrey Regier. *Statistical Inference for Coadded Astronomical Images.* Machine Learning and the Physical Sciences workshop, NeurIPS 2022. [arXiv:2211.09300](https://arxiv.org/abs/2211.09300)
Derek Hansen, Ismael Mendoza, Runjing Liu, Ziteng Pang, Zhe Zhao, Camille Avestruz, and Jeffrey Regier. *Scalable Bayesian Inference for Detection and Deblending in Astronomical Images*. ICML Workshop on Machine Learning for Astrophysics, 2022. [arXiv:2207.05642](https://arxiv.org/abs/2207.05642)
Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, and The LSST Dark Energy Science Collaboration. *Variational Inference for Deblending Crowded Starfields*, 2021. [arXiv:2102.02409](https://arxiv.org/abs/2102.02409)
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