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.

BLISS is pip installable with the following command:

pip install bliss-deblender

and the required dependencies are listed in the [tool.poetry.dependencies] block of the pyproject.toml file.

1. To use and install bliss you first need to install poetry.

2. Then, install the fftw library (which is used by galsim). With Ubuntu you can install it by running

sudo apt-get install libfftw3-dev

3. Install git-lfs if you haven't already installed it for another project:

git-lfs install

4. Now download the bliss repo and fetch some pre-trained models and test data from git-lfs:

git clone https://github.com/prob-ml/bliss.git

5. To create a poetry environment with the bliss dependencies satisified, run

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:

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:

pre-commit --install

• 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.

• BLISS already includes the StarNet functionality from its predecessor repo: DeblendingStarFields.

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

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

Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, and The LSST Dark Energy Science Collaboration. Variational Inference for Deblending Crowded Starfields, 2021. arXiv:2102.02409