Bayesian Light Source Separator (BLISS)

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

To use and install bliss you first need to install poetry.
Then, install the fftw library (which is used by galsim). With Ubuntu you can install it by running

sudo apt-get install libfftw3-dev

Now, to create a poetry environment with the bliss dependencies satisified, run

git clone https://github.com/prob-ml/bliss.git
cd bliss
poetry install
poetry shell

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.

References

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

EiffL / bliss