/Celeste.jl

Scalable inference for a generative model of astronomical images
astronomy bayesian-inference variational-inference astronomical-catalogs astronomical-images graphical-models
  1. Jupyter Notebook 82.3%
  2. Julia 15.5%
  3. Python 1.8%
  4. Other 0.4%
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@jeff-regier
jeff-regier comparing Celeste to SEP and a failure mode of HSC on GalSim data (#736) 
* comparing Celeste to SEP and a failure mode of HSC on GalSim data

* updated comments
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experiments comparing Celeste to SEP and a failure mode of HSC on GalSim data (#736) Aug 27, 2018
src reproduce experiments in Jupyter notebooks (#732) Aug 14, 2018
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README.md

Celeste.jl

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Celeste.jl finds and characterizes stars and galaxies in astronomical images. It performs approximate Bayesian inference as described in

Jeffrey Regier, Andrew Miller, David Schlegel, Ryan Adams, Jon McAuliffe, and Prabhat. "Approximate inference for constructing astronomical catalogs from images." In submission.

and

Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin Thomas, and Prabhat. “Cataloging the visible universe through Bayesian inference at petascale.” In: International Parallel and Distributed Processing Symposium (IPDPS), 2018.

and

Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, and Prabhat. “Celeste: Variational inference for a generative model of astronomical images.” In: Proceedings of the 32nd International Conference on Machine Learning (ICML). 2015.

Please see the project wiki for documentation.

License

Celeste.jl is free software, licensed under the MIT "Expat" License.