Celeste.jl

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Scalable inference for a generative model of astronomical images

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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: Annals of Applied Statistics, 13 (3), 2019.](https://projecteuclid.org/euclid.aoas/1571277777)

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.](https://arxiv.org/pdf/1801.10277.pdf)

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.](
> http://proceedings.mlr.press/v37/regier15.pdf)

Please see [the project wiki](https://github.com/jeff-regier/Celeste.jl/wiki/) for documentation.

#### License

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

Celeste.jl

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