https://github.com/pmelchior/scarlet2
Astronomical source modeling in jax
https://github.com/pmelchior/scarlet2
astronomy image-analysis source-separation
Last synced: about 1 month ago
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Astronomical source modeling in jax
- Host: GitHub
- URL: https://github.com/pmelchior/scarlet2
- Owner: pmelchior
- License: mit
- Created: 2023-01-05T15:57:11.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2025-12-13T17:13:37.000Z (3 months ago)
- Last Synced: 2025-12-15T10:52:53.981Z (2 months ago)
- Topics: astronomy, image-analysis, source-separation
- Language: Python
- Homepage: https://scarlet2.readthedocs.io
- Size: 14.7 MB
- Stars: 25
- Watchers: 2
- Forks: 5
- Open Issues: 38
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# _scarlet2_
_scarlet2_ is an open-source python library for modeling astronomical sources from multi-band, multi-epoch, and
multi-instrument data. It provides non-parametric and parametric models, can handle source overlap (aka blending), and
can integrate neural network priors. It's designed to be modular, flexible, and powerful.
_scarlet2_ is implemented in [jax](http://jax.readthedocs.io/), layered on top of
the [equinox](https://docs.kidger.site/equinox/)
library. It can be deployed to GPUs and TPUs and supports optimization and sampling approaches.
## Installation
For performance reasons, you should first install `jax` with the suitable `jaxlib` for your platform. After that
```
pip install scarlet2
```
should do. If you want the latest development version, use
```
pip install git+https://github.com/pmelchior/scarlet2.git
```
This will allow you to evaluate source models and compute likelihoods of observed data, so you can run your own
optimizer/sampler. If you want a fully fledged library out of the box, you need to install `optax`, `numpyro`, and
`h5py` as well.