JAX-powered machine learning and modeling framework for GRaTeR disks.
Project description
GRaTeR-JAX
GRaTeR-JAX is a machine learning JAX-based implementation of the Generalized Radial Transporter (GRaTeR) framework (Augereau+ 1999), designed for modeling scattered-light images of debris disks. This repository provides tools for forward modeling, optimization, and parameter estimation of scattered-light disk images using JAX's accelerated computations.
Features
- JAX-Based Optimization: Leverages JAX for fast, GPU/TPU-accelerated disk modeling.
- Scattered Light Disk Modeling: Implements physical models of exoplanetary debris disks.
- Differentiable Framework: Enables gradient-based optimization and probabilistic inference.
- Integration with Webbpsf: Supports JWST PSF convolutions for forward modeling.
Installation
To install GRaTeR-JAX and its dependencies, create a new Conda environment with Python and run:
pip install grater-jax
Make sure you have JAX installed with the correct backend for your hardware:
pip install --upgrade "jax[cpu]" # or "jax[cuda]" for GPU
Highly recommended to install this on a fresh environment, just to be safe.
Usage
Refer to the documentation at grater-jax.readthedocs.io.
Check out GRaTeR Image Generator to visualize how each of the parameters affect the disk model!
Repository Structure
GRaTeR-JAX/
│── grater-jax/ # Package root for grater-jax
│── disk_model/ # Code for disk modeling
│── optimization/ # Tools for statistical optimization and analysis
|── docs/ # Documenation and tutorial notebooks
│── pyproject.toml # Installation file
│── README.md
Contributing
We welcome contributions! To contribute:
- Fork the repository.
- Create a feature branch:
git checkout -b feature-branch
- Commit your changes and push to your fork.
- Open a pull request.
Acknowledgments
Developed by the UCSB Exoplanet Polarimetry Lab. This work is inspired by previous implementations of GRaTeR and advances in JAX-based differentiable modeling.
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