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JAX-powered machine learning and modeling framework for GRaTeR disks.

Project description

GRaTeR-JAX

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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:

  1. Fork the repository.
  2. Create a feature branch:
    git checkout -b feature-branch
    
  3. Commit your changes and push to your fork.
  4. 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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