JimGW 0.4.0

A JAX-based gravitational-wave inference toolkit

Jim 🚬

A JAX-based gravitational-wave inference toolkit

Jim is a JAX-based toolkit for Bayesian parameter estimation of gravitational-wave sources. It pairs differentiable waveform models from ripple with GPU-accelerated JAX-based samplers, enabling massively parallel inference.

Supported samplers:

• flowMC — normalizing-flow-enhanced MCMC with optional parallel tempering.
• BlackJAX NS-AW — nested sampling described in Prathaban et al. 2025 (arXiv:2509.04336).
• BlackJAX NSS — nested slice sampling.
• BlackJAX SMC — sequential Monte Carlo with optional adaptive tempering and persistent sampling.

For a quick introduction, see the Quick Start guide.

[!WARNING] Jim has not yet reached v1.0.0 and the API may change. Use at your own risk. Consider pinning to a specific version if you need API stability.

Installation

The simplest way to install Jim is through pip:

pip install JimGW

This will install the latest stable release and its dependencies. Jim is built on JAX. By default, this installs the CPU version of JAX. If you have an NVIDIA GPU, install the CUDA-enabled version:

pip install JimGW[cuda]

If you want to install the latest version of Jim, you can clone this repo and install it locally:

git clone https://github.com/GW-JAX-Team/jim.git
cd jim
pip install -e .

We recommend using uv to manage your Python environment. After cloning the repository, run uv sync to create a virtual environment with all dependencies installed.

Attribution

If you use Jim in your research, please cite the accompanying paper:

@article{Wong:2023lgb,
    author = "Wong, Kaze W. K. and Isi, Maximiliano and Edwards, Thomas D. P.",
    title = "{Fast Gravitational-wave Parameter Estimation without Compromises}",
    eprint = "2302.05333",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.IM",
    doi = "10.3847/1538-4357/acf5cd",
    journal = "Astrophys. J.",
    volume = "958",
    number = "2",
    pages = "129",
    year = "2023"
}