JeansGNN is a neural simulation-based inference framework for Jeans modeling based on Nguyen et al. (2023) [1]. You can also find our paper on arXiv at https://arxiv.org/abs/2208.12825.
JeansGNN can also perform the unbinned Jeans analysis as described in Chang & Necib (2021) [2].
The framework is built on top of the PyTorch Geometric and PyTorch Lightning library.
| Authors: | Tri Nguyen, Siddharth Mishra-Sharma, Reuel Williams, Laura Chang, Lina Necib, |
|---|---|
| Maintainer: | Tri Nguyen (tnguy@mit.edu) |
| Version: | 0.0.0 (2023-04-14) |
To install JeansGNN, simply clone the repo and install with pip:
git clone https://github.com/trivnguyen/JeansGNN.git
pip install .This should install all the dependencies as well. If you want to install the dependencies separately, please see the section below.
The following dependencies are required to run this project:
- Python 3.6 or later
- NumPy 1.22.3 or later
- SciPy 1.9.1 or later
- Astropy 5.2.2 or later
- PyTorch Geometric 2.1.0 or later
- PyTorch Lightning 1.7.6 or later
- PyYAML 5.4.1 or later
- Tensorboard 2.7.0 or later
- Bilby 2.1.0 or later
To install the dependencies separately, you can use pip:
pip install -r requirements.txtIt is recommended to use a virtual environment to manage the dependencies and avoid version conflicts. You can create a virtual environment and activate it using the following commands:
python -m venv env
source env/bin/activate (Linux/MacOS)
env\Scripts\activate.bat (Windows)Once the virtual environment is activated, you can install the dependencies using pip as usual.
An example of the graph-based simulation-based inference method in Nguyen et al. (2023) [1] can be found at tutorials/example_training.ipynb.
An example of the binned Jeans analysis in Chang & Necib (2021) [2] can be found at tutorials/example_binned_jeans.ipynb.
The rest of the tutorials are under construction. More to come!
Under construction.
We welcome contributions to JeansGNN! To contribute, please contact Tri Nguyen (tnguy@mit.edu).
JeansGNN is licensed under the MIT license. See LICENSE.md for more information.
| [1] | (1, 2) Tri Nguyen, Siddharth Mishra-Sharma, Reuel Williams, Lina Necib, "Uncovering dark matter density profiles in dwarf galaxies with graph neural networks", Physical Review D (PRD), vol. 107, no. 4, article no. 043015, Feb. 2023, https://doi.org/10.1103/PhysRevD.107.043015 |
| [2] | (1, 2) Laura J Chang, Lina Necib, Dark matter density profiles in dwarf galaxies: linking Jeans modelling systematics and observation, Monthly Notices of the Royal Astronomical Society, Volume 507, Issue 4, November 2021, Pages 4715 4733, https://doi.org/10.1093/mnras/stab2440 |