Welcome to Cheetah’s documentation!¶

Cheetah is a particle tracking accelerator we built specifically to speed up the training of reinforcement learning models.

GitHub repository: https://github.com/desy-ml/cheetah

Paper: https://doi.org/10.1103/PhysRevAccelBeams.27.054601

Discord server: https://discord.gg/hrwYPC3a

Installation¶

Simply install Cheetah from PyPI by running the following command.

pip install cheetah-accelerator

Examples¶

We provide some examples to demonstrate some features of Cheetah and show how to use them. They provide a good entry point to using Cheetah, but they do not represent its full functionality. To move beyond the examples, please refer to the in-depth documentation. If you feel like other examples should be added, feel free to open an issue on GitHub.

Examples

Getting Started¶

These pages explain how to get started with Cheetah.

Getting Started

Coordinate System in Cheetah

Documentation¶

For more advanced usage, please refer to the in-depth documentation.

Documentation

Cite Cheetah¶

If you use Cheetah, please cite the following two papers:

@article{kaiser2024cheetah,
    title        = {Bridging the gap between machine learning and particle accelerator physics with high-speed, differentiable simulations},
    author       = {Kaiser, Jan and Xu, Chenran and Eichler, Annika and Santamaria Garcia, Andrea},
    year         = 2024,
    month        = {May},
    journal      = {Phys. Rev. Accel. Beams},
    publisher    = {American Physical Society},
    volume       = 27,
    pages        = {054601},
    doi          = {10.1103/PhysRevAccelBeams.27.054601},
    url          = {https://link.aps.org/doi/10.1103/PhysRevAccelBeams.27.054601},
    issue        = 5,
    numpages     = 17
}
@inproceedings{stein2022accelerating,
    title        = {Accelerating Linear Beam Dynamics Simulations for Machine Learning Applications},
    author       = {Stein, Oliver and Kaiser, Jan and Eichler, Annika},
    year         = 2022,
    booktitle    = {Proceedings of the 13th International Particle Accelerator Conference}
}

For Developers¶

Activate your virtual environment. (Optional)

Install the cheetah package as editable

pip install -e .

We suggest installing pre-commit hooks to automatically conform with the code formatting in commits:

pip install pre-commit
pre-commit install

Acknowledgements¶

Author Contributions¶

The following people have contributed to the development of Cheetah:

Jan Kaiser (@jank324)
Chenran Xu (@cr-xu)
Annika Eichler (@AnEichler)
Andrea Santamaria Garcia (@ansantam)
Christian Hespe (@Hespe)
Oliver Stein (@OliStein523)
Grégoire Charleux (@greglenerd)
Remi Lehe (@RemiLehe)
Axel Huebl (@ax3l)
Juan Pablo Gonzalez-Aguilera (@jp-ga)
Ryan Roussel (@roussel-ryan)
Auralee Edelen (@lee-edelen)

Institutions¶

The development of Cheetah is a joint effort by members of the following institutions:

Funding¶

The work to develop Cheetah has in part been funded by the IVF project InternLabs-0011 (HIR3X) and the Initiative and Networking Fund by the Helmholtz Association (Autonomous Accelerator, ZT-I-PF-5-6). Further, we gratefully acknowledge funding by the EuXFEL R&D project “RP-513: Learning Based Methods”. This work was also supported by the U.S. National Science Foundation under Award PHY-1549132, the Center for Bright Beams. In addition, we acknowledge support from DESY (Hamburg, Germany) and KIT (Karlsruhe, Germany), members of the Helmholtz Association HGF.