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

Documentation

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

Documentation

Cite Cheetah

If you use Cheetah, please cite the two papers below.

If you use 3D meshes generated by Cheetah, please respect the licencing terms of the [_3D Assets for Particle Accelerators_](https://github.com/desy-ml/3d-assets) repository.

@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)

  • Zihan Zhu (@zihan-zh)

  • Christian Contreras-Campana (@chrisjcc)

  • Sucheth Shenoy (@SuchethShenoy)

  • Amelia Pollard (@amylizzle)

  • Julian Gethmann (@smartsammler)

Institutions

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

DESY KIT LBNL University of Chicago SLAC University of Liverpool Cockcroft Institute Hamburg University of Technology Science and Technology Facilities Council Argonne National Laboratory Hamburg Open Online University

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 is also supported by the U.S. Department of Energy, Office of Science under Contract No. DE-AC02-76SF00515, the Center for Bright Beams, NSF Award No. PHY-1549132, and the U.S. DOE Office of Science-Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. In addition, we acknowledge support from DESY (Hamburg, Germany) and KIT (Karlsruhe, Germany), members of the Helmholtz Association HGF as well as from the Hamburg Open Online University (HOOU) and the Science and Technology Facilities Council (UK).

Indices and tables