Pre-release

This release is a pre-release and may not be stable for production use.

Coverage

EOS logo

EOS - A software for Flavor Physics Phenomenology

EOS is a software package that addresses several use cases in the field of high-energy flavor physics:

theory predictions of and uncertainty estimation for flavor observables within the Standard Model or within the Weak Effective Theory;
Bayesian parameter inference from both experimental and theoretical constraints; and
Monte Carlo simulation of pseudo events for flavor processes.

An up-to-date list of publications that use EOS can be found here.

EOS is written in C++20 and designed to be used through its Python 3 interface, ideally within a Jupyter notebook environment. It depends on as a small set of external software:

the GNU Scientific Library (libgsl),
a subset of the BOOST C++ libraries,
the Python 3 interpreter.

For details on these dependencies we refer to the online documentation.

Installation

EOS supports several methods of installation. For Linux users, the recommended method is installation via PyPI:

pip3 install eoshep

Development versions tracking the master branch are also available via PyPi:

pip3 install --pre eoshep

For instructions on how to build and install EOS on your computer please have a look at the online documentation.

Attribution

When you use EOS for a project that results in a publication, please cite the appropriate / most recent Zenodo release as well as the publication

@article{EOSAuthors:2021xpv,
    author = "van Dyk, Danny and others", collaboration = "EOS Authors",
    title = "{EOS: a software for flavor physics phenomenology}",
    eprint = "2111.15428", archivePrefix = "arXiv", primaryClass = "hep-ph",
    reportNumber = "EOS-2021-04, TUM-HEP 1371/21, P3H-21-094, SI-HEP-2021-32",
    doi = "10.1140/epjc/s10052-022-10177-4",
    journal = "Eur. Phys. J. C", volume = "82", number = "6", pages = "569", year = "2022"
}

Contact

If you want to report an error or file a request, please file an issue here. For additional information, please contact any of the main authors, e.g. via our Discord server.

To approach individual developers, please look here.

Acknowledgments

We would like to extend our thanks to the following people whose input and support were most helpful in either the development or the maintenance of EOS:

Gudrun Hiller
Gino Isidori
David Leverton
Thomas Mannel
Ciaran McCreesh
Hideki Miyake
Konstantinos Petridis
Nicola Serra
Alexander Shires

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