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:

1. theory predictions of and uncertainty estimation for flavor observables within the Standard Model or within the Weak Effective Theory;
2. Bayesian parameter inference from both experimental and theoretical constraints; and
3. 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.

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.

Authors and Contributors

The main authors are:

• Danny van Dyk danny.van.dyk@gmail.com,
• Frederik Beaujean,
• Christoph Bobeth,
• Carolina Bolognani carolinabolognani@gmail.com,
• Nico Gubernari nicogubernari@gmail.com,
• Meril Reboud merilreboud@gmail.com,

with further code contributions by:

• Marzia Bordone,
• Thomas Blake,
• Lorenz Gaertner,
• Elena Graverini,
• Stephan Jahn,
• Ahmet Kokulu,
• Viktor Kuschke,
• Stephan Kürten,
• Philip Lüghausen,
• Bastian Müller,
• Filip Novak,
• Stefanie Reichert,
• Eduardo Romero,
• Rafael Silva Coutinho,
• Ismo Tojiala,
• K. Keri Vos,
• Christian Wacker.

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