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EOS -- A HEP program for Flavor Observables

Project description

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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++17 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:

with further code contributions by:

  • Marzia Bordone,
  • Thomas Blake,
  • Elena Graverini,
  • Stephan Jahn,
  • Ahmet Kokulu,
  • Stephan Kürten,
  • Philip Lüghausen,
  • Bastian Müller,
  • 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

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1.0.2

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