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

1. calculation and uncertainty estimation of flavor observables within various models,
2. Bayesian inference of parameters from experimental and/or theoretical constraints, and
3. sampling process-specific probability density functions.

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

EOS is written in C++14, with an optional interface to Python, and depends on as a small set of external software libraries:

• the GNU Scientific Library (libgsl),
• a subset of the BOOST C++ libraries,
• the Hierarchical Data Format v5 library (libdf5),
• the minimizer Minuit2 (as of ROOT version 5.14.00 or later),
• the Population Monte Carlo (PMC) library pmclib (optional),
• the Python interpreter (optional).

For details on these dependencies we refer to the user manual.

Presently EOS supports installation from source only. For Ubuntu users, two of the external software dependencies that are not available from the main repositories are provided in the official EOS repository.

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

The main authors are:

• Danny van Dyk [email protected],
• Frederik Beaujean [email protected],
• Christoph Bobeth [email protected],

with code contributions by:

• Marzia Bordone,
• Thomas Blake,
• Elena Graverini,
• Nico Gubernari,
• Stephan Jahn,
• Ahmet Kokulu,
• Bastian Müller,
• Stefanie Reichert,
• Rafael Silva Coutinho,
• Ismo Tojiala,
• 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
• David Leverton
• Ciaran McCreesh
• Hideki Miyake
• Konstantinos Petridis
• Alexander Shires

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