EOS -- A HEP program for Flavor Observables
Project description
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.
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 christoph.bobeth@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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
Hashes for eoshep-1.0.10-cp311-cp311-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 671441e665a3ad82083bee11c36df2fdd34bd203417728b82c6a1a921031f34e |
|
MD5 | de721e6c706ab7661216b6911a694649 |
|
BLAKE2b-256 | 77ea7eaf27bad69865ff713441943cec99e86601be6fb2cd0da57667d3049d9d |
Hashes for eoshep-1.0.10-cp310-cp310-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | be72d6b955e8b3f10c3fd9649966662f5a5fbf9c091b0415eefd84d969d857e5 |
|
MD5 | b4d995fe1b687a99cb31c2d626dcd46b |
|
BLAKE2b-256 | 22c43027073237f6dbc3bf7f1b9216ece821c720e2fbac3ad775e404ef1df0a4 |
Hashes for eoshep-1.0.10-cp39-cp39-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7b824a032b07365091880d28aaa30b08be9ce99b3da4548c88a1409708503060 |
|
MD5 | 0318e0ef5b60661f7bce49ceb99df34f |
|
BLAKE2b-256 | 53cbb47e6c84e0b62d36af91b85c285098f371b7fbfc2baa5cfae1f788cfde45 |
Hashes for eoshep-1.0.10-cp38-cp38-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 22cefa22ce15f780a995030b46a2c5d07913f20f541c15170ebadaa1655062f7 |
|
MD5 | de12d0c7b77d775f2145e708b23b632b |
|
BLAKE2b-256 | ffd84eb4c39209401729808e49345b95d511890abdd94c1681b3c9bfbbbccbaf |