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++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:
- Danny van Dyk danny.van.dyk@gmail.com,
- Frederik Beaujean,
- Christoph Bobeth christoph.bobeth@gmail.com,
- Nico Gubernari nicogubernari@gmail.com,
- Meril Reboud reboud@gmail.com,
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
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-1.0.1-cp39-cp39-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 636567da4d86b4c5b18e0fea6169476f752e74f05fd14af411ce9461a7618fa6 |
|
MD5 | 2097dc771b6bd346817378f8a3e4f68f |
|
BLAKE2b-256 | 356df06f136c805b3b1eee686bce197341ee1258f482a3bf59b4a8316d44449b |
Hashes for eoshep-1.0.1-cp38-cp38-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 282f146c2690d58cc94f878cbe92bce67d0a367d086b3a6dd59cf317896a10e5 |
|
MD5 | 15604571264bfe3332384f5aef0d9c51 |
|
BLAKE2b-256 | b57004341ff675ce7dce18e89eaa228dd4951d70b0d89b195aaaf27f359b078e |
Hashes for eoshep-1.0.1-cp37-cp37m-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e98bdeba21e6fcd2d8b8f3605df1706b7a9e9273262cd877ae14ef61e2395620 |
|
MD5 | 2ee643fc633b20abe22514087a371204 |
|
BLAKE2b-256 | 906c35f0ecfbc4232434c6bf65ba9b6d92ef6143cb63fdd031d34f8a88b14f9e |
Hashes for eoshep-1.0.1-cp36-cp36m-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 27dc6560eb17527378788b27c80ebf0eee326aae324ffef97e644a1d75c4068b |
|
MD5 | 3e777950b5c4c3847977945df2d205b6 |
|
BLAKE2b-256 | a25f2b9e9dcab4d65b8d5be1cc63dffe0e25dd4c78ec656f4f0145594447a79a |