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,
- 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
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-1.0.12.dev340-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3ac8dd127fb7cfbe0080fe3bb9530429401b5a5efefd3700261879bf70498057 |
|
MD5 | 13c46e6a66688aaef1d57e8c14641b2c |
|
BLAKE2b-256 | c82e058f8213f32007e9a436600a3e9974cfc1d556991fa99bdda5d3ff7598f7 |
Hashes for eoshep-1.0.12.dev340-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1c4a237535542d7e31433d997a0aa804870c1e5abdd061e3384fa37cca3b6fdf |
|
MD5 | 068b1cb53d93d18c98ef425153d7ace3 |
|
BLAKE2b-256 | 26e110c3e23dce7fbbc56dbf92fe5f6effa96a066fe1aec4eaac104e64a3e1ee |
Hashes for eoshep-1.0.12.dev340-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3d24016dc6a96dea7ff2138079ea0d1e5437baf4e26c8f3283452d1d747dec8e |
|
MD5 | 903b1a1ea02a8fc4371cbf036c13e869 |
|
BLAKE2b-256 | 1867b46c8321d8c1999673ed5e8dc6895b7a6ada53cea968ae5ae9fdbeee7ba4 |
Hashes for eoshep-1.0.12.dev340-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 90a33f0f012bd4a309e46ec4ee58c2f5e042a72e6b9bd5aa5ff5132a7311eda2 |
|
MD5 | 8ab3e47a1e822f992d462b94e0b1731d |
|
BLAKE2b-256 | cc05225feaa121f9208278becae59d12fc269c44db3fce6fc17a5871a895afd2 |