A Python package providing a global likelihood function in the space of dimension-6 Wilson coefficients of the Standard Model Effective Field Theory (SMEFT)
Project description
smelli – a global likelihood for precision constraints
smelli
is a Python package providing a global likelihood function in the
space of dimension-six Wilson coefficients in the Standard Model Effective
Field Theory (SMEFT). The likelihood includes contributions from
quark and lepton flavour physics, electroweak precision tests, and other
precision observables.
The package is based on flavio for the calculation of observables and statistical treatment and wilson for the running, translation, and matching of Wilson coefficients.
Installation
The package requires Python version 3.6 or above. It can be installed with
python3 -m pip install smelli --user
Documentation
A brief user manual can be found in the paper cited below.
Citation
If you use smelli
in a scientific publication, please cite
J. Aebischer, J. Kumar, P. Stangl, and D. M. Straub
"A Global Likelihood for Precision Constraints and Flavour Anomalies"
Please also cite the publications on flavio and wilson, which are the pillars smelli
is built on.
Bugs and feature requests
Please submit bugs and feature requests using Github's issue system.
Contributing
The aim of the package is to provide a likelihood in the space of dimension-6 SMEFT Wilson coefficients using all relevant available experimental measurements. If you want to contribute additional observables, the easiest way is to implement the observable in flavio. Observables implemented there can be added to the likelihood simply by adding a corresponding entry in one of the observable YAML files.
Alternatively, also observables computed in any other standalone Python package can be incorporated in principle as long as it adheres to the WCxf standard. If you want to follow this route, please open an issue to start the discussion on how to integrate it.
Contributors
Maintainer:
- Peter Stangl (@peterstangl)
Contributors (in alphabetical order):
- Jason Aebischer
- Matěj Hudec
- Matthew Kirk
- Jacky Kumar
- Niladri Sahoo
- Aleks Smolkovič
- Peter Stangl
- David M. Straub
License
smelli is released under the MIT license.
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 Distribution
File details
Details for the file smelli-2.4.2.tar.gz
.
File metadata
- Download URL: smelli-2.4.2.tar.gz
- Upload date:
- Size: 1.4 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.0 CPython/3.12.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | bb59bd89c5db97c28da041273221ff58a11b8ee04bda7894b83f6fd6e36a8882 |
|
MD5 | a132a2ffbc3869b0bfb3faa039a93547 |
|
BLAKE2b-256 | dae21f0dbd78c3f10edbeff94b2e55c5d1cbbbd84a431b9053a6a967018b34f6 |