FlavorPy is a Python library for calculations around discrete flavor symmetries in particle physics. Currently, it is split into two parts:
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The constructterms part allows you to calculate group theoretical tensor products and therefore find the invariant terms in the action.
-
The modelfitting part is concerned with fitting a model to experimental data. More specifically flavor observables, i.e. masses and mixing, for given mass matrices with an associated parameter space can be compared and fitted to experimental data. The minimization heavily relies on lmfit.
You can install FlavorPy from PyPI with pip by running
pip install flavorpy
Alternatively, you can:
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Download the files from the github repository.
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Open python and load the files with:
import os
dir_to_git_folder = "home/.../FlavorPy/current_version" # Adjust this to your case !!
os.chdir(os.path.expanduser(dir_to_git_folder))
import constructterms as ct
import modelfitting as mf
- Start using the FlavorPy packages imported as
ct
andmf
!
A documentation is hosted on https://flavorpy.github.io/FlavorPy/. This site also contains examples of how to use the code.
The goal of current development is to bring the two parts together, integrate GAP, have quark models, and extend the modelfitting with a MCMC method to study the vicinity of minima. If you want to contribute, please feel free to contact Alexander Baur
This package uses experimental data obtained by NuFit published in JHEP 09 (2020) 178, arXiv:2007.14792, and their website www.nu-fit.org.
If FlavorPy contributes to a project that leads to a publication, please acknowledge this fact by citing
A. Baur, "FlavorPy", Zenodo, 2024, doi: 10.5281/zenodo.11060597.
Here is an example of a BibTex entry:
@software{FlavorPy,
author = {Baur, Alexander},
title = "{FlavorPy}",
year = {2024},
publisher = {Zenodo},
version = {v0.1.0},
doi = {10.5281/zenodo.11060597},
url = "\url{https://doi.org/10.5281/zenodo.11060597}"
}
When using the NuFit experimental data, please also cite