flavorpy 0.1.0

Library for calculations around discrete flavor symmetries in particle physics

FlavorPy

What is FlavorPy?

FlavorPy is a Python library for calculations around discrete flavor symmetries in particle physics. Currently, it is split into two parts:

• 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.

How to install FlavorPy?

You can install FlavorPy from PyPI with pip by running

   pip install flavorpy

Alternatively, you can:

1. Download the files from the github repository.

2. 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

3. Start using the FlavorPy packages imported as ct and mf!

Documentation

A documentation is hosted on https://flavorpy.github.io/FlavorPy/. This site also contains examples of how to use the code.

Current development

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

Credit

This package uses experimental data obtained by NuFit published in JHEP 09 (2020) 178, arXiv:2007.14792, and their website www.nu-fit.org.

Citing FlavorPy

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

I. Esteban, M. C. González-García, M. Maltoni, T. Schwetz, and A. Zhou, The fate of hints: updated global analysis of three-flavor neutrino oscillations, JHEP 09 (2020), 178, arXiv:2007.14792 [hep-ph], https://www.nu-fit.org.