This repository contains the code for calculating cross sections, relic density and source spectra for the singlet scalar model of dark matter.
The package relies on pre-computed data obtained according to several codes: MadDM [1–4], DRAKE [5] and micrOMEGAs [6–11].
The package supports Python 3.7 and beyond. Moreover, it depends on the following packages:
To install the package along its dependencies, download a release (for stable version) or clone this repository (for development version) and run the following command in the package directory: and run the following command in the package directory:
python -m pip install .The examples rely on the matplotlib package in order to show the plots.
You can install this dependence using the following command in the package root
directory:
python -m pip install '.[examples]'If you want to rebuild the documentation for yourself, you need to use sphinx
with the configuration provided in the docs/ folder.
You can install the required dependencies using the following command in the package root directory:
python -m pip install '.[docs]'Build using sphinx.
You can run the following commands inside the docs/ directory.
If you want to update the source documentation:
make rstIf you want to build the documentation in a certain format, you can specify a
<builder> among the available builders:
make <builder>Notice that it can take a while to build the whole documentation, given it will run all the examples.
If you wish to change something in the data files or in the code, we advise you to install the package in development or editable mode, using the following command in the package root directory:
python -m pip install --editable .In this way you are free to modify anything inside the src/ folder and the changes
will be immediately reflected in the installed package once you re-import it.
If you have installed the package correctly, you can use it in the following way:
from singletscalar_dm import *Don't forget to cite us!.
The directory examples/ contains ready-to-run examples on how to use the package
to create different plots.
Refer to this section to install the required dependencies.
Examples can be browsed as well in the examples gallery on the GitHub pages website, where you can also download both a python script and a jupyter notebook for each one of them.
If you are using this tool or the results obtained with it in a published work, please cite arXiv:2305.11937.
[1] M. Backovic, K. Kong, and M. McCaskey, Physics of the Dark Universe 5-6, 18 (2014), 1308.4955
[2] M. Backovic, A. Martini, O. Mattelaer, K. Kong, and G. Mohlabeng, Phys. Dark Univ. 9-10, 37 (2015), 1505.04190
[3] F. Ambrogi, C. Arina, M. Backovic, J. Heisig, F. Maltoni, L. Mantani, O. Mattelaer, and G. Mohlabeng, Phys. Dark Univ. 24, 100249 (2019), 1804.00044
[4] C. Arina, J. Heisig, F. Maltoni, D. Massaro, and O. Mattelaer, Eur. Phys. J. C 83, 241 (2023), 2107.04598
[5] T. Binder, T. Bringmann, M. Gustafsson, and A. Hryczuk, Eur. Phys. J. C 81, 577 (2021), 2103.01944
[6] G. Belanger, F. Boudjema, A. Pukhov, and A. Semenov, Comput. Phys. Commun. 176, 367 (2007), hep-ph/0607059
[7] G. Belanger, F. Boudjema, A. Pukhov, and A. Semenov, Comput. Phys. Commun. 185, 960 (2014), 1305.0237
[8] D. Barducci, G. Belanger, J. Bernon, F. Boudjema, J. Da Silva, S. Kraml, U. Laa, and A. Pukhov, Comput. Phys. Commun. 222, 327 (2018), 1606.03834
[9] G. Belanger, F. Boudjema, A. Goudelis, A. Pukhov, and B. Zaldivar, Comput. Phys. Commun. 231, 173 (2018), 1801.03509
[10] G. Belanger, A. Mjallal and A. Pukhov, Eur. Phys. J. C 81, 239 (2021), 2003.08621
[11] G. Alguero, G. Belanger, S. Kraml and A. Pukhov, SciPost Phys. 13, 124 (2022), 2207.10536