Welcome to MadDM documentation#

MadDM is a numerical tool designed to compute dark matter relic abundance, dark matter nucleus scattering rates, and dark matter indirect detection predictions in a generic model. Based on the existing MadGraph 5 architecture, MadDM is easily integrable into any MadGraph collider study. The tool features a simple Python interface, maintaining the user-friendliness characteristic of MadGraph 5 without sacrificing functionality.

Install MadDM#

MadDM is a plugin for MG5_aMC@NLO (MadGraph).

Requirements#

  • MadGraph v2.9 (LTS version) is required to run the latest version of MadDM, MadGraph v3.* may be supported but more testing is necessary.

  • Python 3.6+

  • gfortran

  • make

  • cmake

  • python package:

    • six: to support Python 2.7 as well

    • numpy

    • scipy (required for some recent features)

Installation#

Given MadDM depends on MadGraph, we first need to install the latter, for example, for MadGraph v2.9.24, we need to:

  1. Download a tarball for the latest version v2.9.* from the MadGraph5_aMC@NLO download page (e.g. for v2.9.24 - the link may change for newer/older versions):

    wget https://launchpad.net/mg5amcnlo/3.0/3.6.x/+download/MG5_aMC_v2.9.24.tar.gz

  2. Then unpack the tarball and change into the directory (we use as example the version v2.9.24, but you can use any other version, change the version number accordingly):

    tar xzf MG5_aMC_v2.9.24.tar.gz
cd MG5_aMC_v2.9.24

  1. Start MadGraph by running:

    ./bin/mg5_aMC

  2. Type the following in the command line:

    MG5_aMC>install maddm

  3. Quit MadGraph and run MadDM with:

    ./bin/maddm.py

  4. Install some prerequisites: Pythia and the tabulated spectra CosmiXs (and PPPC4DMID):

    MadDM>install pythia8
MadDM>install PPPC4DMID

    The installation of Pythia 8 will take care of automatically installing also LHAPDF, zlib and the interface in between MadGraph and Pythia (mg5amc_py8_interface), while the command install PPPC4DMID will also install CosmiXs.

  5. You are now set, have fun!

Overview#

MadDM can calculate the dark matter relic abundance in models that include a multi-component dark sector, resonance annihilation channels, and co-annihilations.

Direct Detection Module#

The direct detection module of MadDM calculates spin-independent and spin-dependent dark matter-nucleon cross-sections and differential recoil rates. These are provided as functions of recoil energy, angle, and time. The module also offers a simplified simulation of detector effects for various target materials and volumes.

Indirect Detection Module#

The indirect detection module of MadDM computes the velocity-averaged cross-section for dark matter particles annihilating into multiple final state particles. It further provides the energy spectra of photons, neutrinos, and cosmic rays generated by these final states after decaying, showering, and hadronization. The module automatically computes the flux of prompt neutrinos and gamma rays at detection.

Additionally, it offers a user-friendly interface with the numerical DRAGON code to obtain the flux of cosmic rays at Earth. A built-in interface with the nested sampling PyMultiNest algorithm enables efficient sampling of the model parameter space. The code also allows testing the model against the Fermi-LAT dwarf spheroidal galaxy likelihood.

MadDM is also able to compute loop-induced processes relevant to indirect detection phenomenology. Specifically, MadDM can automatically compute the annihilation of dark matter into final states like gamma-gamma, gamma-Z, gamma-H, gluon-gluon, and others. In generic dark matter models, these final states are loop-induced processes, where the tree-level amplitude is zero.

For the gamma-ray line final state, MadDM performs an analysis of the signal against Fermi-LAT gamma-ray line data to assess whether the model parameter space is allowed by current exclusion limits at 95% CL.

References#

  1. M. Backovic **, **K. Kong, and M. McCaskey (2014), MadDM v.1.0: Computation of Dark Matter Relic Abundance Using MadGraph5. arXiv:1308.4955. DOI: 10.1016/j.dark.2014.04.001. Physics of the Dark Universe, 5-6, 18–28.

  2. M. Backović, A. Martini, O. Mattelaer, K. Kong, and G. Mohlabeng (2015), Direct Detection of Dark Matter with MadDM v.2.0. arXiv:1505.04190. DOI: 10.1016/j.dark.2015.09.001. Phys. Dark Univ., 9-10, 37–50

  3. F. Ambrogi, C. Arina, M. Backovic, J. Heisig, F. Maltoni, L. Mantani, O. Mattelaer, and G. Mohlabeng (2019), MadDM v.3.0: a Comprehensive Tool for Dark Matter Studies. arXiv:1804.00044. DOI: 10.1016/j.dark.2018.11.009. Phys. Dark Univ., 24, 100249.

  4. C. Arina, J. Heisig, F. Maltoni, D. Massaro, and O. Mattelaer (2023), Indirect dark-matter detection with MadDM v3.2 – Lines and Loops. arXiv:2107.04598. DOI: 10.1140/epjc/s10052-023-11377-2. Eur. Phys. J. C, 83 (3), 241.

Some third-party packages can be installed using the install command in the shell. Be sure to cite them as indicated during the install phase if you use them.

Citation#

If you are using this tool or the results obtained with it in a published work, please cite one or more of the previous references.