Getting started¶
Simulator dependencies¶
Make sure the following tools are installed and running:
MadGraph (we’ve tested our setup with MG5_aMC v2.8.0+). See https://launchpad.net/mg5amcnlo for installation instructions. Note that MadGraph requires a Fortran compiler as well as Python 3.6+.
For the analysis of systematic uncertainties, LHAPDF6 has to be installed with Python support (see also the documentation of MadGraph’s systematics tool).
For the detector simulation part, there are different options. For simple parton-level analyses, we provide a bare-bones option to calculate truth-level observables which do not require any additional packages.
We have also implemented a fast detector simulation based on Delphes with a flexible framework to calculate observables. Using this adds additional requirements:
Pythia8 and the MG-Pythia interface, installed from within the MadGraph command line interface: execute
<MadGraph5_directory>/bin/mg5_aMC, and then inside the MadGraph interface, runinstall pythia8andinstall mg5amc_py8_interface.Delphes. Again, you can (but this time you don’t have to) install it from the MadGraph command line interface with
install Delphes.
Finally, Delphes can be replaced with another detector simulation, for instance a full detector simulation based
with Geant4. In this case, the user has to implement code that runs the detector simulation, calculates the observables,
and stores the observables and weights in the HDF5 file. The DelphesProcessor and LHEProcessor classes might provide
some guidance for this.
Install MadMiner¶
To install the MadMiner package with all its Python dependencies, run pip install madminer.
To get the latest development version as well as the tutorials, clone the
GitHub repository and run pip install -e . from the repository main
folder.
Docker image¶
At https://hub.docker.com/u/madminertool/ we provide Docker images for the latest version of MadMiner and the physics simulator. Please email iem244@nyu.edu for any questions about the Docker images.