Welcome to the MadGraph5_aMC@NLO Wiki

This wiki is dedicated to the MadGraph5_aMC@NLO project.

MadGraph5_aMC@NLO is a framework that aims at providing all the elements necessary for SM and BSM phenomenology, such as the computations of cross sections, the generation of hard events and their matching with event generators, and the use of a variety of tools relevant to event manipulation and analysis. Processes can be simulated to LO accuracy for any user-defined Lagrangian, and the NLO accuracy in the case of QCD corrections to SM processes. Matrix elements at the tree- and one-loop-level can also be obtained.

MadGraph5_aMC@NLO is the new version of both MadGraph5 and aMC@NLO that unifies the LO and NLO lines of development of automated tools within the MadGraph family. It therefore supersedes all the MadGraph5 1.5.x versions and all the beta versions of aMC@NLO.

A standard reference for the use of the code is being written. In the meanwhile please contact us: there are many relevant papers, and the appropriate citations must be tailored to physics applications.

Download:

The latest stable release can downloaded as a tar.gz package (see the right of this page), or through the Bazaar versioning system, using bzr branch lp:madgraph5

Installation:

MadGraph5_aMC@NLO needs Python version 2.6 or 2.7 ; gfortran/gcc 4.6 or higher is required for NLO calculations/simulations.

Getting started:

Run bin/mg5_aMC and type "help" to learn how to run MadGraph5_aMC@NLO using the command interface, or run the interactive quick-start tutorial by typing "tutorial". Some third-party packages can be installed using the MG5_aMC shell command "install". LO generation can also be done directly online at: ​http://madgraph.phys.ucl.ac.be or ​http://madgraph.hep.uiuc.edu

MadGraph5_aMC@NLO is the new version of the MadGraph5, written in the Python programming language, that includes the a For downloads, an overview on the project, or for any question related to it, please meet us on ​Launchpad.

If you are a developer, please start here.

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