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Manuscript Title: MadAnalysis 5, a user-friendly framework for collider phenomenology
Authors: Eric Conte, Benjamin Fuks, Guillaume Serret
Program title: MadAnalysis 5
Catalogue identifier: AENO_v1_0
Distribution format: tar.gz
Journal reference: Comput. Phys. Commun. 184(2013)222
Programming language: PYTHON, C++.
Computer: All platforms on which Python version 2.7, Root version 5.27 and the g++ compiler are available. Compatibility with newer versions of these programs is also ensured. However, the Python version must be below version 3.0.
Operating system: Unix, Linux and Mac OS operating systems on which the above-mentioned versions of Python and Root, as well as g++, are available.
Keywords: Particle physics phenomenology, Monte Carlo event generators, Hadron colliders.
PACS: 13.85.-t.
Classification: 11.1.

External routines: ROOT (http://root.cern.ch/drupal/)

Nature of problem:
Implementing sophisticated phenomenological analyses in high-energy physics through a flexible, efficient and straightforward fashion, starting from event files such as those produced by Monte Carlo event generators. The events files can have been matched or not to parton-showering and can have been processed or not by a (fast) simulation of a detector. According to the sophistication level of the event files (parton-level, hadron-level, reconstructed-level), one must note that several input formats are possible.

Solution method:
We implement an interface allowing the production of predefined as well as user-defined histograms for a large class of kinematical distributions after applying a set of event selection cuts specified by the user. This therefore allows us to devise robust and novel search strategies for collider experiments, such as those currently running at the Large Hadron Collider at CERN, in a very efficient way.

Restrictions:
Unsupported event file format.

Unusual features:
The code is fully based on object representations for events, particles, reconstructed objects and cuts, which facilitates the implementation of an analysis.

Running time:
It depends on the purposes of the user and on the number of events to process. It varies from a few seconds to the order of the minute for several millions of events.