The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O., Mattelaer, H.-S. Shao, T. Stelzer, P. Torrielli, M. Zaro

TL;DR
This paper introduces MadGraph5_aMC@NLO, a versatile computational tool that automates the calculation of tree-level and next-to-leading order cross sections, their matching to parton showers, and merging of samples, streamlining high-energy physics simulations.
Contribution
The paper presents a unified, flexible, and highly parallelized software framework for automated NLO and tree-level cross section computations and their integration with parton shower simulations.
Findings
Demonstrates the program's capabilities with LHC and 1-TeV e+e- collider applications.
Shows the software's flexibility to incorporate various renormalisable Lagrangians.
Highlights the high level of automation and minimal human intervention required.
Abstract
We discuss the theoretical bases that underpin the automation of the computations of tree-level and next-to-leading order cross sections, of their matching to parton shower simulations, and of the merging of matched samples that differ by light-parton multiplicities. We present a computer program, MadGraph5_aMC@NLO, capable of handling all these computations -- parton-level fixed order, shower-matched, merged -- in a unified framework whose defining features are flexibility, high level of parallelisation, and human intervention limited to input physics quantities. We demonstrate the potential of the program by presenting selected phenomenological applications relevant to the LHC and to a 1-TeV collider. While next-to-leading order results are restricted to QCD corrections to SM processes in the first public version, we show that from the user viewpoint no changes have to be…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Quantum Chromodynamics and Particle Interactions · Computational Physics and Python Applications
