MCNNTUNES: tuning Shower Monte Carlo generators with machine learning

Abstract

The parameters tuning of event generators is a research topic characterized by complex choices: the generator response to parameter variations is difficult to obtain on a theoretical basis, and numerical methods are hardly tractable due to the long computational times required by generators. Event generator tuning has been tackled by parametrisation-based techniques, with the most successful one being a polynomial parametrisation. In this work, an implementation of tuning procedures based on artificial neural networks is proposed. The implementation was tested with closure testing and experimental measurements from the ATLAS experiment at the Large Hadron Collider.

arXiv comment9 pages, 3 figures, 9 tables, code available at https://github.com/N3PDF/mcnntunes

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Cited by in corpus (6)

• Machine Learning and LHC Event Generation
• Event Generators for High-Energy Physics Experiments
• Novel approaches in Hadron Spectroscopy
• E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
• A Bayesian tune of the Herwig Monte Carlo event generator
• Monte Carlo Event Generators for Future Lepton Colliders