MCNNTUNES: Tuning Shower Monte Carlo generators with machine learning

Published: 8 March 2021| Version 1 | DOI: 10.17632/dmkydsxgd3.1
Contributors:
Marco Lazzarin,
Simone Alioli,
Stefano Carrazza

Description

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 parametrization-based techniques, with the most successful one being a polynomial parametrization. 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.

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Computational Physics, Machine Learning

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