MCNNTUNES: tuning Shower Monte Carlo generators with machine learning

Understand

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.