Fast simulation of electromagnetic particle showers in high granularity calorimeters

CERN, 1 Esplanade des Particules, Geneva, Switzerland

^* e-mail: ricardo.Rocha@cern.ch
^** e-mail: Federico.Carminati@cern.ch
^*** e-mail: gul.rukh.khattak@cern.ch
^**** e-mail: sofia.vallecorsa@cern.ch

Published online: 16 November 2020

Abstract

The future need of simulated events by the LHC experiments and their High Luminosity upgrades, is expected to increase by one or two orders of magnitude. As a consequence, research on new fast simulation solutions, including deep Generative Models, is very active and initial results look promising.

We have previously reported on a prototype that we have developed, based on 3 dimensional convolutional Generative Adversarial Network, to simulate particle showers in high-granularity calorimeters. In this contribution we present improved results on a more realistic simulation. Detailed validation studies show very good agreement with Monte Carlo simulation. In particular, we show how increasing the network representational power, introducing physics-based constraints and using a transfer-learning approach for training improve the level of agreement over a large energy range.