EPJ Web Conf.
Volume 245, 202024th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|Number of page(s)||7|
|Section||2 - Offline Computing|
|Published online||16 November 2020|
Fast simulation of electromagnetic particle showers in high granularity calorimeters
CERN, 1 Esplanade des Particules, Geneva, Switzerland
Published online: 16 November 2020
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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