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 Calorimeter Simulation in ATLAS
University of Washington, Seattle, Washington, United States of America
2 Lawrence Berkeley National Laboratory and University of California Berkeley, Berkeley, California, United States of America
* e-mail: email@example.com
Copyright 2020 CERN for the benefit of the ATLAS Collaboration. Reproduction of this article or parts of it is allowed as specified in the CC-BY-4.0 license
Published online: 16 November 2020
The ATLAS physics program at the LHC relies on very large samples of simulated events. Most of these samples are produced with Geant4, which provides a highly detailed and accurate simulation of the ATLAS detector. However, this accuracy comes with a high price in CPU, and the sensitivity of many physics analyses is already limited by the available Monte Carlo statistics and will be even more so in the future as datasets grow. To solve this problem, sophisticated fast simulation tools are developed, and they will become the default tools in ATLAS production in Run 3 and beyond. The slowest component is the simulation of the calorimeter showers. Those are replaced by a new parametrised description of the longitudinal and lateral energy deposits, including machine learning approaches, achieving a fast but accurate description. In this talk we will describe the new tool for fast calorimeter simulation that has been developed by ATLAS, review its technical and physics performance, and demonstrate its potential to transform physics analyses.
© 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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