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|
Generative Adversarial Networks for LHCb Fast Simulation
National Research University Higher School of Economics, Moscow, Russia
* e-mail: email@example.com
Published online: 16 November 2020
LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of the physics program and the increasing precision of the measurements in LHCb lead to the need of ever larger simulated samples. This need will increase further when the upgraded LHCb detector will start collecting data in the LHC Run 3. Given the computing resources pledged for the production of Monte Carlo simulated events in the next years, the use of fast simulation techniques will be mandatory to cope with the expected dataset size. Generative models, which are nowadays widely used for computer vision and image processing, are being investigated in LHCb to accelerate generation of showers in the calorimeter and high-level responses of Cherenkov detector. We demonstrate that this approach provides high-fidelity results and discuss possible implications of these results. We also present an implementation of this algorithm into LHCb simulation software and validation tests.
© 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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