Issue |
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|
|
---|---|---|
Article Number | 02034 | |
Number of page(s) | 8 | |
Section | T2 - Offline computing | |
DOI | https://doi.org/10.1051/epjconf/201921402034 | |
Published online | 17 September 2019 |
https://doi.org/10.1051/epjconf/201921402034
Generative Models for Fast Calorimeter Simulation: the LHCb case>
1
NRU Higher School of Economics,
Moscow,
Russia
2
Yandex School of Data Analysis,
Moscow,
Russia
3
Skolkovo Institute of Science and Technology,
Moscow,
Russia
* e-mail: fedor.ratnikov@cern.ch
Published online: 17 September 2019
Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider (HL-LHC) needs, so the experiments are in urgent need of new fast simulation techniques. We introduce a new Deep Learning framework based on Generative Adversarial Networks which can be faster than traditional simulation methods by 5 orders of magnitude with reasonable simulation accuracy. This approach will allow physicists to produce a sufficient amount of simulated data needed by the next HL-LHC experiments using limited computing resources.
© The Authors, published by EDP Sciences, 2019
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