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Fast Simulation Using Generative Adversarial Network in LHCB

<!--HTML-->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...

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Autor principal: Maevskiy, Artem
Lenguaje:eng
Publicado: 2019
Materias:
Acceso en línea:http://cds.cern.ch/record/2672121
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author Maevskiy, Artem
author_facet Maevskiy, Artem
author_sort Maevskiy, Artem
collection CERN
description <!--HTML-->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. In LHCb generative models, which are nowadays widely used for computer vision and image processing are being investigated in order to accelerate the generation of showers in the calorimeter and high-level responses of Cherenkov detector. We demonstrate that this approach provides high-fidelity results along with a significant speed increase and discuss possible implication of these results. We also present an implementation of this algorithm into LHCb simulation software and validation tests.
id cern-2672121
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2019
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spelling cern-26721212022-11-02T22:33:37Zhttp://cds.cern.ch/record/2672121engMaevskiy, ArtemFast Simulation Using Generative Adversarial Network in LHCB3rd IML Machine Learning WorkshopLPCC Workshops<!--HTML-->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. In LHCb generative models, which are nowadays widely used for computer vision and image processing are being investigated in order to accelerate the generation of showers in the calorimeter and high-level responses of Cherenkov detector. We demonstrate that this approach provides high-fidelity results along with a significant speed increase and discuss possible implication of these results. We also present an implementation of this algorithm into LHCb simulation software and validation tests.oai:cds.cern.ch:26721212019
spellingShingle LPCC Workshops
Maevskiy, Artem
Fast Simulation Using Generative Adversarial Network in LHCB
title Fast Simulation Using Generative Adversarial Network in LHCB
title_full Fast Simulation Using Generative Adversarial Network in LHCB
title_fullStr Fast Simulation Using Generative Adversarial Network in LHCB
title_full_unstemmed Fast Simulation Using Generative Adversarial Network in LHCB
title_short Fast Simulation Using Generative Adversarial Network in LHCB
title_sort fast simulation using generative adversarial network in lhcb
topic LPCC Workshops
url http://cds.cern.ch/record/2672121
work_keys_str_mv AT maevskiyartem fastsimulationusinggenerativeadversarialnetworkinlhcb
AT maevskiyartem 3rdimlmachinelearningworkshop